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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/2410.16159">arXiv:2410.16159</a> <span> [<a href="https://arxiv.org/pdf/2410.16159">pdf</a>, <a href="https://arxiv.org/format/2410.16159">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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</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="2410.16159v1-abstract-short" style="display: inline;"> Artificial Neural Networks of varying architectures are generally paired with affine transformation at the core. However, we find dot product neurons with global influence less interpretable as compared to local influence of euclidean distance (as used in Radial Basis Function Network). In this work, we explore the generalization of dot product neurons to $l^p$-norm, metrics, and beyond. We find t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16159v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16159v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16159v1-abstract-full" style="display: none;"> Artificial Neural Networks of varying architectures are generally paired with affine transformation at the core. However, we find dot product neurons with global influence less interpretable as compared to local influence of euclidean distance (as used in Radial Basis Function Network). In this work, we explore the generalization of dot product neurons to $l^p$-norm, metrics, and beyond. We find that metrics as transform performs similarly to affine transform when used in MultiLayer Perceptron or Convolutional Neural Network. Moreover, we explore various properties of Metrics, compare it with Affine, and present multiple cases where metrics seem to provide better interpretability. We develop an interpretable local dictionary based Neural Networks and use it to understand and reject adversarial examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16159v1-abstract-full').style.display = 'none'; document.getElementById('2410.16159v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">22 pages, 20 figures, 3 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/2409.10895">arXiv:2409.10895</a> <span> [<a href="https://arxiv.org/pdf/2409.10895">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> </div> </div> <p class="title is-5 mathjax"> An Exploration of Effects of Dark Mode on University Students: A Human Computer Interface Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shrestha%2C+A">Awan Shrestha</a>, <a href="/search/cs?searchtype=author&query=Shrestha%2C+S">Sabil Shrestha</a>, <a href="/search/cs?searchtype=author&query=Paneru%2C+B">Biplov Paneru</a>, <a href="/search/cs?searchtype=author&query=Paneru%2C+B">Bishwash Paneru</a>, <a href="/search/cs?searchtype=author&query=Paudel%2C+S">Sansrit Paudel</a>, <a href="/search/cs?searchtype=author&query=Adhikari%2C+A">Ashish Adhikari</a>, <a href="/search/cs?searchtype=author&query=Sapkota%2C+S+C">Sanjog Chhetri Sapkota</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="2409.10895v1-abstract-short" style="display: inline;"> This research dives into exploring the dark mode effects on students of a university. Research is carried out implementing the dark mode in e-Learning sites and its impact on behavior of the users. Students are spending more time in front of the screen for their studies especially after the pandemic. The blue light from the screen during late hours affects circadian rhythm of the body which negati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10895v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10895v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10895v1-abstract-full" style="display: none;"> This research dives into exploring the dark mode effects on students of a university. Research is carried out implementing the dark mode in e-Learning sites and its impact on behavior of the users. Students are spending more time in front of the screen for their studies especially after the pandemic. The blue light from the screen during late hours affects circadian rhythm of the body which negatively impacts the health of humans including eye strain and headache. The difficulty that students faced during the time of interacting with various e-Learning sites especially during late hours was analyzed using different techniques of HCI like survey, interview, evaluation methods and principles of design. Dark mode is an option which creates a pseudo inverted adaptable interface by changing brighter elements of UI into a dim-lit friendly environment. It is said that using dark mode will lessen the amount of blue light emitted and benefit students who suffer from eye strain. Students' interactions with dark mode were investigated using a survey, and an e-learning site with a dark mode theme was created. Based on the students' comments, researchers looked into the effects of dark mode on HCI in e-learning sites. The findings indicate that students have a clear preference for dark mode: 79.7% of survey participants preferred dark mode on their phones, and 61.7% said they would be interested in seeing this feature added to e-learning websites. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10895v1-abstract-full').style.display = 'none'; document.getElementById('2409.10895v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">none</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.00035">arXiv:2409.00035</a> <span> [<a href="https://arxiv.org/pdf/2409.00035">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neural and Evolutionary Computing">cs.NE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Neurons and Cognition">q-bio.NC</span> </div> </div> <p class="title is-5 mathjax"> EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Paneru%2C+B">Biplov Paneru</a>, <a href="/search/cs?searchtype=author&query=Thapa%2C+B">Bipul Thapa</a>, <a href="/search/cs?searchtype=author&query=Paneru%2C+B">Bishwash Paneru</a>, <a href="/search/cs?searchtype=author&query=Sapkota%2C+S+C">Sanjog Chhetri Sapkota</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="2409.00035v2-abstract-short" style="display: inline;"> Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, us… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00035v2-abstract-full').style.display = 'inline'; document.getElementById('2409.00035v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.00035v2-abstract-full" style="display: none;"> Brain-machine interfaces (BMIs), particularly those based on electroencephalography (EEG), offer promising solutions for assisting individuals with motor disabilities. However, challenges in reliably interpreting EEG signals for specific tasks, such as simulating keystrokes, persist due to the complexity and variability of brain activity. Current EEG-based BMIs face limitations in adaptability, usability, and robustness, especially in applications like virtual keyboards, as traditional machine-learning models struggle to handle high-dimensional EEG data effectively. To address these gaps, we developed an EEG-based BMI system capable of accurately identifying voluntary keystrokes, specifically leveraging right and left voluntary hand movements. Using a publicly available EEG dataset, the signals were pre-processed with band-pass filtering, segmented into 22-electrode arrays, and refined into event-related potential (ERP) windows, resulting in a 19x200 feature array categorized into three classes: resting state (0), 'd' key press (1), and 'l' key press (2). Our approach employs a hybrid neural network architecture with BiGRU-Attention as the proposed model for interpreting EEG signals, achieving superior test accuracy of 90% and a mean accuracy of 91% in 10-fold stratified cross-validation. This performance outperforms traditional ML methods like Support Vector Machines (SVMs) and Naive Bayes, as well as advanced architectures such as Transformers, CNN-Transformer hybrids, and EEGNet. Finally, the BiGRU-Attention model is integrated into a real-time graphical user interface (GUI) to simulate and predict keystrokes from brain activity. Our work demonstrates how deep learning can advance EEG-based BMI systems by addressing the challenges of signal interpretation and classification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00035v2-abstract-full').style.display = 'none'; document.getElementById('2409.00035v2-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> 27 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.18537">arXiv:2406.18537</a> <span> [<a href="https://arxiv.org/pdf/2406.18537">pdf</a>, <a href="https://arxiv.org/format/2406.18537">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="Graphics">cs.GR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Werling%2C+K">Keenon Werling</a>, <a href="/search/cs?searchtype=author&query=Kaneda%2C+J">Janelle Kaneda</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+A">Alan Tan</a>, <a href="/search/cs?searchtype=author&query=Agarwal%2C+R">Rishi Agarwal</a>, <a href="/search/cs?searchtype=author&query=Skov%2C+S">Six Skov</a>, <a href="/search/cs?searchtype=author&query=Van+Wouwe%2C+T">Tom Van Wouwe</a>, <a href="/search/cs?searchtype=author&query=Uhlrich%2C+S">Scott Uhlrich</a>, <a href="/search/cs?searchtype=author&query=Bianco%2C+N">Nicholas Bianco</a>, <a href="/search/cs?searchtype=author&query=Ong%2C+C">Carmichael Ong</a>, <a href="/search/cs?searchtype=author&query=Falisse%2C+A">Antoine Falisse</a>, <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Shardul Sapkota</a>, <a href="/search/cs?searchtype=author&query=Chandra%2C+A">Aidan Chandra</a>, <a href="/search/cs?searchtype=author&query=Carter%2C+J">Joshua Carter</a>, <a href="/search/cs?searchtype=author&query=Preatoni%2C+E">Ezio Preatoni</a>, <a href="/search/cs?searchtype=author&query=Fregly%2C+B">Benjamin Fregly</a>, <a href="/search/cs?searchtype=author&query=Hicks%2C+J">Jennifer Hicks</a>, <a href="/search/cs?searchtype=author&query=Delp%2C+S">Scott Delp</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+C+K">C. Karen Liu</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="2406.18537v1-abstract-short" style="display: inline;"> While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18537v1-abstract-full').style.display = 'inline'; document.getElementById('2406.18537v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.18537v1-abstract-full" style="display: none;"> While reconstructing human poses in 3D from inexpensive sensors has advanced significantly in recent years, quantifying the dynamics of human motion, including the muscle-generated joint torques and external forces, remains a challenge. Prior attempts to estimate physics from reconstructed human poses have been hampered by a lack of datasets with high-quality pose and force data for a variety of movements. We present the AddBiomechanics Dataset 1.0, which includes physically accurate human dynamics of 273 human subjects, over 70 hours of motion and force plate data, totaling more than 24 million frames. To construct this dataset, novel analytical methods were required, which are also reported here. We propose a benchmark for estimating human dynamics from motion using this dataset, and present several baseline results. The AddBiomechanics Dataset is publicly available at https://addbiomechanics.org/download_data.html. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.18537v1-abstract-full').style.display = 'none'; document.getElementById('2406.18537v1-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> 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">15 pages, 6 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/2405.06061">arXiv:2405.06061</a> <span> [<a href="https://arxiv.org/pdf/2405.06061">pdf</a>, <a href="https://arxiv.org/format/2405.06061">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3706598.3713819">10.1145/3706598.3713819 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> GPTCoach: Towards LLM-Based Physical Activity Coaching </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=J%C3%B6rke%2C+M">Matthew J枚rke</a>, <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Shardul Sapkota</a>, <a href="/search/cs?searchtype=author&query=Warkenthien%2C+L">Lyndsea Warkenthien</a>, <a href="/search/cs?searchtype=author&query=Vainio%2C+N">Niklas Vainio</a>, <a href="/search/cs?searchtype=author&query=Schmiedmayer%2C+P">Paul Schmiedmayer</a>, <a href="/search/cs?searchtype=author&query=Brunskill%2C+E">Emma Brunskill</a>, <a href="/search/cs?searchtype=author&query=Landay%2C+J+A">James A. Landay</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="2405.06061v2-abstract-short" style="display: inline;"> Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized support but can be prohibitively expensive and inaccessible. This study draws inspiration from health coaching to explore how large language models (LLMs) might address personalization challenges in mobile health. We con… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06061v2-abstract-full').style.display = 'inline'; document.getElementById('2405.06061v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.06061v2-abstract-full" style="display: none;"> Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized support but can be prohibitively expensive and inaccessible. This study draws inspiration from health coaching to explore how large language models (LLMs) might address personalization challenges in mobile health. We conduct formative interviews with 12 health professionals and 10 potential coaching recipients to develop design principles for an LLM-based health coach. We then built GPTCoach, a chatbot that implements the onboarding conversation from an evidence-based coaching program, uses conversational strategies from motivational interviewing, and incorporates wearable data to create personalized physical activity plans. In a lab study with 16 participants using three months of historical data, we find promising evidence that GPTCoach gathers rich qualitative information to offer personalized support, with users feeling comfortable sharing concerns. We conclude with implications for future research on LLM-based physical activity support. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06061v2-abstract-full').style.display = 'none'; document.getElementById('2405.06061v2-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> 20 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">Please note that the title has been updated from a previous pre-print (previously: "Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents")</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.18735">arXiv:2311.18735</a> <span> [<a href="https://arxiv.org/pdf/2311.18735">pdf</a>, <a href="https://arxiv.org/format/2311.18735">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> </div> </div> <p class="title is-5 mathjax"> Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2311.18735v3-abstract-short" style="display: inline;"> The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these architectures can be interpreted through the lens of a general concept of dimension mixing. Research on coupling flows and the butterfly transform shows that partial and hierarchical signal mixing schemes are sufficient… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18735v3-abstract-full').style.display = 'inline'; document.getElementById('2311.18735v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.18735v3-abstract-full" style="display: none;"> The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these architectures can be interpreted through the lens of a general concept of dimension mixing. Research on coupling flows and the butterfly transform shows that partial and hierarchical signal mixing schemes are sufficient for efficient and expressive function approximation. In this work, we study group-wise sparse, non-linear, multi-layered and learnable mixing schemes of inputs and find that they are complementary to many standard neural architectures. Following our observations and drawing inspiration from the Fast Fourier Transform, we generalize Butterfly Structure to use non-linear mixer function allowing for MLP as mixing function called Butterfly MLP. We were also able to sparsely mix along sequence dimension for Transformer-based architectures called Butterfly Attention. Experiments on CIFAR and LRA datasets demonstrate that the proposed Non-Linear Butterfly Mixers are efficient and scale well when the host architectures are used as mixing function. Additionally, we propose Patch-Only MLP-Mixer for processing spatial 2D signals demonstrating a different dimension mixing strategy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.18735v3-abstract-full').style.display = 'none'; document.getElementById('2311.18735v3-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> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </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">12 pages, 7 figures, 8 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/2310.20203">arXiv:2310.20203</a> <span> [<a href="https://arxiv.org/pdf/2310.20203">pdf</a>, <a href="https://arxiv.org/format/2310.20203">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> </div> </div> <p class="title is-5 mathjax"> Importance Estimation with Random Gradient for Neural Network Pruning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2310.20203v1-abstract-short" style="display: inline;"> Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient information or both, which demands abundant labelled examples. In this work, we use heuristics to derive importance estimation similar to Taylor First Order (TaylorFO) a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.20203v1-abstract-full').style.display = 'inline'; document.getElementById('2310.20203v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.20203v1-abstract-full" style="display: none;"> Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient information or both, which demands abundant labelled examples. In this work, we use heuristics to derive importance estimation similar to Taylor First Order (TaylorFO) approximation based methods. We name our methods TaylorFO-abs and TaylorFO-sq. We propose two additional methods to improve these importance estimation methods. Firstly, we propagate random gradients from the last layer of a network, thus avoiding the need for labelled examples. Secondly, we normalize the gradient magnitude of the last layer output before propagating, which allows all examples to contribute similarly to the importance score. Our methods with additional techniques perform better than previous methods when tested on ResNet and VGG architectures on CIFAR-100 and STL-10 datasets. Furthermore, our method also complements the existing methods and improves their performances when combined with them. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.20203v1-abstract-full').style.display = 'none'; document.getElementById('2310.20203v1-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> 31 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </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">7 pages, 2 figures, ICLR 2023 Workshop on Sparsity in Neural Networks. arXiv admin note: text overlap with arXiv:2306.13203</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.13203">arXiv:2306.13203</a> <span> [<a href="https://arxiv.org/pdf/2306.13203">pdf</a>, <a href="https://arxiv.org/format/2306.13203">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"> Neural Network Pruning for Real-time Polyp Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Poudel%2C+P">Pranav Poudel</a>, <a href="/search/cs?searchtype=author&query=Regmi%2C+S">Sudarshan Regmi</a>, <a href="/search/cs?searchtype=author&query=Panthi%2C+B">Bibek Panthi</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2306.13203v1-abstract-short" style="display: inline;"> Computer-assisted treatment has emerged as a viable application of medical imaging, owing to the efficacy of deep learning models. Real-time inference speed remains a key requirement for such applications to help medical personnel. Even though there generally exists a trade-off between performance and model size, impressive efforts have been made to retain near-original performance by compromising… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13203v1-abstract-full').style.display = 'inline'; document.getElementById('2306.13203v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.13203v1-abstract-full" style="display: none;"> Computer-assisted treatment has emerged as a viable application of medical imaging, owing to the efficacy of deep learning models. Real-time inference speed remains a key requirement for such applications to help medical personnel. Even though there generally exists a trade-off between performance and model size, impressive efforts have been made to retain near-original performance by compromising model size. Neural network pruning has emerged as an exciting area that aims to eliminate redundant parameters to make the inference faster. In this study, we show an application of neural network pruning in polyp segmentation. We compute the importance score of convolutional filters and remove the filters having the least scores, which to some value of pruning does not degrade the performance. For computing the importance score, we use the Taylor First Order (TaylorFO) approximation of the change in network output for the removal of certain filters. Specifically, we employ a gradient-normalized backpropagation for the computation of the importance score. Through experiments in the polyp datasets, we validate that our approach can significantly reduce the parameter count and FLOPs retaining similar performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.13203v1-abstract-full').style.display = 'none'; document.getElementById('2306.13203v1-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> 22 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.04467">arXiv:2207.04467</a> <span> [<a href="https://arxiv.org/pdf/2207.04467">pdf</a>, <a href="https://arxiv.org/format/2207.04467">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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2207.04467v1-abstract-short" style="display: inline;"> Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models. In this paper, we present a new Network Morphism based NAS called Noisy Heuristics NAS which uses heuristics learned from manually developing neural network models… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04467v1-abstract-full').style.display = 'inline'; document.getElementById('2207.04467v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.04467v1-abstract-full" style="display: none;"> Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models. In this paper, we present a new Network Morphism based NAS called Noisy Heuristics NAS which uses heuristics learned from manually developing neural network models and inspired by biological neuronal dynamics. Firstly, we add new neurons randomly and prune away some to select only the best fitting neurons. Secondly, we control the number of layers in the network using the relationship of hidden units to the number of input-output connections. Our method can increase or decrease the capacity or non-linearity of models online which is specified with a few meta-parameters by the user. Our method generalizes both on toy datasets and on real-world data sets such as MNIST, CIFAR-10, and CIFAR-100. The performance is comparable to the hand-engineered architecture ResNet-18 with the similar parameters. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.04467v1-abstract-full').style.display = 'none'; document.getElementById('2207.04467v1-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> 10 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">11 pages, 10 figures, DyNN workshop at the 39 th International Conference on Machine Learning, 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/2109.02929">arXiv:2109.02929</a> <span> [<a href="https://arxiv.org/pdf/2109.02929">pdf</a>, <a href="https://arxiv.org/format/2109.02929">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Brand Label Albedo Extraction of eCommerce Products using Generative Adversarial Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Juneja%2C+M">Manish Juneja</a>, <a href="/search/cs?searchtype=author&query=Keleras%2C+L">Laurynas Keleras</a>, <a href="/search/cs?searchtype=author&query=Kotwal%2C+P">Pranav Kotwal</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2109.02929v2-abstract-short" style="display: inline;"> In this paper we present our solution to extract albedo of branded labels for e-commerce products. To this end, we generate a large-scale photo-realistic synthetic data set for albedo extraction followed by training a generative model to translate images with diverse lighting conditions to albedo. We performed an extensive evaluation to test the generalisation of our method to in-the-wild images.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.02929v2-abstract-full').style.display = 'inline'; document.getElementById('2109.02929v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.02929v2-abstract-full" style="display: none;"> In this paper we present our solution to extract albedo of branded labels for e-commerce products. To this end, we generate a large-scale photo-realistic synthetic data set for albedo extraction followed by training a generative model to translate images with diverse lighting conditions to albedo. We performed an extensive evaluation to test the generalisation of our method to in-the-wild images. From the experimental results, we observe that our solution generalises well compared to the existing method both in the unseen rendered images as well as in the wild image. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.02929v2-abstract-full').style.display = 'none'; document.getElementById('2109.02929v2-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> 7 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">5 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.08748">arXiv:2106.08748</a> <span> [<a href="https://arxiv.org/pdf/2106.08748">pdf</a>, <a href="https://arxiv.org/format/2106.08748">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="Neural and Evolutionary Computing">cs.NE</span> </div> </div> <p class="title is-5 mathjax"> Input Invex Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</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="2106.08748v4-abstract-short" style="display: inline;"> Connected decision boundaries are useful in several tasks like image segmentation, clustering, alpha-shape or defining a region in nD-space. However, the machine learning literature lacks methods for generating connected decision boundaries using neural networks. Thresholding an invex function, a generalization of a convex function, generates such decision boundaries. This paper presents two metho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.08748v4-abstract-full').style.display = 'inline'; document.getElementById('2106.08748v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.08748v4-abstract-full" style="display: none;"> Connected decision boundaries are useful in several tasks like image segmentation, clustering, alpha-shape or defining a region in nD-space. However, the machine learning literature lacks methods for generating connected decision boundaries using neural networks. Thresholding an invex function, a generalization of a convex function, generates such decision boundaries. This paper presents two methods for constructing invex functions using neural networks. The first approach is based on constraining a neural network with Gradient Clipped-Gradient Penality (GCGP), where we clip and penalise the gradients. In contrast, the second one is based on the relationship of the invex function to the composition of invertible and convex functions. We employ connectedness as a basic interpretation method and create connected region-based classifiers. We show that multiple connected set based classifiers can approximate any classification function. In the experiments section, we use our methods for classification tasks using an ensemble of 1-vs-all models as well as using a single multiclass model on small-scale datasets. The experiments show that connected set-based classifiers do not pose any disadvantage over ordinary neural network classifiers, but rather, enhance their interpretability. We also did an extensive study on the properties of invex function and connected sets for interpretability and network morphism with experiments on toy and real-world data sets. Our study suggests that invex function is fundamental to understanding and applying locality and connectedness of input space which is useful for various downstream tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.08748v4-abstract-full').style.display = 'none'; document.getElementById('2106.08748v4-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> 3 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 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">45 pages, 23 figures, 10 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/2105.05501">arXiv:2105.05501</a> <span> [<a href="https://arxiv.org/pdf/2105.05501">pdf</a>, <a href="https://arxiv.org/format/2105.05501">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"> Label Geometry Aware Discriminator for Conditional Generative Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sapkota%2C+S">Suman Sapkota</a>, <a href="/search/cs?searchtype=author&query=Khanal%2C+B">Bidur Khanal</a>, <a href="/search/cs?searchtype=author&query=Bhattarai%2C+B">Binod Bhattarai</a>, <a href="/search/cs?searchtype=author&query=Khanal%2C+B">Bishesh Khanal</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+T">Tae-Kyun Kim</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="2105.05501v1-abstract-short" style="display: inline;"> Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification. Improving downstream tasks with synthetic examples requires generating images with high fidelity to the unk… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.05501v1-abstract-full').style.display = 'inline'; document.getElementById('2105.05501v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.05501v1-abstract-full" style="display: none;"> Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification. Improving downstream tasks with synthetic examples requires generating images with high fidelity to the unknown conditional distribution of the target class, which many labeled conditional GANs attempt to achieve by adding soft-max cross-entropy loss based auxiliary classifier in the discriminator. As recent studies suggest that the soft-max loss in Euclidean space of deep feature does not leverage their intrinsic angular distribution, we propose to replace this loss in auxiliary classifier with an additive angular margin (AAM) loss that takes benefit of the intrinsic angular distribution, and promotes intra-class compactness and inter-class separation to help generator synthesize high fidelity images. We validate our method on RaFD and CIFAR-100, two challenging face expression and natural image classification data set. Our method outperforms state-of-the-art methods in several different evaluation criteria including recently proposed GAN-train and GAN-test metrics designed to assess the impact of synthetic data on downstream classification task, assessing the usefulness in data augmentation for supervised tasks with prediction accuracy score and average confidence score, and the well known FID metric. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.05501v1-abstract-full').style.display = 'none'; document.getElementById('2105.05501v1-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> 12 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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