Papers
11,951 papers found
HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts
Mengqi Liao, Wei Chen, Junfeng Shen et al.
Holistically Evaluating the Environmental Impact of Creating Language Models
Jacob Morrison, Clara Na, Jared Fernandez et al.
Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
Seiji Maekawa, Hayate Iso, Nikita Bhutani
Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings
Beatrice Bevilacqua, Joshua Robinson, Jure Leskovec et al.
Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao, Emily Jin, Michael M. Bronstein et al.
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
Jingchu Gai, Yiheng Du, Bohang Zhang et al.
HOPE for a Robust Parameterization of Long-memory State Space Models
Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
Horizon Generalization in Reinforcement Learning
Vivek Myers, Catherine Ji, Benjamin Eysenbach
Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection
Lei Shen, Zhenheng Tang, Lijun Wu et al.
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Jiahan Li, Tong Chen, Shitong Luo et al.
How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff et al.
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning
Arthur Jacot, Seok Hoan Choi, Yuxiao Wen
How Does Critical Batch Size Scale in Pre-training?
Hanlin Zhang, Depen Morwani, Nikhil Vyas et al.
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Seongyun Lee, Geewook Kim, Jiyeon Kim et al.
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
Xinnan Dai, Haohao Qu, Yifei Shen et al.
How efficient is LLM-generated code? A rigorous & high-standard benchmark
Ruizhong Qiu, Weiliang Will Zeng, James Ezick et al.
How Far Are We from True Unlearnability?
Kai Ye, Liangcai Su, Chenxiong Qian
How Feature Learning Can Improve Neural Scaling Laws
Blake Bordelon, Alexander Atanasov, Cengiz Pehlevan
How Gradient descent balances features: A dynamical analysis for two-layer neural networks
Zhenyu Zhu, Fanghui Liu, Volkan Cevher
How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings
Samuel Audia, Soheil Feizi, Matthias Zwicker et al.
How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings
Nikolaos Nakis, Niels Raunkjær Holm, Andreas Lyhne Fiehn et al.
How many samples are needed to train a deep neural network?
Pegah Golestaneh, Mahsa Taheri, Johannes Lederer
How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion.
Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos Costis Daskalakis
How Much is Unseen Depends Chiefly on Information About the Seen
Seongmin Lee, Marcel Boehme
How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning
Yao Tong, Jiayuan Ye, Sajjad Zarifzadeh et al.