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08/29/2023
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with str...
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05/19/2023
Moment Matching Denoising Gibbs Sampling
Energy-Based Models (EBMs) offer a versatile framework for modeling comp...
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09/15/2022
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and st...
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06/19/2022
Integrated Weak Learning
We introduce Integrated Weak Learning, a principled framework that integ...
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06/08/2022
Out-of-Distribution Detection with Class Ratio Estimation
Density-based Out-of-distribution (OOD) detection has recently been show...
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05/28/2022
Improving VAE-based Representation Learning
Latent variable models like the Variational Auto-Encoder (VAE) are commo...
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05/23/2022
Generalization Gap in Amortized Inference
The ability of likelihood-based probabilistic models to generalize to un...
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01/13/2022
Parallel Neural Local Lossless Compression
The recently proposed Neural Local Lossless Compression (NeLLoC), which ...
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10/23/2021
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Continual learning aims to learn a sequence of tasks from dynamic data d...
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09/29/2021
Flow Based Models For Manifold Data
Flow-based generative models typically define a latent space with dimens...
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09/04/2021
On the Out-of-distribution Generalization of Probabilistic Image Modelling
Out-of-distribution (OOD) detection and lossless compression constitute ...
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07/30/2019
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit t...
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07/27/2019
Variational f-divergence Minimization
Probabilistic models are often trained by maximum likelihood, which corr...
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11/21/2018