Papers
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
Shuhuai Ren, Aston Zhang, Yi Zhu et al.
PromptRestorer: A Prompting Image Restoration Method with Degradation Perception
Cong Wang, Jinshan Pan, Wei Wang et al.
Propagating Knowledge Updates to LMs Through Distillation
Shankar Padmanabhan, Yasumasa Onoe, Michael Zhang et al.
ProPILE: Probing Privacy Leakage in Large Language Models
Siwon Kim, Sangdoo Yun, Hwaran Lee et al.
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization
Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey et al.
Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan Frey et al.
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
Pascal Notin, Aaron Kollasch, Daniel Ritter et al.
ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics
Zhangyang Gao, Cheng Tan, Yijie Zhang et al.
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
Pascal Notin, Ruben Weitzman, Debora Marks et al.
ProteinShake: Building datasets and benchmarks for deep learning on protein structures
Tim Kucera, Carlos Oliver, Dexiong Chen et al.
PROTES: Probabilistic Optimization with Tensor Sampling
Anastasiia Batsheva, Andrei Chertkov, Gleb Ryzhakov et al.
ProtoDiff: Learning to Learn Prototypical Networks by Task-Guided Diffusion
Yingjun Du, Zehao Xiao, Shengcai Liao et al.
Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval
Hao Li, Jingkuan Song, Lianli Gao et al.
Prototypical Variational Autoencoder for 3D Few-shot Object Detection
Weiliang Tang, Biqi YANG, Xianzhi Li et al.
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
Emmanuel Abbe, Elisabetta Cornacchia, Aryo Lotfi
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
Jan Schuchardt, Yan Scholten, Stephan Günnemann
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
Omar Chehab, Aapo Hyvarinen, Andrej Risteski
Provable benefits of score matching
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari et al.
Provable convergence guarantees for black-box variational inference
Justin Domke, Robert Gower, Guillaume Garrigos
Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior
Adam Block, Ali Jadbabaie, Daniel Pfrommer et al.
Provable Guarantees for Neural Networks via Gradient Feature Learning
Zhenmei Shi, Junyi Wei, Yingyu Liang
Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
Eshaan Nichani, Alex Damian, Jason Lee
Provable Training for Graph Contrastive Learning
Yue Yu, Xiao Wang, Mengmei Zhang et al.
Provably Bounding Neural Network Preimages
Suhas Kotha, Christopher Brix, J. Zico Kolter et al.
Provably Efficient Algorithm for Nonstationary Low-Rank MDPs
Yuan Cheng, Jing Yang, Yingbin Liang