Papers
Dissecting Neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park et al.
Learning discrete distributions with infinite support
Doron Cohen, Aryeh Kontorovich, Geoffrey Wolfer
Learning About Objects by Learning to Interact with Them
Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi et al.
An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan et al.
OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification
Taewon Jeong, Heeyoung Kim
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
Dennis Wei, Tian Gao, Yue Yu
On Warm-Starting Neural Network Training
Jordan Ash, Ryan P. Adams
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching
Zeping Yu, Wenxin Zheng, Jiaqi Wang et al.
Adaptive Discretization for Model-Based Reinforcement Learning
Sean Sinclair, Tianyu Wang, Gauri Jain et al.
Unsupervised Sound Separation Using Mixture Invariant Training
Scott Wisdom, Efthymios Tzinis, Hakan Erdogan et al.
Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin et al.
Submodular Meta-Learning
Arman Adibi, Aryan Mokhtari, Hamed Hassani
How to Characterize The Landscape of Overparameterized Convolutional Neural Networks
Yihong Gu, Weizhong Zhang, Cong Fang et al.
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Adil Salim, Peter Richtarik
All Word Embeddings from One Embedding
Sho Takase, Sosuke Kobayashi
Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration
Cédric Colas, Tristan Karch, Nicolas Lair et al.
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo
ZHIYAN DING, Qin Li
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi, Xuankai Chang, Pengcheng Guo et al.
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Didrik Nielsen, Ole Winther
Distribution-free binary classification: prediction sets, confidence intervals and calibration
Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas
On ranking via sorting by estimated expected utility
Clement Calauzenes, Nicolas Usunier
Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan et al.
Bidirectional Convolutional Poisson Gamma Dynamical Systems
wenchao chen, Chaojie Wang, Bo Chen et al.
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Max Ryabinin, Anton Gusev