10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS735 papers
- Massachusetts Institute of TechnologyUS455 papers
- University of OxfordGB379 papers
- University of ChicagoUS368 papers
- University of MichiganUS356 papers
- Lawrence Berkeley National LaboratoryUS354 papers
- The Ohio State UniversityUS350 papers
- Yale UniversityUS348 papers
- University of PittsburghUS317 papers
- Argonne National LaboratoryUS316 papers
- University College LondonGB312 papers
- University of PisaIT310 papers
6 papers · 2 filters
Adaptive EEG-based stroke diagnosis with a GRU-TCN classifier and deep Q-learning thresholding
Shakeel Abdulkareem, Bora Yimenicioglu, Khartik Uppalapati +3
Rapid triage of suspected stroke needs accurate, bedside-deployable tools; EEG is promising but underused at first contact. We present an adaptive multitask EEG classifier that con…
Analyzing Political Text at Scale with Online Tensor LDA
Sara Kangaslahti, Danny Ebanks, Jean Kossaifi +3
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Di…
Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
Lothar Heimbach, Sebastian Kaltenbach, Petr Karnakov +2
Partial Differential Equations (PDEs) describe phenomena ranging from turbulence and epidemics to quantum mechanics and financial markets. Despite recent advances in computational…
A Preliminary Framework for Intersectionality in ML Pipelines
Michelle Nashla Turcios, Alicia E. Boyd, Angela D. R. Smith +1
Machine learning (ML) has become a go-to solution for improving how we use, experience, and interact with technology (and the world around us). Unfortunately, studies have repeated…
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…
A ghost mechanism: An analytical model of abrupt learning in recurrent networks
Fatih Dinc, Ege Cirakman, Bariscan Kurtkaya +4
Abrupt learning is a common phenomenon in recurrent neural networks (RNNs) trained on working memory tasks. In such cases, the networks develop transient slow regions in state spac…