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08/29/2023
Heterogeneous Multi-Task Gaussian Cox Processes
This paper presents a novel extension of multi-task Gaussian Cox process...
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08/21/2023
Towards Accelerated Model Training via Bayesian Data Selection
Mislabeled, duplicated, or biased data in real-world scenarios can lead ...
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04/20/2023
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Large-scale pre-trained models have achieved remarkable success in a var...
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04/06/2023
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a long-standing challenge in compu...
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02/21/2023
On Calibrating Diffusion Probabilistic Models
Recently, diffusion probabilistic models (DPMs) have achieved promising ...
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02/10/2023
Confidence-based Reliable Learning under Dual Noises
Deep neural networks (DNNs) have achieved remarkable success in a variet...
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10/23/2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Laplace approximation (LA) and its linearized variant (LLA) enable effor...
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04/30/2022
NeuralEF: Deconstructing Kernels by Deep Neural Networks
Learning the principal eigenfunctions of an integral operator defined by...
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04/30/2022
Deep Ensemble as a Gaussian Process Approximate Posterior
Deep Ensemble (DE) is an effective alternative to Bayesian neural networ...
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06/03/2021
Exploring Memorization in Adversarial Training
It is well known that deep learning models have a propensity for fitting...
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03/28/2021
Accurate and Reliable Forecasting using Stochastic Differential Equations
It is critical yet challenging for deep learning models to properly char...
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03/27/2021
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Despite their appealing flexibility, deep neural networks (DNNs) are vul...
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03/24/2021
Black-box Detection of Backdoor Attacks with Limited Information and Data
Although deep neural networks (DNNs) have made rapid progress in recent ...
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02/14/2020
Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to impr...
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11/22/2019
DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
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03/24/2019
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Deep learning methods have shown promise in unsupervised domain adaptati...
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02/25/2019
Batch Virtual Adversarial Training for Graph Convolutional Networks
We present batch virtual adversarial training (BVAT), a novel regulariza...
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11/02/2017