353 citations · 379 across the 21 of their papers we have counts for
6 papers · 1 filter
Diffusion Models With Learned Adaptive Noise
Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa +1
Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images. Central to these algorithms is the diffusion process, a set of equations which ma…
Coneheads: Hierarchy Aware Attention
Albert Tseng, Tao Yu, Toni J. B. Liu +1
Attention networks such as transformers have achieved state-of-the-art performance in many domains. These networks rely heavily on the dot product attention operator, which compute…
Shadow Cones: A Generalized Framework for Partial Order Embeddings
Tao Yu, Toni J. B. Liu, Albert Tseng +1
Hyperbolic space has proven to be well-suited for capturing hierarchical relations in data, such as trees and directed acyclic graphs. Prior work introduced the concept of entailme…
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
Yucheng Lu, Shivani Agrawal, Suvinay Subramanian +3
Recent innovations on hardware (e.g. Nvidia A100) have motivated learning N:M structured sparsity masks from scratch for fast model inference. However, state-of-the-art learning re…
Coordinating Distributed Example Orders for Provably Accelerated Training
A. Feder Cooper, Wentao Guo, Khiem Pham +4
Recent research on online Gradient Balancing (GraB) has revealed that there exist permutation-based example orderings for SGD that are guaranteed to outperform random reshuffling (…
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi +6
Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification. In practice, the variance on some data examp…