Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Pennsylvania State UniversityUS558 papers
- University of Maryland, College ParkUS521 papers
- Massachusetts Institute of TechnologyUS457 papers
- University of California, BerkeleyUS447 papers
- Centre National de la Recherche ScientifiqueFR442 papers
- Columbia UniversityUS374 papers
- Université Libre de BruxellesBE370 papers
- The University of AdelaideAU343 papers
- California Institute of TechnologyUS337 papers
- Goddard Space Flight CenterUS327 papers
- University of Wisconsin–MadisonUS321 papers
- University of DelawareUS313 papers
456 papers · 1 filter
A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection
Davood Fattahi, Runze Yan, Saurabh Kataria +2
Objective: Accurate classification of physiological signals in real-world deployments is challenged by sensor noise, motion artifacts, and distribution shifts between training and…
pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Zhuodong Liu, Xiangyu Li, Zhihao Zhang
Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regu…
ScaleMAP: Preserving Local Density and Neighborhood Structure in Low-Dimensional Embeddings
Rajas Poorna, Marcus T. Cicerone
Nonlinear dimensionality-reduction methods such as UMAP and PaCMAP adaptively normalize local distances during graph construction, erasing neighborhood scale from the data. This di…
Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search
Pranav Somu, Advay Balakrishnan, Stepan Kravtsov +2
Developing effective surrogates (performance predictors) for Neural Architecture Search (NAS) typically requires expensive fine-tuning or the engineering of complex representations…
A Few-Step Generative Model on Cumulative Flow Maps
Zhiqi Li, Duowen Chen, Yuchen Sun +1
We propose a unified, few-step generative modeling framework based on \emph{cumulative flow maps} for long-range transport in probability space, inspired by flow-map techniques for…
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
Gyujun Jeong, Sungwon Cho, Minji Shon +13
Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages…
KindSleep: Knowledge-Informed Diagnosis of Obstructive Sleep Apnea from Oximetry
Micky C Nnamdi, Wenqi Shi, Cheng Wan +4
Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. Traditional diagnosis through poly…
A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
Prithwijit Chowdhury, Ahmad Mustafa, Mohit Prabhushankar +1
In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based cl…
Robust Multimodal Representation Learning in Healthcare
Xiaoguang Zhu, Linxiao Gong, Lianlong Sun +3
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world med…
Report for NSF Workshop on AI for Electronic Design Automation
Deming Chen, Vijay Ganesh, Weikai Li +7
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIP…
Deep Reinforcement Learning for Multi-Agent Coordination
Kehinde O. Aina, Sehoon Ha
We address the challenge of coordinating multiple robots in narrow and confined environments, where congestion and interference often hinder collective task performance. Drawing in…
Detection of Chagas Disease from the ECG: The George B. Moody PhysioNet Challenge 2025
Matthew A. Reyna, Zuzana Koscova, Jan Pavlus +11
Objective: Chagas disease is a parasitic infection that is endemic to South America, Central America, and, more recently, the U.S., primarily transmitted by insects. Chronic Chagas…
CSI-4CAST: A Hybrid Deep Learning Model for CSI Prediction with Comprehensive Robustness and Generalization Testing
Sikai Cheng, Reza Zandehshahvar, Haoruo Zhao +4
Channel state information (CSI) prediction is a promising strategy for ensuring reliable and efficient operation of massive multiple-input multiple-output (mMIMO) systems by provid…
Causal Machine Learning for Surgical Interventions
J. Ben Tamo, Nishant S. Chouhan, Micky C. Nnamdi +6
Surgical decision-making is complex and requires understanding causal relationships between patient characteristics, interventions, and outcomes. In high-stakes settings like spina…
Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training
Wesley Brewer, Murali Meena Gopalakrishnan, Matthias Maiterth +12
With the end of Moore's law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intell…
Interpretable Credit Default Prediction with Ensemble Learning and SHAP
Shiqi Yang, Ziyi Huang, Wengran Xiao +1
This study focuses on the problem of credit default prediction, builds a modeling framework based on machine learning, and conducts comparative experiments on a variety of mainstre…
EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
Arnab Sanyal, Gourav Datta, Prithwish Mukherjee +2
Large Language Models (LLMs) achieve strong performance across tasks, but face storage and compute challenges on edge devices. We propose EntroLLM, a compression framework combinin…
Power-scaled Bayesian Inference with Score-based Generative Models
Huseyin Tuna Erdinc, Yunlin Zeng, Abhinav Prakash Gahlot +1
We propose a score-based generative algorithm for sampling from power-scaled priors and likelihoods within the Bayesian inference framework. Our algorithm enables flexible control…
Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation
Srihas Yarlagadda, Amey Agrawal, Elton Pinto +6
Training large foundation models costs hundreds of millions of dollars, making deployment optimization critical. Current approaches require machine learning engineers to manually c…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report
Rachael Fleurence, Xiaoyan Wang, Jiang Bian +5
Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outl…
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
Yuchen Cao, Jianglai Dai, Zhongyan Wang +4
The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through use…
Data Poisoning and Leakage Analysis in Federated Learning
Wenqi Wei, Tiansheng Huang, Zachary Yahn +3
Data poisoning and leakage risks impede the massive deployment of federated learning in the real world. This chapter reveals the truths and pitfalls of understanding two dominating…
Peer-to-Peer Learning Dynamics of Wide Neural Networks
Shreyas Chaudhari, Srinivasa Pranav, Emile Anand +1
Peer-to-peer learning is an increasingly popular framework that enables beyond-5G distributed edge devices to collaboratively train deep neural networks in a privacy-preserving man…