Dawn Song

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berkeley college

Professor of Computer Science Division at University of California, Berkeley

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients...
12/10/2019 ∙ by Peter Kairouz, et al. ∙ 31 ∙ share

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A Benchmark for Anomaly Segmentation

Detecting out-of-distribution examples is important for safety-critical ...
11/25/2019 ∙ by Dan Hendrycks, et al. ∙ 26 ∙ share

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Synthetic Datasets for Neural Program Synthesis

The goal of program synthesis is to automatically generate programs in a...
12/27/2019 ∙ by Richard Shin, et al. ∙ 21 ∙ share

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How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

Machine learning has been widely applied to various applications, some o...
04/24/2019 ∙ by Xinlei Pan, et al. ∙ 20 ∙ share

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Towards Efficient Data Valuation Based on the Shapley Value

"How much is my data worth?" is an increasingly common question posed by...
02/27/2019 ∙ by Ruoxi Jia, et al. ∙ 17 ∙ share

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Hierarchical Variational Imitation Learning of Control Programs

Autonomous agents can learn by imitating teacher demonstrations of the i...
12/29/2019 ∙ by Roy Fox, et al. ∙ 17 ∙ share

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An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms

This paper focuses on valuating training data for supervised learning ta...
11/17/2019 ∙ by Ruoxi Jia, et al. ∙ 14 ∙ share

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Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy

Outlier detection and novelty detection are two important topics for ano...
11/16/2019 ∙ by Min Du, et al. ∙ 14 ∙ share

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Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Given a data set D containing millions of data points and a data consume...
08/22/2019 ∙ by Ruoxi Jia, et al. ∙ 11 ∙ share

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Assessing Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) has achieved breakthrough results on ma...
10/29/2018 ∙ by Charles Packer, et al. ∙ 10 ∙ share

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Anomalous Instance Detection in Deep Learning: A Survey

Deep Learning (DL) is vulnerable to out-of-distribution and adversarial ...
03/16/2020 ∙ by Saikiran Bulusu, et al. ∙ 8 ∙ share

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Efficient Deep Learning on Multi-Source Private Data

Machine learning models benefit from large and diverse datasets. Using s...
07/17/2018 ∙ by Nick Hynes, et al. ∙ 6 ∙ share

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Natural Adversarial Examples

We introduce natural adversarial examples -- real-world, unmodified, and...
07/16/2019 ∙ by Dan Hendrycks, et al. ∙ 6 ∙ share

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The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks

This paper studies model-inversion attacks, in which the access to a mod...
11/17/2019 ∙ by Yuheng Zhang, et al. ∙ 6 ∙ share

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Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Self-supervision provides effective representations for downstream tasks...
06/28/2019 ∙ by Dan Hendrycks, et al. ∙ 5 ∙ share

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Data Poisoning Attack against Unsupervised Node Embedding Methods

Unsupervised node embedding methods (e.g., DeepWalk, LINE, and node2vec)...
10/30/2018 ∙ by Mingjie Sun, et al. ∙ 4 ∙ share

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Characterizing Attacks on Deep Reinforcement Learning

Deep reinforcement learning (DRL) has achieved great success in various ...
07/21/2019 ∙ by Chaowei Xiao, et al. ∙ 4 ∙ share

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TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems

A trojan backdoor is a hidden pattern typically implanted in a deep neur...
08/02/2019 ∙ by Wenbo Guo, et al. ∙ 3 ∙ share

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Physical Adversarial Examples for Object Detectors

Deep neural networks (DNNs) are vulnerable to adversarial examples-malic...
07/20/2018 ∙ by Kevin Eykholt, et al. ∙ 2 ∙ share

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Characterizing Audio Adversarial Examples Using Temporal Dependency

Recent studies have highlighted adversarial examples as a ubiquitous thr...
09/28/2018 ∙ by Zhuolin Yang, et al. ∙ 2 ∙ share

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Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

The problem of cross-platform binary code similarity detection aims at d...
08/22/2017 ∙ by Xiaojun Xu, et al. ∙ 0 ∙ share

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Making Neural Programming Architectures Generalize via Recursion

Empirically, neural networks that attempt to learn programs from data ha...
04/21/2017 ∙ by Jonathon Cai, et al. ∙ 0 ∙ share

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Can you fool AI with adversarial examples on a visual Turing test?

Deep learning has achieved impressive results in many areas of Computer ...
09/25/2017 ∙ by Xiaojun Xu, et al. ∙ 0 ∙ share

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Delving into adversarial attacks on deep policies

Adversarial examples have been shown to exist for a variety of deep lear...
05/18/2017 ∙ by Jernej Kos, et al. ∙ 0 ∙ share

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Adversarial examples for generative models

We explore methods of producing adversarial examples on deep generative ...
02/22/2017 ∙ by Jernej Kos, et al. ∙ 0 ∙ share

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Gradients explode - Deep Networks are shallow - ResNet explained

Whereas it is believed that techniques such as Adam, batch normalization...
12/15/2017 ∙ by George Philipp, et al. ∙ 0 ∙ share

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Mining Permission Request Patterns from Android and Facebook Applications (extended author version)

Android and Facebook provide third-party applications with access to use...
10/08/2012 ∙ by Mario Frank, et al. ∙ 0 ∙ share

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Latent Attention For If-Then Program Synthesis

Automatic translation from natural language descriptions into programs i...
11/07/2016 ∙ by Xinyun Chen, et al. ∙ 0 ∙ share

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Spatially transformed adversarial examples

Recent studies show that widely used deep neural networks (DNNs) are vul...
01/08/2018 ∙ by Chaowei Xiao, et al. ∙ 0 ∙ share

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Generating adversarial examples with adversarial networks

Deep neural networks (DNNs) have been found to be vulnerable to adversar...
01/08/2018 ∙ by Chaowei Xiao, et al. ∙ 0 ∙ share

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Adversarial Texts with Gradient Methods

Adversarial samples for images have been extensively studied in the lite...
01/22/2018 ∙ by Zhitao Gong, et al. ∙ 0 ∙ share

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Towards Practical Differential Privacy for SQL Queries

Differential privacy promises to enable general data analytics while pro...
06/28/2017 ∙ by Noah Johnson, et al. ∙ 0 ∙ share

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Learning Neural Programs To Parse Programs

In this work, we study an important problem: learning programs from inpu...
06/05/2017 ∙ by Xinyun Chen, et al. ∙ 0 ∙ share

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A Berkeley View of Systems Challenges for AI

With the increasing commoditization of computer vision, speech recogniti...
12/15/2017 ∙ by Ion Stoica, et al. ∙ 0 ∙ share

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Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Deep learning models have achieved high performance on many tasks, and t...
12/15/2017 ∙ by Xinyun Chen, et al. ∙ 0 ∙ share

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Note on Attacking Object Detectors with Adversarial Stickers

Deep learning has proven to be a powerful tool for computer vision and h...
12/21/2017 ∙ by Kevin Eykholt, et al. ∙ 0 ∙ share

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Exploring the Space of Black-box Attacks on Deep Neural Networks

Existing black-box attacks on deep neural networks (DNNs) so far have la...
12/27/2017 ∙ by Arjun Nitin Bhagoji, et al. ∙ 0 ∙ share

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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

Deep Neural Networks (DNNs) have recently been shown to be vulnerable ag...
01/08/2018 ∙ by Xingjun Ma, et al. ∙ 0 ∙ share

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The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets

Machine learning models based on neural networks and deep learning are b...
02/22/2018 ∙ by Nicholas Carlini, et al. ∙ 0 ∙ share

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Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution

Smart contracts are applications that execute on blockchains. Today they...
04/14/2018 ∙ by Raymond Cheng, et al. ∙ 0 ∙ share

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A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks

Malicious calls, i.e., telephony spams and scams, have been a long-stand...
04/07/2018 ∙ by Huichen Li, et al. ∙ 0 ∙ share

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SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

Synthesizing SQL queries from natural language is a long-standing open p...
11/13/2017 ∙ by Xiaojun Xu, et al. ∙ 0 ∙ share

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Curriculum Adversarial Training

Recently, deep learning has been applied to many security-sensitive appl...
05/13/2018 ∙ by Qi-Zhi Cai, et al. ∙ 0 ∙ share

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Tree-to-tree Neural Networks for Program Translation

Program translation is an important tool to migrate legacy code in one l...
02/11/2018 ∙ by Xinyun Chen, et al. ∙ 0 ∙ share

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Chorus: Differential Privacy via Query Rewriting

We present Chorus, a system with a novel architecture for providing diff...
09/20/2018 ∙ by Noah Johnson, et al. ∙ 0 ∙ share

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GamePad: A Learning Environment for Theorem Proving

In this paper, we introduce a system called GamePad that can be used to ...
06/02/2018 ∙ by Daniel Huang, et al. ∙ 0 ∙ share

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Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation

Deep Neural Networks (DNNs) have been widely applied in various recognit...
10/11/2018 ∙ by Chaowei Xiao, et al. ∙ 0 ∙ share

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Sanctorum: A lightweight security monitor for secure enclaves

Enclaves have emerged as a particularly compelling primitive to implemen...
12/27/2018 ∙ by Ilia Lebedev, et al. ∙ 0 ∙ share

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SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Impossibility of Full Decentralization in Permissionless Blockchains

Bitcoin uses blockchain technology and proof-of-work (PoW) mechanism whe...
05/13/2019 ∙ by Yujin Kwon, et al. ∙ 0 ∙ share

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Dawn Song

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