Papers
Pooling by Sliced-Wasserstein Embedding
Navid Naderializadeh, Joseph F Comer, Reed Andrews et al.
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
Yi Ren, Jinglin Liu, Zhou Zhao
Post-Contextual-Bandit Inference
Aurelien Bibaut, Maria Dimakopoulou, Nathan Kallus et al.
Posterior Collapse and Latent Variable Non-identifiability
Yixin Wang, David M. Blei, John P. Cunningham
Posterior Meta-Replay for Continual Learning
Christian Henning, Maria Cervera, Francesco D'Angelo et al.
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun et al.
Post-Training Quantization for Vision Transformer
Zhenhua Liu, Yunhe Wang, Kai Han et al.
Post-Training Sparsity-Aware Quantization
Gil Shomron, Freddy Gabbay, Samer Kurzum et al.
Powerpropagation: A sparsity inducing weight reparameterisation
Jonathan Schwarz, Siddhant Jayakumar, Razvan Pascanu et al.
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
David Applegate, Mateo Diaz, Oliver Hinder et al.
Practical Near Neighbor Search via Group Testing
Joshua Engels, Benjamin Coleman, Anshumali Shrivastava
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers
Julian Katz-Samuels, Blake Mason, Kevin G. Jamieson et al.
Pragmatic Image Compression for Human-in-the-Loop Decision-Making
Sid Reddy, Anca Dragan, Sergey Levine
Precise characterization of the prior predictive distribution of deep ReLU networks
Lorenzo Noci, Gregor Bachmann, Kevin Roth et al.
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization
Jialun Zhang, Salar Fattahi, Richard Y Zhang
Predicting Deep Neural Network Generalization with Perturbation Response Curves
Yair Schiff, Brian Quanz, Payel Das et al.
Predicting Event Memorability from Contextual Visual Semantics
Qianli Xu, Fen Fang, Ana Molino et al.
Predicting Molecular Conformation via Dynamic Graph Score Matching
Shitong Luo, Chence Shi, Minkai Xu et al.
Predicting What You Already Know Helps: Provable Self-Supervised Learning
Jason Lee, Qi Lei, Nikunj Saunshi et al.
Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics
Bhavin Choksi, Milad Mozafari, Callum Biggs O'May et al.
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
Neehar Peri, Michael Curry, Samuel Dooley et al.
Preserved central model for faster bidirectional compression in distributed settings
Constantin Philippenko, Aymeric Dieuleveut
Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch et al.
Prior-independent Dynamic Auctions for a Value-maximizing Buyer
Yuan Deng, Hanrui Zhang
Private and Non-private Uniformity Testing for Ranking Data
Róbert Busa-Fekete, Dimitris Fotakis, Emmanouil Zampetakis