4.7k citations · 5.1k across the 10 of their papers we have counts for
10 papers
Context-Aware Legal Citation Recommendation using Deep Learning
Zihan Huang, Charles Low, Mengqiu Teng +4
Lawyers and judges spend a large amount of time researching the proper legal authority to cite while drafting decisions. In this paper, we develop a citation recommendation tool th…
One Backward from Ten Forward, Subsampling for Large-Scale Deep Learning
Chaosheng Dong, Xiaojie Jin, Weihao Gao +5
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments. The sheer volume of streaming training…
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang +5
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the pri…
Fixup Initialization: Residual Learning Without Normalization
Hongyi Zhang, Yann N. Dauphin, Tengyu Ma
Normalization layers are a staple in state-of-the-art deep neural network architectures. They are widely believed to stabilize training, enable higher learning rate, accelerate con…
R-SPIDER: A Fast Riemannian Stochastic Optimization Algorithm with Curvature Independent Rate
Jingzhao Zhang, Hongyi Zhang, Suvrit Sra
We study smooth stochastic optimization problems on Riemannian manifolds. Via adapting the recently proposed SPIDER algorithm \citep{fang2018spider} (a variance reduced stochastic…
Towards Riemannian Accelerated Gradient Methods
Hongyi Zhang, Suvrit Sra
We propose a Riemannian version of Nesterov's Accelerated Gradient algorithm (RAGD), and show that for geodesically smooth and strongly convex problems, within a neighborhood of th…
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin +1
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple le…
Matrix Completion from Samples in Linear Time
David Gamarnik, Quan Li, Hongyi Zhang
We consider the problem of reconstructing a rank- matrix from a sampling of its entries. Under a certain incoherence assumption on and for the case when bot…
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
Hongyi Zhang, Sashank J. Reddi, Suvrit Sra
We study optimization of finite sums of geodesically smooth functions on Riemannian manifolds. Although variance reduction techniques for optimizing finite-sums have witnessed trem…
First-order Methods for Geodesically Convex Optimization
Hongyi Zhang, Suvrit Sra
Geodesic convexity generalizes the notion of (vector space) convexity to nonlinear metric spaces. But unlike convex optimization, geodesically convex (g-convex) optimization is muc…