597 citations · 2.3k across the 106 of their papers we have counts for
9 papers · 1 filter
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
Jiyan He, Xuechen Li, Da Yu +6
Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the tw…
Return-Based Contrastive Representation Learning for Reinforcement Learning
Guoqing Liu, Chuheng Zhang, Li Zhao +5
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxi…
Self-supervised Adversarial Training
Kejiang Chen, Hang Zhou, Yuefeng Chen +6
Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in whi…
CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network
Xiaoyi Dong, Weiming Zhang, Nenghai Yu
In this paper, we propose an improvement of Adversarial Transformation Networks(ATN) to generate adversarial examples, which can fool white-box models and black-box models with a s…
Capacity Control of ReLU Neural Networks by Basis-path Norm
Shuxin Zheng, Qi Meng, Huishuai Zhang +3
Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property o…
Dual Supervised Learning
Yingce Xia, Tao Qin, Wei Chen +3
Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image cl…
SOL: A Library for Scalable Online Learning Algorithms
Yue Wu, Steven C. H. Hoi, Chenghao Liu +3
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data. The library provides a family of regula…
Asynchronous Stochastic Gradient Descent with Delay Compensation
Shuxin Zheng, Qi Meng, Taifeng Wang +4
With the fast development of deep learning, it has become common to learn big neural networks using massive training data. Asynchronous Stochastic Gradient Descent (ASGD) is widely…
Thompson Sampling for Budgeted Multi-armed Bandits
Yingce Xia, Haifang Li, Tao Qin +2
Thompson sampling is one of the earliest randomized algorithms for multi-armed bandits (MAB). In this paper, we extend the Thompson sampling to Budgeted MAB, where there is random…