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DNN2LR: Automatic Feature Crossing for Credit Scoring
Credit scoring is a major application of machine learning for financial ...
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Rethinking Natural Adversarial Examples for Classification Models
Recently, it was found that many real-world examples without intentional...
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Deep Learning-Based Autoencoder for Data-Driven Modeling of an RF Photoinjector
We adopt a data-driven approach to model the longitudinal phase-space di...
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Cognitive Visual Inspection Service for LCD Manufacturing Industry
With the rapid growth of display devices, quality inspection via machine...
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Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in ...
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ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Continual learning usually assumes the incoming data are fully labeled, ...
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Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Time series forecasting is an important yet challenging task. Though dee...
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Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward ...
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Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
Various evaluation measures have been developed for multi-label classifi...
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Further Analysis of Outlier Detection with Deep Generative Models
The recent, counter-intuitive discovery that deep generative models (DGM...
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Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
The learning and evaluation of energy-based latent variable models (EBLV...
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Bi-level Score Matching for Learning Energy-based Latent Variable Models
Score matching (SM) provides a compelling approach to learn energy-based...
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Bag of Tricks for Adversarial Training
Adversarial training (AT) is one of the most effective strategies for pr...
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Deep Active Learning by Model Interpretability
Recent successes of Deep Neural Networks (DNNs) in a variety of research...
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Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Convolutional neural networks (CNNs) have been successfully used in a ra...
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A quasi-conservative discontinuous Galerkin method for multi-component flows using the non-oscillatory kinetic flux
In this paper, a high order quasi-conservative discontinuous Galerkin (D...
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Delving into the Adversarial Robustness on Face Recognition
Face recognition has recently made substantial progress and achieved hig...
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Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks
Hawkes process provides an effective statistical framework for analyzing...
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Absolutely convergent fixed-point fast sweeping WENO methods for steady state of hyperbolic conservation laws
Fixed-point iterative sweeping methods were developed in the literature ...
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Calibrated Reliable Regression using Maximum Mean Discrepancy
Accurate quantification of uncertainty is crucial for real-world applica...
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Dynamic Window-level Granger Causality of Multi-channel Time Series
Granger causality method analyzes the time series causalities without bu...
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Brain-inspired global-local hybrid learning towards human-like intelligence
The combination of neuroscience-oriented and computer-science-oriented a...
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Nonparametric Score Estimators
Estimating the score, i.e., the gradient of log density function, from a...
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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models p...
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Towards Privacy Protection by Generating Adversarial Identity Masks
As billions of personal data such as photos are shared through social me...
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Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously...
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VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling tha...
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Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses to impro...
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A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Score matching provides an effective approach to learning flexible unnor...
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Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to impr...
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Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becom...
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Triple Generative Adversarial Networks
Generative adversarial networks (GANs) have shown promise in image gener...
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Control variates and Rao-Blackwellization for deterministic sweep Markov chains
We study control variate methods for Markov chain Monte Carlo (MCMC) in ...
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The Search for Sparse, Robust Neural Networks
Recent work on deep neural network pruning has shown there exist sparse ...
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Design and Interpretation of Universal Adversarial Patches in Face Detection
We consider universal adversarial patches for faces - small visual eleme...
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DBSN: Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structures
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep...
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Generative Well-intentioned Networks
We propose Generative Well-intentioned Networks (GWINs), a novel framewo...
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Understanding and Stabilizing GANs' Training Dynamics with Control Theory
Generative adversarial networks (GANs) have made significant progress on...
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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
It has been widely recognized that adversarial examples can be easily cr...
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A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels
Recently deep neural networks have shown their capacity to memorize trai...
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Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind
Recent work in cross-lingual contextual word embedding learning cannot h...
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Spatial Process Decomposition for Quantitative Imaging Biomarkers Using Multiple Images of Varying Shapes
Quantitative imaging biomarkers (QIB) are extracted from medical images ...
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Improving Black-box Adversarial Attacks with a Transfer-based Prior
We consider the black-box adversarial setting, where the adversary has t...
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Multi-objects Generation with Amortized Structural Regularization
Deep generative models (DGMs) have shown promise in image generation. Ho...
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Automatic Realistic Music Video Generation from Segments of Youtube Videos
A Music Video (MV) is a video aiming at visually illustrating or extendi...
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Scalable Training of Inference Networks for Gaussian-Process Models
Inference in Gaussian process (GP) models is computationally challenging...
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Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires la...
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Boosting Generative Models by Leveraging Cascaded Meta-Models
Deep generative models are effective methods of modeling data. However, ...
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Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
Face recognition has obtained remarkable progress in recent years due to...
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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Deep neural networks are vulnerable to adversarial examples, which can m...
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