224 citations · 3.2k across the 170 of their papers we have counts for
22 papers · 1 filter
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Qingyi Pan, Wenbo Hu, Jun Zhu
Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to impro…
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
Guoqiang Wu, Jun Zhu
Various evaluation measures have been developed for multi-label classification, including Hamming Loss (HL), Subset Accuracy (SA) and Ranking Loss (RL). However, there is a gap bet…
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang, Bin Dai, David Wipf +1
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applica…
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao, Chongxuan Li, Kun Xu +3
Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn…
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
Fan Bao, Kun Xu, Chongxuan Li +3
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition…
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning
Zhijie Deng, Jun Zhu
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibili…
Bag of Tricks for Adversarial Training
Tianyu Pang, Xiao Yang, Yinpeng Dong +2
Adversarial training (AT) is one of the most effective strategies for promoting model robustness. However, recent benchmarks show that most of the proposed improvements on AT are l…
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
Chen Ma, Shuyu Cheng, Li Chen +2
We propose a simple and highly query-efficient black-box adversarial attack named SWITCH, which has a state-of-the-art performance in the score-based setting. SWITCH features a hig…
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng +5
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is…
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Xiao Yang, Dingcheng Yang, Yinpeng Dong +3
Face recognition (FR) has recently made substantial progress and achieved high accuracy on standard benchmarks. However, it has raised security concerns in enormous FR applications…
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang, Kun Xu, Chongxuan Li +3
Several machine learning applications involve the optimization of higher-order derivatives (e.g., gradients of gradients) during training, which can be expensive in respect to memo…
Nonparametric Score Estimators
Yuhao Zhou, Jiaxin Shi, Jun Zhu
Estimating the score, i.e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of prob…
Dynamic Window-level Granger Causality of Multi-channel Time Series
Zhiheng Zhang, Wenbo Hu, Tian Tian +1
Granger causality method analyzes the time series causalities without building a complex causality graph. However, the traditional Granger causality method assumes that the causali…
Calibrated Reliable Regression using Maximum Mean Discrepancy
Peng Cui, Wenbo Hu, Jun Zhu
Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertai…
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Yucen Luo, Alex Beatson, Mohammad Norouzi +4
Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest. We introduce an unbiased estimator of the log margin…
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Liyuan Wang, Bo Lei, Qian Li +3
Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by cata…
Towards Face Encryption by Generating Adversarial Identity Masks
Xiao Yang, Yinpeng Dong, Tianyu Pang +4
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention. Several attempts have been made to all…
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Ziyu Wang, Shuyu Cheng, Yueru Li +2
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative. In this pa…
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli +2
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architec…
Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang, Xiao Yang, Yinpeng Dong +3
Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models. In this work, we advocate incorporating the hypersphere embedd…
Adversarial Distributional Training for Robust Deep Learning
Yinpeng Dong, Zhijie Deng, Tianyu Pang +2
Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples. However, most existing AT method…
Analyzing the Noise Robustness of Deep Neural Networks
Kelei Cao, Mengchen Liu, Hang Su +3
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to normal examples, can mislead deep neural networks (DNNs) to make incorrect predicti…