ICLR 2023

Poster

Continual Transformers: Redundancy-Free Attention for Online Inference
Lukas Hedegaard · Arian Bakhtiarnia · Alexandros Iosifidis

Poster

Mon 2:30

Denoising Diffusion Samplers
Francisco Vargas · Will Grathwohl · Arnaud Doucet

Poster

Real-time variational method for learning neural trajectory and its dynamics
Matthew Dowling · Yuan Zhao · Il Memming Park

Poster

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent
Tobias Pielok · Bernd Bischl · David Rügamer

Poster

Tue 2:30

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
Nicholas Gao · Stephan Günnemann

Poster

Wed 2:30

Calibrating Transformers via Sparse Gaussian Processes
Wenlong Chen · Yingzhen Li

Poster

Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo · Philip Greengard · Hongyi Wang · Andrew Gelman · Yoon Kim · Eric Xing

Poster

Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks
Cheng Zhang

Poster

Semi-Implicit Variational Inference via Score Matching
Longlin Yu · Cheng Zhang

Poster

Tue 7:30

Energy-Based Test Sample Adaptation for Domain Generalization
Zehao Xiao · Xiantong Zhen · Shengcai Liao · Cees G Snoek

Poster

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
Raghav Singhal · Mark Goldstein · Rajesh Ranganath

Poster

Deep Variational Implicit Processes
Luis A. Ortega · Simon Rodriguez · Daniel Hernández-Lobato

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