Papers
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models
Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee et al.
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
Huaiyu Li, Weiming Dong, Xing Mei et al.
Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations
Kubilay Atasu, Thomas Mittelholzer
Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song et al.
LIT: Learned Intermediate Representation Training for Model Compression
Animesh Koratana, Daniel Kang, Peter Bailis et al.
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield et al.
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation
Tahrima Rahman, Shasha Jin, Vibhav Gogate
Lorentzian Distance Learning for Hyperbolic Representations
Marc Law, Renjie Liao, Jake Snell et al.
Loss Landscapes of Regularized Linear Autoencoders
Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva et al.
Lossless or Quantized Boosting with Integer Arithmetic
Richard Nock, Robert Williamson
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou, Quanquan Gu
Low Latency Privacy Preserving Inference
Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
Brian Trippe, Jonathan Huggins, Raj Agrawal et al.
Making Convolutional Networks Shift-Invariant Again
Richard Zhang
Making Decisions that Reduce Discriminatory Impacts
Matt Kusner, Chris Russell, Joshua Loftus et al.
Making Deep Q-learning methods robust to time discretization
Corentin Tallec, Léonard Blier, Yann Ollivier
Manifold Mixup: Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham et al.
MASS: Masked Sequence to Sequence Pre-training for Language Generation
Kaitao Song, Xu Tan, Tao Qin et al.
Matrix-Free Preconditioning in Online Learning
Ashok Cutkosky, Tamas Sarlos
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao, Xudong Sun, Volker Tresp
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant et al.
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Albert Gural, Boris Murmann