activity
20172024
most citedQuality of Uncertainty Quantification for Bayesian Neural Network Inference

73 citations · 129 across the 10 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

cs.LG2022

Deep Semi-supervised Learning with Double-Contrast of Features and Semantics

Quan Feng, Jiayu Yao, Zhison Pan +1

In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. Howeve…

stat.ML2022

An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks

Jiayu Yao, Yaniv Yacoby, Beau Coker +2

Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in t…

cs.LG2022

Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry

Mark Penrod, Harrison Termotto, Varshini Reddy +3

For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictiv…

cs.LG2022

Policy Optimization with Sparse Global Contrastive Explanations

Jiayu Yao, Sonali Parbhoo, Weiwei Pan +1

We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaini…

cs.LG2022

Learning Downstream Task by Selectively Capturing Complementary Knowledge from Multiple Self-supervisedly Learning Pretexts

Jiayu Yao, Qingyuan Wu, Quan Feng +1

Self-supervised learning (SSL), as a newly emerging unsupervised representation learning paradigm, generally follows a two-stage learning pipeline: 1) learning invariant and discri…