Papers
Meta-learning from Tasks with Heterogeneous Attribute Spaces
Tomoharu Iwata, Atsutoshi Kumagai
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway et al.
Learning Physical Graph Representations from Visual Scenes
Daniel Bear, Chaofei Fan, Damian Mrowca et al.
Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?
Qiwen Cui, Lin Yang
Differentially-Private Federated Linear Bandits
Abhimanyu Dubey, Alex `Sandy' Pentland
Revisiting Parameter Sharing for Automatic Neural Channel Number Search
Jiaxing Wang, Haoli Bai, Jiaxiang Wu et al.
NeuMiss networks: differentiable programming for supervised learning with missing values.
Marine Le Morvan, Julie Josse, Thomas Moreau et al.
Model Agnostic Multilevel Explanations
Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang et al.
Random Reshuffling is Not Always Better
Christopher M De Sa
GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification
John T Halloran, David M Rocke
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noe
Neuron Shapley: Discovering the Responsible Neurons
Amirata Ghorbani, James Y Zou
On Second Order Behaviour in Augmented Neural ODEs
Alexander Norcliffe, Cristian Bodnar, Ben Day et al.
Evaluating Attribution for Graph Neural Networks
Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee et al.
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
Junsouk Choi, Robert Chapkin, Yang Ni
Stochastic Deep Gaussian Processes over Graphs
Naiqi Li, Wenjie Li, Jifeng Sun et al.
Graph Meta Learning via Local Subgraphs
Kexin Huang, Marinka Zitnik
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul et al.
Bayesian Probabilistic Numerical Integration with Tree-Based Models
Harrison Zhu, Xing Liu, Ruya Kang et al.
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta et al.
Graph Contrastive Learning with Augmentations
Yuning You, Tianlong Chen, Yongduo Sui et al.
Woodbury Transformations for Deep Generative Flows
You Lu, Bert Huang
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization
Yan Yan, Yi Xu, Qihang Lin et al.
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Wenhui Wang, Furu Wei, Li Dong et al.
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen, Vaden Masrani, Rob Brekelmans et al.