Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Lawrence Berkeley National LaboratoryUS1.9k papers
- Argonne National LaboratoryUS1.9k papers
- University of ChicagoUS1.9k papers
- University of PittsburghUS1.8k papers
- The Ohio State UniversityUS1.8k papers
- University of MichiganUS1.8k papers
- University of GenevaCH1.7k papers
- University of OxfordGB1.7k papers
- Yale UniversityUS1.7k papers
- Centre National de la Recherche ScientifiqueFR1.7k papers
- Michigan State UniversityUS1.7k papers
- University College LondonGB1.7k papers
229 papers · 1 filter
Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
Yuan Shan
Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subgroups remains poorly underst…
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Yuzhe Fu, Hancheng Ye, Cong Guo +7
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces…
Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion
Abdul Jabbar, Ethan Grooby, Yang Yi Poh +5
Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide. Echocardiography, the gold-standard diagnostic method, is costl…
Resolving Predictive Multiplicity for the Rashomon Set
Parian Haghighat, Hadis Anahideh, Cynthia Rudin
The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a Rashomon set of models achieve similar accuracy but diverge…
RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty +3
Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce…
OASIS: Outlier-Aware LUT-Based GEMM with Dual-Side Quantization for LLM Inference Acceleration
Xueying Wu, Baijun Zhou, Zhihui Gao +4
Large language models (LLMs) have demonstrated impressive capabilities across a wide range of applications, but demand substantial memory and compute resources during inference. Ex…
Detecting immune cells with label-free two-photon autofluorescence and deep learning
Lucas Kreiss, Amey Chaware, Maryam Roohian +7
Label-free imaging has gained broad interest because of its potential to omit elaborate staining procedures which is especially relevant for in vivo use. Label-free multiphoton mic…
Learning Survival Distributions with the Asymmetric Laplace Distribution
Deming Sheng, Ricardo Henao
Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have p…
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery
Minh-Quyet Ha, Dinh-Khiet Le, Duc-Anh Dao +6
Discovering novel high-entropy alloys (HEAs) with desirable properties is challenging due to the vast compositional space and complex phase formation mechanisms. Efficient explorat…
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…
Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks
Rajiv Teja Nagipogu, John H. Reif
Molecular circuits capable of autonomous learning could unlock novel applications in fields such as bioengineering and synthetic biology. To this end, existing chemical implementat…
Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
Siqi Li, Xin Li, Kunyu Yu +12
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effecti…
Structured and Balanced Multi-Component and Multi-Layer Neural Networks
Shijun Zhang, Hongkai Zhao, Yimin Zhong +1
In this work, we propose a balanced multi-component and multi-layer neural network (MMNN) structure to accurately and efficiently approximate functions with complex features, in te…
Optimal Sparse Survival Trees
Rui Zhang, Rui Xin, Margo Seltzer +1
Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve huma…
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Siqi Li, Di Miao, Qiming Wu +9
Federated learning (FL) has shown promising potential in safeguarding data privacy in healthcare collaborations. While the term "FL" was originally coined by the engineering commun…
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova +4
Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (…
A Path to Simpler Models Starts With Noise
Lesia Semenova, Harry Chen, Ronald Parr +1
The Rashomon set is the set of models that perform approximately equally well on a given dataset, and the Rashomon ratio is the fraction of all models in a given hypothesis space t…
Why Shallow Networks Struggle to Approximate and Learn High Frequencies
Shijun Zhang, Hongkai Zhao, Yimin Zhong +1
In this work, we present a comprehensive study combining mathematical and computational analysis to explain why a two-layer neural network struggles to handle high frequencies in b…
An Effective Meaningful Way to Evaluate Survival Models
Shi-ang Qi, Neeraj Kumar, Mahtab Farrokh +5
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by…
Machine learning with tree tensor networks, CP rank constraints, and tensor dropout
Hao Chen, Thomas Barthel
Tensor networks developed in the context of condensed matter physics try to approximate order- tensors with a reduced number of degrees of freedom that is only polynomial in …
Federated and distributed learning applications for electronic health records and structured medical data: A scoping review
Siqi Li, Pinyan Liu, Gustavo G. Nascimento +12
Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms…
Globalizing Fairness Attributes in Machine Learning: A Case Study on Health in Africa
Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong +3
With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness…
OKRidge: Scalable Optimal k-Sparse Ridge Regression
Jiachang Liu, Sam Rosen, Chudi Zhong +1
We consider an important problem in scientific discovery, namely identifying sparse governing equations for nonlinear dynamical systems. This involves solving sparse ridge regressi…
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
Chudi Zhong, Zhi Chen, Jiachang Liu +2
In real applications, interaction between machine learning models and domain experts is critical; however, the classical machine learning paradigm that usually produces only a sing…