
ElasticInfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data
We propose a novel unsupervised generative model, ElasticInfoGAN, that ...
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LargeBatch Training for LSTM and Beyond
Largebatch training approaches have enabled researchers to utilize larg...
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The Limitations of Adversarial Training and the BlindSpot Attack
The adversarial training procedure proposed by Madry et al. (2018) is on...
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Toward Finding The Global Optimal of Adversarial Examples
Current machine learning models are vulnerable to adversarial examples (...
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Reducing BERT PreTraining Time from 3 Days to 76 Minutes
Largebatch training is key to speeding up deep neural network training ...
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Learning to Learn by ZerothOrder Oracle
In the learning to learn (L2L) framework, we cast the design of optimiza...
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SignOPT: A QueryEfficient Hardlabel Adversarial Attack
We study the most practical problem setup for evaluating adversarial rob...
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Stochastic Shared Embeddings: Datadriven Regularization of Embedding Layers
In deep neural nets, lower level embedding layers account for a large po...
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VisualBERT: A Simple and Performant Baseline for Vision and Language
We propose VisualBERT, a simple and flexible framework for modeling a br...
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Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
Neural Ordinary Differential Equation (Neural ODE) has been proposed as ...
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QueryEfficient Hardlabel Blackbox Attack:An Optimizationbased Approach
We study the problem of attacking a machine learning model in the hardl...
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Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying r...
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AutoZOOM: Autoencoderbased Zeroth Order Optimization Method for Attacking Blackbox Neural Networks
Recent studies have shown that adversarial examples in stateoftheart ...
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Temporal Collaborative Ranking Via Personalized Transformer
The collaborative ranking problem has been an important open research qu...
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MACER: Attackfree and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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MLLOO: Detecting Adversarial Examples with Feature Attribution
Deep neural networks obtain stateoftheart performance on a series of ...
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Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
Neural language models have been widely used in various NLP tasks, inclu...
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EAD: ElasticNet Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural network...
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ImageNet Training in Minutes
Finishing 90epoch ImageNet1k training with ResNet50 on a NVIDIA M40 G...
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ZOO: Zeroth Order Optimization based Blackbox Attacks to Deep Neural Networks without Training Substitute Models
Deep neural networks (DNNs) are one of the most prominent technologies o...
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GPUacceleration for Largescale Tree Boosting
In this paper, we present a novel massively parallel algorithm for accel...
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Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Most distributed machine learning systems nowadays, including TensorFlow...
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ShowandFool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoderdecod...
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PU Learning for Matrix Completion
In this paper, we consider the matrix completion problem when the observ...
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Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
The L1regularized Gaussian maximum likelihood estimator (MLE) has been ...
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Towards Robust Neural Networks via Random Selfensemble
Recent studies have revealed the vulnerability of deep neural networks ...
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History PCA: A New Algorithm for Streaming PCA
In this paper we propose a new algorithm for streaming principal compone...
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Seq2Sick: Evaluating the Robustness of SequencetoSequence Models with Adversarial Examples
Crafting adversarial examples has become an important technique to evalu...
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
The robustness of neural networks to adversarial examples has received g...
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Graphic displays of MLB pitching mechanics and its evolutions in PITCHf/x data
Systemic and idiosyncratic patterns in pitching mechanics of 24 top star...
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LearningWord Embeddings for Lowresource Languages by PU Learning
Word embedding is a key component in many downstream applications in pro...
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Towards Fast Computation of Certified Robustness for ReLU Networks
Verifying the robustness property of a general Rectified Linear Unit (Re...
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Stochastic Zerothorder Optimization via Variance Reduction method
Derivativefree optimization has become an important technique used in m...
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SQLRank: A Listwise Approach to Collaborative Ranking
In this paper, we propose a listwise approach for constructing userspec...
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Accurate, Fast and Scalable Kernel Ridge Regression on Parallel and Distributed Systems
We propose two new methods to address the weak scaling problems of KRR: ...
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An inexact subsampled proximal Newtontype method for largescale machine learning
We propose a fast proximal Newtontype algorithm for minimizing regulari...
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Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data
We present a probabilistic framework for studying adversarial attacks on...
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GroupReduce: BlockWise LowRank Approximation for Neural Language Model Shrinking
Model compression is essential for serving large deep neural nets on dev...
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From Adversarial Training to Generative Adversarial Networks
In this paper, we are interested in two seemingly different concepts: ad...
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Fast Variance Reduction Method with Stochastic Batch Size
In this paper we study a family of variance reduction methods with rando...
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Stochastically Controlled Stochastic Gradient for the Convex and Nonconvex Composition problem
In this paper, we consider the convex and nonconvex composition problem...
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Stochastic Secondorder Methods for Nonconvex Optimization with Inexact Hessian and Gradient
Trust region and cubic regularization methods have demonstrated good per...
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Attack Graph Convolutional Networks by Adding Fake Nodes
Graph convolutional networks (GCNs) have been widely used for classifyin...
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RedSync : Reducing Synchronization Traffic for Distributed Deep Learning
Data parallelism has already become a dominant method to scale Deep Neur...
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Blockwise Partitioning for Extreme Multilabel Classification
Extreme multilabel classification aims to learn a classifier that annot...
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RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications
The Jacobian matrix (or the gradient for singleoutput networks) is dire...
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AdvBNN: Improved Adversarial Defense through Robust Bayesian Neural Network
We present a new algorithm to train a robust neural network against adve...
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Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding
Recent studies have demonstrated the vulnerability of deep convolutional...
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On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
CLEVER (CrossLipschitz Extreme Value for nEtwork Robustness) is an Extr...
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A Convex Relaxation Barrier to Tight Robust Verification of Neural Networks
Verification of neural networks enables us to gauge their robustness aga...
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ChoJui Hsieh
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Assistant professor of Computer Science and Statistics at UC Davis