Sami Abu-El-Haija

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Foundational ML & Algorithms <span class="icon icon--caret"></span> </button> <div class="navigation__nested-sub js-sub-nav-parent"> <div class="navigation__sub__mobile-heading"> <button class="glue-header__link js-sub-nav-close-mobile" role="menuitem" aria-haspopup="true"> <span class="sr-text">Back to</span> <span class="icon icon--caret"></span> Foundational ML & Algorithms <span class="sr-text">menu</span> </button> <hr/> </div> <ul> <li role="menuitem"> <a href="http://research.google/research-areas/algorithms-and-theory/" class="navigation__sub__columns__mobile__link" > Algorithms & Theory <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/data-management/" class="navigation__sub__columns__mobile__link" > Data Management <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/data-mining-and-modeling/" class="navigation__sub__columns__mobile__link" > Data Mining & Modeling <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/information-retrieval-and-the-web/" class="navigation__sub__columns__mobile__link" > Information Retrieval & the Web <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/machine-intelligence/" class="navigation__sub__columns__mobile__link" > Machine Intelligence <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/machine-perception/" class="navigation__sub__columns__mobile__link" > Machine Perception <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/machine-translation/" class="navigation__sub__columns__mobile__link" > Machine Translation <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/natural-language-processing/" class="navigation__sub__columns__mobile__link" > Natural Language Processing <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/speech-processing/" class="navigation__sub__columns__mobile__link" > Speech Processing <span> </span> </a> </li> </ul> </div> </div> </li> <li data-gt-secondary="Computing Systems & Quantum AI"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading">Computing Systems & Quantum AI</h2> <ul> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/distributed-systems-and-parallel-computing/" > Distributed Systems & Parallel Computing </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/hardware-and-architecture/" > Hardware & Architecture </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/mobile-systems/" > Mobile Systems </a> </li> <li> <a 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Software Systems </a> </li> </ul> </div> <div class="navigation__sub__columns__mobile"> <button class="glue-header__link js-sub-nav-target" data-panel="nested" role="menuitem" aria-haspopup="true"> Computing Systems & Quantum AI <span class="icon icon--caret"></span> </button> <div class="navigation__nested-sub js-sub-nav-parent"> <div class="navigation__sub__mobile-heading"> <button class="glue-header__link js-sub-nav-close-mobile" role="menuitem" aria-haspopup="true"> <span class="sr-text">Back to</span> <span class="icon icon--caret"></span> Computing Systems & Quantum AI <span class="sr-text">menu</span> </button> <hr/> </div> <ul> <li role="menuitem"> <a href="http://research.google/research-areas/distributed-systems-and-parallel-computing/" class="navigation__sub__columns__mobile__link" > Distributed Systems & Parallel Computing <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/hardware-and-architecture/" class="navigation__sub__columns__mobile__link" > Hardware & Architecture <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/mobile-systems/" class="navigation__sub__columns__mobile__link" > Mobile Systems <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/networking/" class="navigation__sub__columns__mobile__link" > Networking <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/quantum-computing/" class="navigation__sub__columns__mobile__link" > Quantum Computing <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/robotics/" class="navigation__sub__columns__mobile__link" > Robotics <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/security-privacy-and-abuse-prevention/" class="navigation__sub__columns__mobile__link" > Security, Privacy, & Abuse Prevention <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/software-engineering/" class="navigation__sub__columns__mobile__link" > Software Engineering <span> </span> </a> </li> <li role="menuitem"> <a href="http://research.google/research-areas/software-systems/" class="navigation__sub__columns__mobile__link" > Software Systems <span> </span> </a> </li> </ul> </div> </div> </li> <li data-gt-secondary="Science, AI & Society"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading">Science, AI & Society</h2> <ul> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/climate-and-sustainability/" > Climate & Sustainability </a> </li> <li> <a class="navigation__sub__columns__list-link caption js-drawer-link" href="http://research.google/research-areas/economics-and-electronic-commerce/" > Economics & Electronic Commerce </a> </li> <li> <a 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class="navigation__sub__container"> <div class="navigation__sub__mobile-heading"> <button class="glue-header__link js-sub-nav-close-mobile"> <span class="sr-text">Back to</span> <span class="icon icon--caret"></span> Our work <span class="sr-text">menu</span> </button> <hr/> </div> <div class="block-nav_drawer_columns_content"> <div class="navigation__sub--content" data-gt-secondary=""> <div class="navigation__sub__wrapper"> <ul class="navigation__sub__columns"> <li data-gt-secondary="Projects"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading"> Projects </h2> <p class="navigation__sub__columns__description caption">We regularly open-source projects with the broader research community and apply our developments to Google products.</p> <a href="http://research.google/resources/our-projects/" class="glue-inline-link js-drawer-link" > <span class="sr-text">Learn more about our Projects</span> <span aria-hidden="true">Learn more</span> </a> </div> <div class="navigation__sub__columns__mobile"> <a class="glue-header__link" href="http://research.google/resources/our-projects/" > Projects </a> </div> </li> <li data-gt-secondary="Publications"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading"> Publications </h2> <p class="navigation__sub__columns__description caption">Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.</p> <a href="http://research.google/pubs/" class="glue-inline-link js-drawer-link" > <span class="sr-text">Learn more about our Publications</span> <span aria-hidden="true">Learn more</span> </a> </div> <div class="navigation__sub__columns__mobile"> <a class="glue-header__link" href="http://research.google/pubs/" > Publications </a> </div> </li> <li data-gt-secondary="Resources"> <div class="navigation__sub__columns__desktop"> <h2 class="headline-6 navigation__sub__columns__heading"> Resources </h2> <p class="navigation__sub__columns__description caption">We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.</p> <a href="http://research.google/resources/" class="glue-inline-link js-drawer-link" > <span class="sr-text">Learn more about our Resources</span> <span aria-hidden="true">Learn more</span> </a> </div> <div class="navigation__sub__columns__mobile"> <a class="glue-header__link" href="http://research.google/resources/" > Resources </a> </div> </li> </ul> </div> </div> </div> </div> </div> </li> <li class="glue-header__item js-sub-nav-parent --parent" data-gt-primary="Programs & events" > <button class="glue-header__link js-sub-nav-target" aria-haspopup="true" aria-expanded="false" > <span class=""> Programs & events <span class="icon icon--caret"></span> </span> </button> <div class="navigation__sub js-sub-nav" role="menu"> <div class="navigation__sub__container"> <div 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for="filter-type-google"> <span class="js-filter-option-label-text">Google</span> <span class="filter-option-label-count">10</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-type-other" name="filter-type-other" value="other" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-type-other"> <span class="js-filter-option-label-text">Other</span> <span class="filter-option-label-count">0</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-year" data-glue-expansion-panel-toggle-for="filter-panel-year"> <span class="glue-expansion-panel__header-text" id="filter-year-label">Years</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-year"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="year" role="list" aria-controls="filter-results" aria-labelledby="filter-year-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2024" name="filter-year-2024" value="2024" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2024"> <span class="js-filter-option-label-text">2024</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2023" name="filter-year-2023" value="2023" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path 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<ul class="chipset-list" title="Applied filters"></ul> </div> </div> <div class="publications-list --theme- row-card-list" data-hot-swap="google-publications-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/general-geospatial-inference-with-a-population-dynamics-foundation-model/ > General Geospatial Inference with a Population Dynamics Foundation Model </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/mohitagarwal/"> Mohit Agarwal </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/mimisun/"> Mimi Sun </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Chaitanya Kamath </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/arbaazmuslim/"> Arbaaz Muslim </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Prithul Sarker </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Joydeep Paul </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/hectoryee/"> Hector Yee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/marcinsieniek/"> Marcin Sieniek </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/kimjablonski/"> Kim Jablonski </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yael Mayer </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/davidfork/"> David Fork </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Sheila de Guia </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jamie McPike </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Adam Boulanger </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107849/"> Tomer Shekel </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> David Schottlander </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Yao Xiao </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Manjit Chakravarthy Manukonda </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105698/"> Yun Liu </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108580/"> Neslihan Bulut </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/author39322/"> Arno Eigenwillig </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Monica Bharel </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Von Nguyen </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Luke Barrington </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Niv Efron </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/yossimatias/"> Yossi Matias </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/gregcorrado/"> Greg Corrado </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Krish Eswaran </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/shruthiprabhakara/"> Shruthi Prabhakara </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Shravya Shetty </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/gautamprasad/"> Gautam Prasad </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> (2024) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentsupporting-the-health-and-well-bei tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentsupporting-the-health-and-well-bei" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Supporting the health and well-being of dynamic populations around the world requires governmental agencies, organizations, and researchers to understand and reason over complex relationships between human behavior and local contexts. This support includes identifying populations at elevated risk and gauging where to target limited aid resources. Traditional approaches to these classes of problems often entail developing manually curated, task-specific features and models to represent human behavior and the natural and built environment, which can be challenging to adapt to new, or even related tasks. To address this, we introduce the Population Dynamics Foundation Model (PDFM), which aims to capture the relationships between diverse data modalities and is applicable to a broad range of geospatial tasks. We first construct a geo-indexed dataset for postal codes and counties across the United States, capturing rich aggregated information on human behavior from maps, busyness, and aggregated search trends, and environmental factors such as weather and air quality. We then model this data and the complex relationships between locations using a graph neural network, producing embeddings that can be adapted to a wide range of downstream tasks using relatively simple models. We evaluate the effectiveness of our approach by benchmarking it on 27 downstream tasks spanning three distinct domains: health indicators, socioeconomic factors, and environmental measurements. The approach achieves state-of-the-art performance on geospatial interpolation across all tasks, surpassing existing satellite and geotagged image based location encoders. In addition, it achieves state-of-the-art performance in extrapolation and super-resolution for 25 of the 27 tasks. We also show that the PDFM can be combined with a state-of-the-art forecasting foundation model, TimesFM, to predict unemployment and poverty, achieving performance that surpasses fully supervised forecasting. The full set of embeddings and sample code are publicly available for researchers. In conclusion, we have demonstrated a general purpose approach to geospatial modeling tasks critical to understanding population dynamics by leveraging a rich set of complementary globally available datasets that can be readily adapted to previously unseen machine learning tasks.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/general-geospatial-inference-with-a-population-dynamics-foundation-model/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/tpugraphs-performance-prediction-datasets-on-large-tensor-computational-graphs/ > TpuGraphs: Performance Prediction Datasets on Large Tensor Computational Graphs </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Mangpo Phothilimthana </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Kaidi Cao </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108300/"> Bahar Fatemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Charith Mendis </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Advances in Neural Information Processing Systems (2023) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentprecise-hardware-performance-model tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentprecise-hardware-performance-model" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal configuration for a given program. For example, the autotuner for XLA, a machine learning compiler, discovered 10–20\% speedup on state-of-the-art models serving substantial production traffic at Google. Although there exist a few datasets for program performance prediction, they target small sub-programs such as basic blocks or kernels. This paper introduces TpuGraphs, a performance prediction dataset on full tensor programs, represented as computational graphs, running on Tensor Processing Units (TPUs). Each graph in the dataset represents the main computation of a machine learning workload, eg, a training epoch or an inference step. Each data sample contains a computational graph, a compilation configuration, and the execution time of the graph when compiled with the configuration. The graphs in the dataset are collected from open-source machine learning programs, featuring popular model architectures (eg, ResNet, EfficientNet, Mask R-CNN, and Transformer). TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs. This graph-level prediction task on large graphs introduces new challenges in learning, ranging from scalability, training efficiency, to model quality.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/tpugraphs-performance-prediction-datasets-on-large-tensor-computational-graphs/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/learning-large-graph-property-prediction-via-graph-segment-training/ > Learning Large Graph Property Prediction via Graph Segment Training </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Kaidi Cao </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Mangpo Phothilimthana </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/106375/"> Dustin Zelle </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/107323/"> Yanqi Zhou </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Charith Mendis </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jure Leskovec </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Advances in Neural Information Processing Systems (2023) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentlearning-to-predict-properties-of tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentlearning-to-predict-properties-of" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during training is bounded. Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint. GST first divides a large graph into segments and then backpropagates through only a few segments sampled per training iteration. We refine the GST paradigm by introducing a historical embedding table to efficiently obtain embeddings for segments not sampled for backpropagation. To mitigate the staleness of historical embeddings, we design two novel techniques. First, we finetune the prediction head to fix the input distribution shift. Second, we introduce Stale Embedding Dropout to drop some stale embeddings during training to reduce bias. We evaluate our complete method GST-EFD (with all the techniques together) on two large graph property prediction benchmarks: MalNet and TpuGraphs. Our experiments show that GST-EFD is both memory-efficient and fast, while offering a slight boost on test accuracy over a typical full graph training regime.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/learning-large-graph-property-prediction-via-graph-segment-training/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/end-to-end-learning-of-compressible-features/ > End-to-end Learning of Compressible Features </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/saurabhsingh/"> Saurabh Singh </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/nickjohnston/"> Nick Johnston </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Johannes Ballé </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Abhinav Shrivastava </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/author38233/"> George Dan Toderici </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> 2020 IEEE Int. Conf. on Image Processing (ICIP) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentpre-trained-convolutional-neural-n tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentpre-trained-convolutional-neural-n" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Pre-trained convolutional neural networks (CNNs) are very powerful as an off the shelf feature generator and have been shown to perform very well on a variety of tasks. Unfortunately, the generated features are high dimensional and expensive to store: potentially hundreds of thousands of floats per example when processing videos. Traditional entropy based lossless compression methods are of little help as they do not yield desired level of compression while general purpose lossy alternatives (e.g. dimensionality reduction techniques) are sub-optimal as they end up losing important information. We propose a learned method that jointly optimizes for compressibility along with the original objective for learning the features. The plug-in nature of our method makes it straight-forward to integrate with any target objective and trade-off against compressibility. We present results on multiple benchmarks and demonstrate that features learned by our method maintain their informativeness while being order of magnitude more compressible.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/end-to-end-learning-of-compressible-features/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/watch-your-step-learning-node-embeddings-via-graph-attention/ > Watch Your Step: Learning Node Embeddings via Graph Attention </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Rami Al-Rfou </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104980/"> Alex Alemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> NIPS (2018) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentgraph-embedding-methods-represent tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentgraph-embedding-methods-represent" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Graph embedding methods represent nodes in a continuous vector space, preserving information from the graph (e.g. by sampling random walks). There are many hyper-parameters to these methods (such as random walk length) which have to be manually tuned for every graph. In this paper, we replace random walk hyper-parameters with trainable parameters that we automatically learn via backpropagation. In particular, we learn a novel attention model on the power series of the transition matrix, which guides the random walk to optimize an upstream objective. Unlike previous approaches to attention models, the method that we propose utilizes attention parameters exclusively on the data (e.g. on the random walk), and not used by the model for inference. We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art on a comprehensive suite of real world datasets including social, collaboration, and biological networks. Adding attention to random walks can reduce the error by 20% to 45% on datasets we attempted. Further, our learned attention parameters are different for every graph, and our automatically-found values agree with the optimal choice of hyper-parameter if we manually tune existing methods.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/watch-your-step-learning-node-embeddings-via-graph-attention/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/collaborative-deep-metric-learning-for-video-understanding/ > Collaborative Deep Metric Learning for Video Understanding </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/joonseoklee/"> Joonseok Lee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Balakrishnan Varadarajan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/apostolnatsev/"> Apostol (Paul) Natsev </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM (2018) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentthe-goal-of-video-understanding-is tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentthe-goal-of-video-understanding-is" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">The goal of video understanding is to develop algorithms that enable machines understand videos at the level of human experts. Researchers have tackled various domains including video classification, search, personalized recommendation, and more. However, there is a research gap in combining these domains in one unified learning framework. Towards that, we propose a deep network that embeds videos using their audio-visual content, onto a metric space which preserves video-to-video relationships. Then, we use the trained embedding network to tackle various domains including video classification and recommendation, showing significant improvements over state-of-the-art baselines. The proposed approach is highly scalable to deploy on large-scale video sharing platforms like YouTube.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/collaborative-deep-metric-learning-for-video-understanding/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/learning-edge-representations-via-low-rank-asymmetric-projections/ > Learning Edge Representations via Low-Rank Asymmetric Projections </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Rami Al-Rfou </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ACM International Conference on Information and Knowledge Management (2017) (to appear) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwe-propose-a-new-method-for-embedd tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwe-propose-a-new-method-for-embedd" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the "graph likelihood", which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/learning-edge-representations-via-low-rank-asymmetric-projections/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/large-scale-content-only-video-recommendation/ > Large-Scale Content-Only Video Recommendation </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/joonseoklee/"> Joonseok Lee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> International Conference on Computer Vision Workshop, Computer Vision Foundation (2017), pp. 987 - 995 </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contenttraditional-recommendation-systems tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contenttraditional-recommendation-systems" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Traditional recommendation systems using collaborative filtering (CF) approaches work relatively well when the candidate videos are sufficiently popular. With the increase of user-created videos, however, recommending fresh videos gets more and more important, but pure CF-based systems may not perform well in such cold-start situation. In this paper, we model recommendation as a video content-based similarity learning problem, and learn deep video embeddings trained to predict video relationships identified by a co-watch-based system but using only visual and audial content. The system does not depend on availability on video meta-data, and can generalize to both popular and tail content, including new video uploads. We demonstrate performance of the proposed method in large-scale datasets, both quantitatively and qualitatively.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/large-scale-content-only-video-recommendation/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/detecting-events-and-key-actors-in-multi-person-videos/ > Detecting Events and Key Actors in Multi-Person Videos </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Vignesh Ramanathan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Jonathan Huang </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Alexander Gorban </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/kevinmurphy/"> Kevin Murphy </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Li Fei-Fei </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Computer Vision and Pattern Recognition (CVPR) (2016) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmulti-person-event-recognition-is tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmulti-person-event-recognition-is" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Multi-person event recognition is a challenging task, often with many people active in the scene but only a small subset contributing to an actual event. In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event. Our model does not use explicit annotations regarding who or where those people are during training and testing. In particular, we track people in videos and use a recurrent neural network (RNN) to represent the track features. We learn time-varying attention weights to combine these features at each time-instant. The attended features are then processed using another RNN for event detection/classification. Since most video datasets with multiple people are restricted to a small number of videos, we also collected a new basketball dataset comprising 257 basketball games with 14K event annotations corresponding to 11 event classes. Our model outperforms state-of-the-art methods for both event classification and detection on this new dataset. Additionally, we show that the attention mechanism is able to consistently localize the relevant players.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/detecting-events-and-key-actors-in-multi-person-videos/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=http://research.google/pubs/youtube-8m-a-large-scale-video-classification-benchmark/ > YouTube-8M: A Large-Scale Video Classification Benchmark </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Nisarg Kothari </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/joonseoklee/"> Joonseok Lee </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/apostolnatsev/"> Apostol (Paul) Natsev </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/author38233/"> George Toderici </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Balakrishnan Varadarajan </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/105363/"> Sudheendra Vijayanarasimhan </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> arXiv:1609.08675 (2016) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentmany-recent-advancements-in-comput tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentmany-recent-advancements-in-comput" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of ~8 million videos---500K hours of video---annotated with a vocabulary of 4803 visual entities. To get the videos and their (multiple) labels, we used the YouTube Data APIs. We filtered the video labels (Freebase topics) using both automated and manual curation strategies, including by asking Mechanical Turk workers if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pre-trained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. The dataset contains frame-level features for over 1.9 billion video frames and 8 million videos, making it the largest public multi-label video dataset. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using the publicly-available TensorFlow framework. We plan to release code for training a basic TensorFlow model and for computing metrics. We show that pre-training on large data generalizes to other datasets like Sports-1M and ActivityNet. We achieve state-of-the-art on ActivityNet, improving mAP from 53.8% to 77.8%. We hope that the unprecedented scale and diversity of YouTube-8M will lead to advances in video understanding and representation learning.</span> <a class="glue-button glue-button--low-emphasis" href="http://research.google/pubs/youtube-8m-a-large-scale-video-classification-benchmark/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> <div class="filtered-list-base__pagination" data-hot-swap="pagination"> </div> </div> </div> </section> <section class="google_scholar_search">  <a href="#" class="js-google-scholar-search-link glue-button glue-button--low-emphasis glue-button--icon" title="See more related publications on Google Scholar" target="_blank"> <svg role="presentation" aria-hidden="true" class="glue-icon glue-icon--24px "> <use href="/gr/static/assets/icons/glue-icons.svg#open-in-new"></use> </svg> Search on Google Scholar </a> </section> </div> <section class="banner --theme-" data-gt-id="banner" 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