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08/01/2023
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
Learning from positive and unlabeled data is known as positive-unlabeled...
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07/04/2023
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
Few-shot image generation (FSIG) aims to learn to generate new and diver...
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05/31/2023
Efficient Diffusion Policies for Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn optimal policies from ...
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05/26/2023
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unpreced...
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05/03/2023
Nonparametric Generative Modeling with Conditional and Locally-Connected Sliced-Wasserstein Flows
Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric g...
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04/13/2023
CoSDA: Continual Source-Free Domain Adaptation
Without access to the source data, source-free domain adaptation (SFDA) ...
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03/17/2023
A Recipe for Watermarking Diffusion Models
Recently, diffusion models (DMs) have demonstrated their advantageous po...
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02/21/2023
On Calibrating Diffusion Probabilistic Models
Recently, diffusion probabilistic models (DPMs) have achieved promising ...
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02/09/2023
Better Diffusion Models Further Improve Adversarial Training
It has been recognized that the data generated by the denoising diffusio...
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02/09/2023
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving mor...
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01/28/2023
Does Federated Learning Really Need Backpropagation?
Federated learning (FL) is a general principle for decentralized clients...
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11/25/2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
Modern online advertising systems inevitably rely on personalization met...
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05/25/2019
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires la...
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01/25/2019
Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on variou...
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01/24/2019
Adversarial Variational Inference and Learning in Markov Random Fields
Markov random fields (MRFs) find applications in a variety of machine le...
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07/10/2018
Learning Implicit Generative Models by Teaching Explicit Ones
Implicit generative models are difficult to train as no explicit probabi...
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02/26/2018
Max-Mahalanobis Linear Discriminant Analysis Networks
A deep neural network (DNN) consists of a nonlinear transformation from ...
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12/09/2016
Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
For decades, advances in electronics were directly driven by the scaling...
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06/19/2014
