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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.13349">arXiv:2402.13349</a> <span> [<a href="https://arxiv.org/pdf/2402.13349">pdf</a>, <a href="https://arxiv.org/format/2402.13349">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Aria Everyday Activities Dataset </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Lv%2C+Z">Zhaoyang Lv</a>, <a href="/search/cs?searchtype=author&query=Charron%2C+N">Nicholas Charron</a>, <a href="/search/cs?searchtype=author&query=Moulon%2C+P">Pierre Moulon</a>, <a href="/search/cs?searchtype=author&query=Gamino%2C+A">Alexander Gamino</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+C">Cheng Peng</a>, <a href="/search/cs?searchtype=author&query=Sweeney%2C+C">Chris Sweeney</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+E">Edward Miller</a>, <a href="/search/cs?searchtype=author&query=Tang%2C+H">Huixuan Tang</a>, <a href="/search/cs?searchtype=author&query=Meissner%2C+J">Jeff Meissner</a>, <a href="/search/cs?searchtype=author&query=Dong%2C+J">Jing Dong</a>, <a href="/search/cs?searchtype=author&query=Somasundaram%2C+K">Kiran Somasundaram</a>, <a href="/search/cs?searchtype=author&query=Pesqueira%2C+L">Luis Pesqueira</a>, <a href="/search/cs?searchtype=author&query=Schwesinger%2C+M">Mark Schwesinger</a>, <a href="/search/cs?searchtype=author&query=Parkhi%2C+O">Omkar Parkhi</a>, <a href="/search/cs?searchtype=author&query=Gu%2C+Q">Qiao Gu</a>, <a href="/search/cs?searchtype=author&query=De+Nardi%2C+R">Renzo De Nardi</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+S">Shangyi Cheng</a>, <a href="/search/cs?searchtype=author&query=Saarinen%2C+S">Steve Saarinen</a>, <a href="/search/cs?searchtype=author&query=Baiyya%2C+V">Vijay Baiyya</a>, <a href="/search/cs?searchtype=author&query=Zou%2C+Y">Yuyang Zou</a>, <a href="/search/cs?searchtype=author&query=Newcombe%2C+R">Richard Newcombe</a>, <a href="/search/cs?searchtype=author&query=Engel%2C+J+J">Jakob Julian Engel</a>, <a href="/search/cs?searchtype=author&query=Pan%2C+X">Xiaqing Pan</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+C">Carl Ren</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.13349v2-abstract-short" style="display: inline;"> We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data includi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13349v2-abstract-full').style.display = 'inline'; document.getElementById('2402.13349v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.13349v2-abstract-full" style="display: none;"> We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13349v2-abstract-full').style.display = 'none'; document.getElementById('2402.13349v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Dataset website: https://www.projectaria.com/datasets/aea/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02441">arXiv:2402.02441</a> <span> [<a href="https://arxiv.org/pdf/2402.02441">pdf</a>, <a href="https://arxiv.org/format/2402.02441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Software">cs.MS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> </div> </div> <p class="title is-5 mathjax"> TopoX: A Suite of Python Packages for Machine Learning on Topological Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hajij%2C+M">Mustafa Hajij</a>, <a href="/search/cs?searchtype=author&query=Papillon%2C+M">Mathilde Papillon</a>, <a href="/search/cs?searchtype=author&query=Frantzen%2C+F">Florian Frantzen</a>, <a href="/search/cs?searchtype=author&query=Agerberg%2C+J">Jens Agerberg</a>, <a href="/search/cs?searchtype=author&query=AlJabea%2C+I">Ibrahem AlJabea</a>, <a href="/search/cs?searchtype=author&query=Ballester%2C+R">Ruben Ballester</a>, <a href="/search/cs?searchtype=author&query=Battiloro%2C+C">Claudio Battiloro</a>, <a href="/search/cs?searchtype=author&query=Bern%C3%A1rdez%2C+G">Guillermo Bern谩rdez</a>, <a href="/search/cs?searchtype=author&query=Birdal%2C+T">Tolga Birdal</a>, <a href="/search/cs?searchtype=author&query=Brent%2C+A">Aiden Brent</a>, <a href="/search/cs?searchtype=author&query=Chin%2C+P">Peter Chin</a>, <a href="/search/cs?searchtype=author&query=Escalera%2C+S">Sergio Escalera</a>, <a href="/search/cs?searchtype=author&query=Fiorellino%2C+S">Simone Fiorellino</a>, <a href="/search/cs?searchtype=author&query=Gardaa%2C+O+H">Odin Hoff Gardaa</a>, <a href="/search/cs?searchtype=author&query=Gopalakrishnan%2C+G">Gurusankar Gopalakrishnan</a>, <a href="/search/cs?searchtype=author&query=Govil%2C+D">Devendra Govil</a>, <a href="/search/cs?searchtype=author&query=Hoppe%2C+J">Josef Hoppe</a>, <a href="/search/cs?searchtype=author&query=Karri%2C+M+R">Maneel Reddy Karri</a>, <a href="/search/cs?searchtype=author&query=Khouja%2C+J">Jude Khouja</a>, <a href="/search/cs?searchtype=author&query=Lecha%2C+M">Manuel Lecha</a>, <a href="/search/cs?searchtype=author&query=Livesay%2C+N">Neal Livesay</a>, <a href="/search/cs?searchtype=author&query=Mei%C3%9Fner%2C+J">Jan Mei脽ner</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Soham Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Nikitin%2C+A">Alexander Nikitin</a>, <a href="/search/cs?searchtype=author&query=Papamarkou%2C+T">Theodore Papamarkou</a> , et al. (18 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.02441v4-abstract-short" style="display: inline;"> We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02441v4-abstract-full').style.display = 'inline'; document.getElementById('2402.02441v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02441v4-abstract-full" style="display: none;"> We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://pyt-team.github.io/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02441v4-abstract-full').style.display = 'none'; document.getElementById('2402.02441v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.13093">arXiv:2308.13093</a> <span> [<a href="https://arxiv.org/pdf/2308.13093">pdf</a>, <a href="https://arxiv.org/format/2308.13093">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> EgoBlur: Responsible Innovation in Aria </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raina%2C+N">Nikhil Raina</a>, <a href="/search/cs?searchtype=author&query=Somasundaram%2C+G">Guruprasad Somasundaram</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+K">Kang Zheng</a>, <a href="/search/cs?searchtype=author&query=Miglani%2C+S">Sagar Miglani</a>, <a href="/search/cs?searchtype=author&query=Saarinen%2C+S">Steve Saarinen</a>, <a href="/search/cs?searchtype=author&query=Meissner%2C+J">Jeff Meissner</a>, <a href="/search/cs?searchtype=author&query=Schwesinger%2C+M">Mark Schwesinger</a>, <a href="/search/cs?searchtype=author&query=Pesqueira%2C+L">Luis Pesqueira</a>, <a href="/search/cs?searchtype=author&query=Prasad%2C+I">Ishita Prasad</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+E">Edward Miller</a>, <a href="/search/cs?searchtype=author&query=Gupta%2C+P">Prince Gupta</a>, <a href="/search/cs?searchtype=author&query=Yan%2C+M">Mingfei Yan</a>, <a href="/search/cs?searchtype=author&query=Newcombe%2C+R">Richard Newcombe</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+C">Carl Ren</a>, <a href="/search/cs?searchtype=author&query=Parkhi%2C+O+M">Omkar M Parkhi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.13093v2-abstract-short" style="display: inline;"> Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach. To protect the privacy of bystanders being recorded by the glasses, our research protocols are designed to ensure recorded video is processed by an AI anonymization model that removes bystander faces and vehicle license plates. Detected face a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13093v2-abstract-full').style.display = 'inline'; document.getElementById('2308.13093v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.13093v2-abstract-full" style="display: none;"> Project Aria pushes the frontiers of Egocentric AI with large-scale real-world data collection using purposely designed glasses with privacy first approach. To protect the privacy of bystanders being recorded by the glasses, our research protocols are designed to ensure recorded video is processed by an AI anonymization model that removes bystander faces and vehicle license plates. Detected face and license plate regions are processed with a Gaussian blur such that these personal identification information (PII) regions are obscured. This process helps to ensure that anonymized versions of the video is retained for research purposes. In Project Aria, we have developed a state-of-the-art anonymization system EgoBlur. In this paper, we present extensive analysis of EgoBlur on challenging datasets comparing its performance with other state-of-the-art systems from industry and academia including extensive Responsible AI analysis on recently released Casual Conversations V2 dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13093v2-abstract-full').style.display = 'none'; document.getElementById('2308.13093v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.16079">arXiv:2210.16079</a> <span> [<a href="https://arxiv.org/pdf/2210.16079">pdf</a>, <a href="https://arxiv.org/format/2210.16079">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Debiasing Masks: A New Framework for Shortcut Mitigation in NLU </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Meissner%2C+J+M">Johannes Mario Meissner</a>, <a href="/search/cs?searchtype=author&query=Sugawara%2C+S">Saku Sugawara</a>, <a href="/search/cs?searchtype=author&query=Aizawa%2C+A">Akiko Aizawa</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2210.16079v1-abstract-short" style="display: inline;"> Debiasing language models from unwanted behaviors in Natural Language Understanding tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical correlations in the data allow models to perform shortcuts and avoid uncovering more advanced and desirable linguistic features. A multitude of effective debiasing approaches has been proposed, but flexibility remains a maj… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16079v1-abstract-full').style.display = 'inline'; document.getElementById('2210.16079v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.16079v1-abstract-full" style="display: none;"> Debiasing language models from unwanted behaviors in Natural Language Understanding tasks is a topic with rapidly increasing interest in the NLP community. Spurious statistical correlations in the data allow models to perform shortcuts and avoid uncovering more advanced and desirable linguistic features. A multitude of effective debiasing approaches has been proposed, but flexibility remains a major issue. For the most part, models must be retrained to find a new set of weights with debiased behavior. We propose a new debiasing method in which we identify debiased pruning masks that can be applied to a finetuned model. This enables the selective and conditional application of debiasing behaviors. We assume that bias is caused by a certain subset of weights in the network; our method is, in essence, a mask search to identify and remove biased weights. Our masks show equivalent or superior performance to the standard counterparts, while offering important benefits. Pruning masks can be stored with high efficiency in memory, and it becomes possible to switch among several debiasing behaviors (or revert back to the original biased model) at inference time. Finally, it opens the doors to further research on how biases are acquired by studying the generated masks. For example, we observed that the early layers and attention heads were pruned more aggressively, possibly hinting towards the location in which biases may be encoded. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.16079v1-abstract-full').style.display = 'none'; document.getElementById('2210.16079v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">EMNLP 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.01824">arXiv:2209.01824</a> <span> [<a href="https://arxiv.org/pdf/2209.01824">pdf</a>, <a href="https://arxiv.org/format/2209.01824">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ho%2C+X">Xanh Ho</a>, <a href="/search/cs?searchtype=author&query=Meissner%2C+J+M">Johannes Mario Meissner</a>, <a href="/search/cs?searchtype=author&query=Sugawara%2C+S">Saku Sugawara</a>, <a href="/search/cs?searchtype=author&query=Aizawa%2C+A">Akiko Aizawa</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.01824v2-abstract-short" style="display: inline;"> The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve tasks that were meant to exhibit advanced language understanding and reasoning capabilities. In this survey paper, we focus on the field of machine reading comprehension (MRC), an important task for showcasing high-level l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.01824v2-abstract-full').style.display = 'inline'; document.getElementById('2209.01824v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.01824v2-abstract-full" style="display: none;"> The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve tasks that were meant to exhibit advanced language understanding and reasoning capabilities. In this survey paper, we focus on the field of machine reading comprehension (MRC), an important task for showcasing high-level language understanding that also suffers from a range of shortcuts. We summarize the available techniques for measuring and mitigating shortcuts and conclude with suggestions for further progress in shortcut research. Importantly, we highlight two concerns for shortcut mitigation in MRC: (1) the lack of public challenge sets, a necessary component for effective and reusable evaluation, and (2) the lack of certain mitigation techniques that are prominent in other areas. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.01824v2-abstract-full').style.display = 'none'; document.getElementById('2209.01824v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 2 figures, 4 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.03020">arXiv:2106.03020</a> <span> [<a href="https://arxiv.org/pdf/2106.03020">pdf</a>, <a href="https://arxiv.org/format/2106.03020">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Embracing Ambiguity: Shifting the Training Target of NLI Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Meissner%2C+J+M">Johannes Mario Meissner</a>, <a href="/search/cs?searchtype=author&query=Thumwanit%2C+N">Napat Thumwanit</a>, <a href="/search/cs?searchtype=author&query=Sugawara%2C+S">Saku Sugawara</a>, <a href="/search/cs?searchtype=author&query=Aizawa%2C+A">Akiko Aizawa</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.03020v1-abstract-short" style="display: inline;"> Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI ta… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.03020v1-abstract-full').style.display = 'inline'; document.getElementById('2106.03020v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.03020v1-abstract-full" style="display: none;"> Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels. While many research works do not pay much attention to this fact, several recent efforts have been made to acknowledge and embrace the existence of ambiguity, such as UNLI and ChaosNLI. In this paper, we explore the option of training directly on the estimated label distribution of the annotators in the NLI task, using a learning loss based on this ambiguity distribution instead of the gold-labels. We prepare AmbiNLI, a trial dataset obtained from readily available sources, and show it is possible to reduce ChaosNLI divergence scores when finetuning on this data, a promising first step towards learning how to capture linguistic ambiguity. Additionally, we show that training on the same amount of data but targeting the ambiguity distribution instead of gold-labels can result in models that achieve higher performance and learn better representations for downstream tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.03020v1-abstract-full').style.display = 'none'; document.getElementById('2106.03020v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted to ACL 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1709.02592">arXiv:1709.02592</a> <span> [<a href="https://arxiv.org/pdf/1709.02592">pdf</a>, <a href="https://arxiv.org/format/1709.02592">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> An Adversarial Model for Scheduling with Testing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=D%C3%BCrr%2C+C">Christoph D眉rr</a>, <a href="/search/cs?searchtype=author&query=Erlebach%2C+T">Thomas Erlebach</a>, <a href="/search/cs?searchtype=author&query=Megow%2C+N">Nicole Megow</a>, <a href="/search/cs?searchtype=author&query=Mei%C3%9Fner%2C+J">Julie Mei脽ner</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1709.02592v3-abstract-short" style="display: inline;"> We introduce a novel adversarial model for scheduling with explorable uncertainty. In this model, the processing time of a job can potentially be reduced (by an a priori unknown amount) by testing the job. Testing a job $j$ takes one unit of time and may reduce its processing time from the given upper limit $\bar{p}_j$ (which is the time taken to execute the job if it is not tested) to any value b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.02592v3-abstract-full').style.display = 'inline'; document.getElementById('1709.02592v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1709.02592v3-abstract-full" style="display: none;"> We introduce a novel adversarial model for scheduling with explorable uncertainty. In this model, the processing time of a job can potentially be reduced (by an a priori unknown amount) by testing the job. Testing a job $j$ takes one unit of time and may reduce its processing time from the given upper limit $\bar{p}_j$ (which is the time taken to execute the job if it is not tested) to any value between $0$ and $\bar{p}_j$. This setting is motivated e.g. by applications where a code optimizer can be run on a job before executing it. We consider the objective of minimizing the sum of completion times on a single machine. All jobs are available from the start, but the reduction in their processing times as a result of testing is unknown, making this an online problem that is amenable to competitive analysis. The need to balance the time spent on tests and the time spent on job executions adds a novel flavor to the problem. We give the first and nearly tight lower and upper bounds on the competitive ratio for deterministic and randomized algorithms. We also show that minimizing the makespan is a considerably easier problem for which we give optimal deterministic and randomized online algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1709.02592v3-abstract-full').style.display = 'none'; document.getElementById('1709.02592v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 September, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2017. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1701.08809">arXiv:1701.08809</a> <span> [<a href="https://arxiv.org/pdf/1701.08809">pdf</a>, <a href="https://arxiv.org/ps/1701.08809">ps</a>, <a href="https://arxiv.org/format/1701.08809">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Structures and Algorithms">cs.DS</span> </div> </div> <p class="title is-5 mathjax"> Scheduling Maintenance Jobs in Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Abed%2C+F">Fidaa Abed</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lin Chen</a>, <a href="/search/cs?searchtype=author&query=Disser%2C+Y">Yann Disser</a>, <a href="/search/cs?searchtype=author&query=Gro%C3%9F%2C+M">Martin Gro脽</a>, <a href="/search/cs?searchtype=author&query=Megow%2C+N">Nicole Megow</a>, <a href="/search/cs?searchtype=author&query=Mei%C3%9Fner%2C+J">Julie Mei脽ner</a>, <a href="/search/cs?searchtype=author&query=Richter%2C+A+T">Alexander T. Richter</a>, <a href="/search/cs?searchtype=author&query=Rischke%2C+R">Roman Rischke</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="1701.08809v1-abstract-short" style="display: inline;"> We investigate the problem of scheduling the maintenance of edges in a network, motivated by the goal of minimizing outages in transportation or telecommunication networks. We focus on maintaining connectivity between two nodes over time; for the special case of path networks, this is related to the problem of minimizing the busy time of machines. We show that the problem can be solved in polyno… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.08809v1-abstract-full').style.display = 'inline'; document.getElementById('1701.08809v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1701.08809v1-abstract-full" style="display: none;"> We investigate the problem of scheduling the maintenance of edges in a network, motivated by the goal of minimizing outages in transportation or telecommunication networks. We focus on maintaining connectivity between two nodes over time; for the special case of path networks, this is related to the problem of minimizing the busy time of machines. We show that the problem can be solved in polynomial time in arbitrary networks if preemption is allowed. If preemption is restricted to integral time points, the problem is NP-hard and in the non-preemptive case we give strong non-approximability results. Furthermore, we give tight bounds on the power of preemption, that is, the maximum ratio of the values of non-preemptive and preemptive optimal solutions. Interestingly, the preemptive and the non-preemptive problem can be solved efficiently on paths, whereas we show that mixing both leads to a weakly NP-hard problem that allows for a simple 2-approximation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1701.08809v1-abstract-full').style.display = 'none'; document.getElementById('1701.08809v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2017. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">CIAC 2017</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> F.2.2 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 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