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06/25/2023
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Deep neural networks (DNNs) are incredibly vulnerable to crafted, imperc...
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05/27/2023
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Vision-language models have achieved tremendous progress far beyond what...
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01/31/2023
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Real-world data contains a vast amount of multimodal information, among ...
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12/24/2022
Frequency Regularization for Improving Adversarial Robustness
Deep neural networks are incredibly vulnerable to crafted, human-imperce...
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10/17/2022
ODG-Q: Robust Quantization via Online Domain Generalization
Quantizing neural networks to low-bitwidth is important for model deploy...
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03/16/2022
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
Although many fields have witnessed the superior performance brought abo...
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07/12/2021
Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning
When we deploy machine learning models in high-stakes medical settings, ...
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03/23/2021
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Interpretability in machine learning models is important in high-stakes ...
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02/15/2021
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
Learning convolutional neural networks (CNNs) with low bitwidth is chall...
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08/03/2020
Dynamic and Static Context-aware LSTM for Multi-agent Motion Prediction
Multi-agent motion prediction is challenging because it aims to foresee ...
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04/21/2019
