190 citations · 1.3k across the 44 of their papers we have counts for
47 papers
Purify Before You Align: Improving Representation Alignment for Diffusion Models
Yingheng Wang, Yaoqiang Li, Yaqin Wu +4
Representation alignment improves and accelerates diffusion training by encouraging intermediate features to match a pretrained visual encoder. However, standard alignment matches…
VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation
Katherine J. Koebel, Renata Ivanek, Parminder Basran +33
VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through st…
Stochastic Optimization Under Power-Law Spectra: Tight Bounds and Shuffling Analysis
Thomas Dybdahl Ahle, Yaroslav Bulatov, Christopher De Sa +1
Recent work has established that power-law spectral conditions on data enable tight convergence bounds for deterministic gradient descent, resolving the conflict between classical…
Structured Pruning is All You Need for Pruning CNNs at Initialization
Yaohui Cai, Weizhe Hua, Hongzheng Chen +3
Pruning is a popular technique for reducing the model size and computational cost of convolutional neural networks (CNNs). However, a slow retraining or fine-tuning procedure is of…
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam
Yucheng Lu, Conglong Li, Minjia Zhang +2
1-bit gradient compression and local steps are two representative techniques that enable drastic communication reduction in distributed SGD. Their benefits, however, remain an open…
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning
Tao Yu, Yichi Zhang, Zhiru Zhang +1
Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. It is attractive because of its energy efficiency and low late…