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20232026
most citedThe FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input

3 citations · 13 across the 13 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

See it to Place it: Evolving Macro Placements with Vision-Language Models

Ikechukwu Uchendu, Swati Goel, Karly Hou +5

We propose using Vision-Language Models (VLMs) for macro placement in chip floorplanning, a complex optimization task that has recently shown promising advancements through machine…

cs.LG2026

EUGens: Efficient, Unified, and General Dense Layers

Sang Min Kim, Byeongchan Kim, Arijit Sehanobish +7

Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFL…

cs.LG2025

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates

Jincheng Mei, Bo Dai, Alekh Agarwal +4

We provide a new understanding of the stochastic gradient bandit algorithm by showing that it converges to a globally optimal policy almost surely using \emph{any} constant learnin…

cs.LG2025★ 2 cited

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions

Taedong Yun, Eric Yang, Mustafa Safdari +13

We present an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle…

cs.LG2024

Verified Neural Compressed Sensing

Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar +2

We develop the first (to the best of our knowledge) provably correct neural networks for a precise computational task, with the proof of correctness generated by an automated verif…

cs.LG2023

Latent Space Representations of Neural Algorithmic Reasoners

Vladimir V. Mirjanić, Razvan Pascanu, Petar Veličković

Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algo…