Search Results for author: Tom Goldstein

Found 127 papers, 54 papers with code

Representation-Constrained Autoencoders and an Application to Wireless Positioning

no code implementations ICLR 2019 Pengzhi Huang, Emre Gonultas, Said Medjkouh, Oscar Castaneda, Olav Tirkkonen, Tom Goldstein, Christoph Studer

In a number of practical applications that rely on dimensionality reduction, the dataset or measurement process provides valuable side information that can be incorporated when learning low-dimensional embeddings.

Dimensionality Reduction

Analyzing the effect of neural network architecture on training performance

no code implementations ICML 2020 Karthik Abinav Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

Through novel theoretical and experimental results, we show how the neural net architecture affects gradient confusion, and thus the efficiency of training.

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning

no code implementations3 Jan 2022 Harrison Foley, Liam Fowl, Tom Goldstein, Gavin Taylor

Data poisoning for reinforcement learning has historically focused on general performance degradation, and targeted attacks have been successful via perturbations that involve control of the victim's policy and rewards.

Atari Games Data Poisoning

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering

no code implementations NeurIPS 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving.

Autonomous Driving Self-Driving Cars

Active Learning at the ImageNet Scale

1 code implementation25 Nov 2021 Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang, Wojciech Czaja, Richard Leapman, Micah Goldblum, Tom Goldstein

Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset.

Active Learning

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features

no code implementations26 Oct 2021 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.

A Frequency Perspective of Adversarial Robustness

no code implementations26 Oct 2021 Shishira R Maiya, Max Ehrlich, Vatsal Agarwal, Ser-Nam Lim, Tom Goldstein, Abhinav Shrivastava

Our analysis shows that adversarial examples are neither in high-frequency nor in low-frequency components, but are simply dataset dependent.

Adversarial Robustness

Comparing Human and Machine Bias in Face Recognition

no code implementations15 Oct 2021 Samuel Dooley, Ryan Downing, George Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P Dickerson, Tom Goldstein

Much recent research has uncovered and discussed serious concerns of bias in facial analysis technologies, finding performance disparities between groups of people based on perceived gender, skin type, lighting condition, etc.

Face Recognition

Stochastic Training is Not Necessary for Generalization

1 code implementation29 Sep 2021 Jonas Geiping, Micah Goldblum, Phillip E. Pope, Michael Moeller, Tom Goldstein

It is widely believed that the implicit regularization of stochastic gradient descent (SGD) is fundamental to the impressive generalization behavior we observe in neural networks.

Data Augmentation

Protecting Proprietary Data: Poisoning for Secure Dataset Release

no code implementations29 Sep 2021 Liam H Fowl, Ping-Yeh Chiang, Micah Goldblum, Jonas Geiping, Arpit Amit Bansal, Wojciech Czaja, Tom Goldstein

These two behaviors can be in conflict as an organization wants to prevent competitors from using their own data to replicate the performance of their proprietary models.

Data Poisoning

Towards Transferable Adversarial Attacks on Vision Transformers

1 code implementation9 Sep 2021 Zhipeng Wei, Jingjing Chen, Micah Goldblum, Zuxuan Wu, Tom Goldstein, Yu-Gang Jiang

We evaluate the transferability of attacks on state-of-the-art ViTs, CNNs and robustly trained CNNs.

Robustness Disparities in Commercial Face Detection

1 code implementation27 Aug 2021 Samuel Dooley, Tom Goldstein, John P. Dickerson

Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade.

Face Detection

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

1 code implementation3 Aug 2021 Roman Levin, Manli Shu, Eitan Borgnia, Furong Huang, Micah Goldblum, Tom Goldstein

We find that samples which cause similar parameters to malfunction are semantically similar.

Long-Short Transformer: Efficient Transformers for Language and Vision

3 code implementations NeurIPS 2021 Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, Bryan Catanzaro

For instance, Transformer-LS achieves 0. 97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware.

Language Modelling

Adversarial Examples Make Strong Poisons

1 code implementation NeurIPS 2021 Liam Fowl, Micah Goldblum, Ping-Yeh Chiang, Jonas Geiping, Wojtek Czaja, Tom Goldstein

The adversarial machine learning literature is largely partitioned into evasion attacks on testing data and poisoning attacks on training data.

Data Poisoning

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

1 code implementation16 Jun 2021 Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

In contrast, the Hidden Trigger Backdoor Attack achieves poisoning without placing a trigger into the training data at all.

Learning Revenue-Maximizing Auctions With Differentiable Matching

no code implementations15 Jun 2021 Michael J. Curry, Uro Lyi, Tom Goldstein, John Dickerson

We propose a new architecture to approximately learn incentive compatible, revenue-maximizing auctions from sampled valuations.

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks

1 code implementation NeurIPS 2021 Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein

In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.

The Intrinsic Dimension of Images and Its Impact on Learning

1 code implementation ICLR 2021 Phillip Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein

We find that common natural image datasets indeed have very low intrinsic dimension relative to the high number of pixels in the images.

Image Generation

THAT: Two Head Adversarial Training for Improving Robustness at Scale

no code implementations25 Mar 2021 Zuxuan Wu, Tom Goldstein, Larry S. Davis, Ser-Nam Lim

Many variants of adversarial training have been proposed, with most research focusing on problems with relatively few classes.

Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving

no code implementations15 Mar 2021 Shivam Akhauri, Laura Zheng, Tom Goldstein, Ming Lin

Practical learning-based autonomous driving models must be capable of generalizing learned behaviors from simulated to real domains, and from training data to unseen domains with unusual image properties.

Autonomous Driving Data Augmentation +2

Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy

no code implementations7 Mar 2021 Chen Chen, Kezhi Kong, Peihong Yu, Juan Luque, Tom Goldstein, Furong Huang

Randomized smoothing (RS) is an effective and scalable technique for constructing neural network classifiers that are certifiably robust to adversarial perturbations.

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations

no code implementations2 Mar 2021 Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

The InstaHide method has recently been proposed as an alternative to DP training that leverages supposed privacy properties of the mixup augmentation, although without rigorous guarantees.

Data Poisoning

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images

no code implementations26 Feb 2021 Yu Shen, Laura Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming C. Lin

For safety of autonomous driving, vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +1

What Doesn't Kill You Makes You Robust(er): Adversarial Training against Poisons and Backdoors

no code implementations26 Feb 2021 Jonas Geiping, Liam Fowl, Gowthami Somepalli, Micah Goldblum, Michael Moeller, Tom Goldstein

Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time.

Data Poisoning

The Uncanny Similarity of Recurrence and Depth

1 code implementation22 Feb 2021 Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein

It is widely believed that deep neural networks contain layer specialization, wherein networks extract hierarchical features representing edges and patterns in shallow layers and complete objects in deeper layers.

Image Classification

Center Smoothing: Certified Robustness for Networks with Structured Outputs

1 code implementation NeurIPS 2021 Aounon Kumar, Tom Goldstein

We extend the scope of certifiable robustness to problems with more general and structured outputs like sets, images, language, etc.

Adversarial Robustness Dimensionality Reduction +5

Technical Challenges for Training Fair Neural Networks

no code implementations12 Feb 2021 Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein

As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging).

Fairness Medical Diagnosis

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

no code implementations ICLR 2021 Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John Dickerson, Gavin Taylor, Tom Goldstein

Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike.

Face Detection Face Recognition

Certified Watermarks for Neural Networks

no code implementations1 Jan 2021 Arpit Amit Bansal, Ping-Yeh Chiang, Michael Curry, Hossein Souri, Rama Chellappa, John P Dickerson, Rajiv Jain, Tom Goldstein

Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.

WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic

no code implementations ICLR 2021 Renkun Ni, Hong-Min Chu, Oscar Castaneda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein

Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.

Quantization

Driving through the Lens: Improving Generalization of Learning-based Steering using Simulated Adversarial Examples

no code implementations1 Jan 2021 Yu Shen, Laura Yu Zheng, Manli Shu, Weizi Li, Tom Goldstein, Ming Lin

To ensure the wide adoption and safety of autonomous driving, the vehicles need to be able to drive under various lighting, weather, and visibility conditions in different environments.

Autonomous Driving Data Augmentation +2

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

no code implementations18 Dec 2020 Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance.

Data Poisoning

Analyzing the Machine Learning Conference Review Process

no code implementations24 Nov 2020 David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias.

Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks

no code implementations24 Oct 2020 Huimin Zeng, Chen Zhu, Tom Goldstein, Furong Huang

Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks.

FLAG: Adversarial Data Augmentation for Graph Neural Networks

2 code implementations19 Oct 2020 Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).

Data Augmentation Graph Classification +2

Data Augmentation for Meta-Learning

1 code implementation14 Oct 2020 Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, Tom Goldstein

Conventional image classifiers are trained by randomly sampling mini-batches of images.

Data Augmentation Meta-Learning

Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer

no code implementations14 Oct 2020 Chen Zhu, Zheng Xu, Ali Shafahi, Manli Shu, Amin Ghiasi, Tom Goldstein

Further, we demonstrate that the compact structure and corresponding initialization from the Lottery Ticket Hypothesis can also help in data-free training.

Quantization Transfer Learning

ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

no code implementations13 Oct 2020 Kevin Kuo, Anthony Ostuni, Elizabeth Horishny, Michael J. Curry, Samuel Dooley, Ping-Yeh Chiang, Tom Goldstein, John P. Dickerson

Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.

Fairness

An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Process

1 code implementation11 Oct 2020 David Tran, Alex Valtchanov, Keshav Ganapathy, Raymond Feng, Eric Slud, Micah Goldblum, Tom Goldstein

Members of the machine learning community are likely to overhear allegations ranging from randomness of acceptance decisions to institutional bias.

Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization

no code implementations28 Sep 2020 Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

Adversarial training is the industry standard for producing models that are robust to small adversarial perturbations.

Semantic Segmentation

Encoding Robustness to Image Style via Adversarial Feature Perturbations

1 code implementation NeurIPS 2021 Manli Shu, Zuxuan Wu, Micah Goldblum, Tom Goldstein

We adapt adversarial training by directly perturbing feature statistics, rather than image pixels, to produce models that are robust to various unseen distributional shifts.

Data Augmentation Semantic Segmentation

Certifying Confidence via Randomized Smoothing

no code implementations NeurIPS 2020 Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein

It uses the probabilities of predicting the top two most-likely classes around an input point under a smoothing distribution to generate a certified radius for a classifier's prediction.

High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO with Processing-In-Memory

no code implementations8 Sep 2020 Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer

All-digital basestation (BS) architectures enable superior spectral efficiency compared to hybrid solutions in massive multi-user MIMO systems.

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

1 code implementation ICLR 2021 Jonas Geiping, Liam Fowl, W. Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, Tom Goldstein

We consider a particularly malicious poisoning attack that is both "from scratch" and "clean label", meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data.

Data Poisoning

WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic

no code implementations26 Jul 2020 Renkun Ni, Hong-Min Chu, Oscar Castañeda, Ping-Yeh Chiang, Christoph Studer, Tom Goldstein

Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.

Quantization

MaxVA: Fast Adaptation of Step Sizes by Maximizing Observed Variance of Gradients

1 code implementation21 Jun 2020 Chen Zhu, Yu Cheng, Zhe Gan, Furong Huang, Jingjing Liu, Tom Goldstein

Adaptive gradient methods such as RMSProp and Adam use exponential moving estimate of the squared gradient to compute adaptive step sizes, achieving better convergence than SGD in face of noisy objectives.

Image Classification Machine Translation +3

Certifying Strategyproof Auction Networks

no code implementations NeurIPS 2020 Michael J. Curry, Ping-Yeh Chiang, Tom Goldstein, John Dickerson

We focus on the RegretNet architecture, which can represent auctions with arbitrary numbers of items and participants; it is trained to be empirically strategyproof, but the property is never exactly verified leaving potential loopholes for market participants to exploit.

Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training

no code implementations30 May 2020 Zheng Xu, Ali Shafahi, Tom Goldstein

Our adaptive networks also outperform larger widened non-adaptive architectures that have 1. 5 times more parameters.

MetaPoison: Practical General-purpose Clean-label Data Poisoning

2 code implementations NeurIPS 2020 W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

Existing attacks for data poisoning neural networks have relied on hand-crafted heuristics, because solving the poisoning problem directly via bilevel optimization is generally thought of as intractable for deep models.

AutoML bilevel optimization +2

Certified Defenses for Adversarial Patches

1 code implementation ICLR 2020 Ping-Yeh Chiang, Renkun Ni, Ahmed Abdelkader, Chen Zhu, Christoph Studer, Tom Goldstein

Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems.

Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers

no code implementations22 Feb 2020 Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein

Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness.

Adversarial Attacks on Machine Learning Systems for High-Frequency Trading

no code implementations21 Feb 2020 Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein

Algorithmic trading systems are often completely automated, and deep learning is increasingly receiving attention in this domain.

Algorithmic Trading

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness

1 code implementation ICML 2020 Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

Notably, for $p \geq 2$, this dependence on $d$ is no better than that of the $\ell_p$-radius that can be certified using isotropic Gaussian smoothing, essentially putting a matching lower bound on the robustness radius.

MSE-Optimal Neural Network Initialization via Layer Fusion

1 code implementation28 Jan 2020 Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past.

General Classification Knowledge Distillation

WITCHcraft: Efficient PGD attacks with random step size

no code implementations18 Nov 2019 Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi

State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points.

Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?

no code implementations25 Oct 2019 Ali Shafahi, Amin Ghiasi, Furong Huang, Tom Goldstein

Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization.

Adversarial Robustness

Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets

1 code implementation17 Oct 2019 Yogesh Balaji, Tom Goldstein, Judy Hoffman

Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks.

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

1 code implementation NeurIPS 2020 Micah Goldblum, Liam Fowl, Tom Goldstein

Previous work on adversarially robust neural networks for image classification requires large training sets and computationally expensive training procedures.

Few-Shot Image Classification General Classification +1

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

1 code implementation ICLR 2020 Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein

We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike.

Learning Theory

Siamese Neural Networks for Wireless Positioning and Channel Charting

no code implementations29 Sep 2019 Eric Lei, Oscar Castañeda, Olav Tirkkonen, Tom Goldstein, Christoph Studer

In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting.

Dimensionality Reduction

Deep k-NN Defense against Clean-label Data Poisoning Attacks

1 code implementation29 Sep 2019 Neehar Peri, Neal Gupta, W. Ronny Huang, Liam Fowl, Chen Zhu, Soheil Feizi, Tom Goldstein, John P. Dickerson

Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference.

Adversarial Attack Data Poisoning

FreeLB: Enhanced Adversarial Training for Natural Language Understanding

2 code implementations ICLR 2020 Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, Jingjing Liu

Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models.

Natural Language Understanding Word Embeddings

Improved Training of Certifiably Robust Models

no code implementations25 Sep 2019 Chen Zhu, Renkun Ni, Ping-Yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein

Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical (PGD) robustness.

The Effect of Neural Net Architecture on Gradient Confusion & Training Performance

no code implementations25 Sep 2019 Karthik A. Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

Through novel theoretical and experimental results, we show how the neural net architecture affects gradient confusion, and thus the efficiency of training.

Improving Channel Charting with Representation-Constrained Autoencoders

no code implementations7 Aug 2019 Pengzhi Huang, Oscar Castañeda, Emre Gönültaş, Saïd Medjkouh, Olav Tirkkonen, Tom Goldstein, Christoph Studer

Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI).

Dimensionality Reduction

Adversarial attacks on Copyright Detection Systems

no code implementations ICML 2020 Parsa Saadatpanah, Ali Shafahi, Tom Goldstein

Our goal is to raise awareness of the threats posed by adversarial examples in this space, and to highlight the importance of hardening copyright detection systems to attacks.

Understanding Generalization through Visualizations

2 code implementations NeurIPS Workshop ICBINB 2020 W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein

The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.

Network Deconvolution

5 code implementations ICLR 2020 Chengxi Ye, Matthew Evanusa, Hua He, Anton Mitrokhin, Tom Goldstein, James A. Yorke, Cornelia Fermüller, Yiannis Aloimonos

Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.

Image Classification

Adversarially Robust Distillation

2 code implementations23 May 2019 Micah Goldblum, Liam Fowl, Soheil Feizi, Tom Goldstein

In addition to producing small models with high test accuracy like conventional distillation, ARD also passes the superior robustness of large networks onto the student.

Adversarial Robustness Knowledge Distillation

Adversarially robust transfer learning

1 code implementation ICLR 2020 Ali Shafahi, Parsa Saadatpanah, Chen Zhu, Amin Ghiasi, Christoph Studer, David Jacobs, Tom Goldstein

By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks.

Transfer Learning

Transferable Clean-Label Poisoning Attacks on Deep Neural Nets

1 code implementation15 May 2019 Chen Zhu, W. Ronny Huang, Ali Shafahi, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein

Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being trained on this data.

Transfer Learning

The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent

no code implementations15 Apr 2019 Karthik A. Sankararaman, Soham De, Zheng Xu, W. Ronny Huang, Tom Goldstein

Our results show that, for popular initialization techniques, increasing the width of neural networks leads to lower gradient confusion, and thus faster model training.

ACE: Adapting to Changing Environments for Semantic Segmentation

no code implementations ICCV 2019 Zuxuan Wu, Xin Wang, Joseph E. Gonzalez, Tom Goldstein, Larry S. Davis

However, neural classifiers are often extremely brittle when confronted with domain shift---changes in the input distribution that occur over time.

Meta-Learning Semantic Segmentation

Understanding the (un)interpretability of natural image distributions using generative models

no code implementations6 Jan 2019 Ryen Krusinga, Sohil Shah, Matthias Zwicker, Tom Goldstein, David Jacobs

Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.

Density Estimation

Universal Adversarial Training

no code implementations27 Nov 2018 Ali Shafahi, Mahyar Najibi, Zheng Xu, John Dickerson, Larry S. Davis, Tom Goldstein

Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels.

Are adversarial examples inevitable?

no code implementations ICLR 2019 Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, Tom Goldstein

Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples.

Channel Charting: Locating Users within the Radio Environment using Channel State Information

no code implementations13 Jul 2018 Christoph Studer, Saïd Medjkouh, Emre Gönültaş, Tom Goldstein, Olav Tirkkonen

We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area.

Dimensionality Reduction

Linear Spectral Estimators and an Application to Phase Retrieval

no code implementations ICML 2018 Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer

Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements.

DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation

no code implementations ECCV 2018 Zuxuan Wu, Xintong Han, Yen-Liang Lin, Mustafa Gkhan Uzunbas, Tom Goldstein, Ser Nam Lim, Larry S. Davis

In particular, given an image from the source domain and unlabeled samples from the target domain, the generator synthesizes new images on-the-fly to resemble samples from the target domain in appearance and the segmentation network further refines high-level features before predicting semantic maps, both of which leverage feature statistics of sampled images from the target domain.

Semantic Segmentation

Visualizing the Loss Landscape of Neural Nets

7 code implementations ICLR 2018 Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein

Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions.

Convex Phase Retrieval without Lifting via PhaseMax

no code implementations ICML 2017 Tom Goldstein, Christoph Studer

Semidefinite relaxation methods transform a variety of non-convex optimization problems into convex problems, but square the number of variables.

Adaptive Consensus ADMM for Distributed Optimization

no code implementations ICML 2017 Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo, Xiaoming Yuan, Tom Goldstein

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters.

Distributed Optimization

Training Quantized Nets: A Deeper Understanding

no code implementations NeurIPS 2017 Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, Tom Goldstein

Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems.

Stabilizing Adversarial Nets With Prediction Methods

1 code implementation ICLR 2018 Abhay Yadav, Sohil Shah, Zheng Xu, David Jacobs, Tom Goldstein

Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train.

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation

no code implementations CVPR 2017 Zheng Xu, Mario A. T. Figueiredo, Xiaoming Yuan, Christoph Studer, Tom Goldstein

Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user.

A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery

no code implementations CVPR 2017 Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri

We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6.

An Empirical Study of ADMM for Nonconvex Problems

no code implementations10 Dec 2016 Zheng Xu, Soham De, Mario Figueiredo, Christoph Studer, Tom Goldstein

The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems.

Image Denoising

Non-negative Factorization of the Occurrence Tensor from Financial Contracts

1 code implementation10 Dec 2016 Zheng Xu, Furong Huang, Louiqa Raschid, Tom Goldstein

We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks.

Big Batch SGD: Automated Inference using Adaptive Batch Sizes

no code implementations18 Oct 2016 Soham De, Abhay Yadav, David Jacobs, Tom Goldstein

The high fidelity gradients enable automated learning rate selection and do not require stepsize decay.

Biconvex Relaxation for Semidefinite Programming in Computer Vision

1 code implementation31 May 2016 Sohil Shah, Abhay Kumar, Carlos Castillo, David Jacobs, Christoph Studer, Tom Goldstein

We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.

Metric Learning

Adaptive ADMM with Spectral Penalty Parameter Selection

no code implementations24 May 2016 Zheng Xu, Mario A. T. Figueiredo, Tom Goldstein

The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems, with differentiable or non-differentiable objective functions.

Training Neural Networks Without Gradients: A Scalable ADMM Approach

2 code implementations6 May 2016 Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

With the growing importance of large network models and enormous training datasets, GPUs have become increasingly necessary to train neural networks.

Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity

no code implementations CVPR 2016 Sohil Shah, Tom Goldstein, Christoph Studer

We demonstrate the efficacy of our regularizers on a variety of imaging tasks including compressive image recovery, image restoration, and robust PCA.

Image Restoration

Efficient Distributed SGD with Variance Reduction

no code implementations9 Dec 2015 Soham De, Tom Goldstein

Stochastic Gradient Descent (SGD) has become one of the most popular optimization methods for training machine learning models on massive datasets.

Stochastic Optimization

Variance Reduction for Distributed Stochastic Gradient Descent

no code implementations5 Dec 2015 Soham De, Gavin Taylor, Tom Goldstein

Variance reduction (VR) methods boost the performance of stochastic gradient descent (SGD) by enabling the use of larger, constant stepsizes and preserving linear convergence rates.

Stochastic Optimization

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing

no code implementations NeurIPS 2015 Tom Goldstein, Min Li, Xiaoming Yuan

The alternating direction method of multipliers (ADMM) is an important tool for solving complex optimization problems, but it involves minimization sub-steps that are often difficult to solve efficiently.

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

no code implementations8 Apr 2015 Tom Goldstein, Gavin Taylor, Kawika Barabin, Kent Sayre

Recent approaches to distributed model fitting rely heavily on consensus ADMM, where each node solves small sub-problems using only local data.

Distributed Computing

FASTA: A Generalized Implementation of Forward-Backward Splitting

2 code implementations16 Jan 2015 Tom Goldstein, Christoph Studer, Richard Baraniuk

This is a user manual for the software package FASTA.

Mathematical Software Numerical Analysis Numerical Analysis

A Field Guide to Forward-Backward Splitting with a FASTA Implementation

4 code implementations13 Nov 2014 Tom Goldstein, Christoph Studer, Richard Baraniuk

Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond.

Numerical Analysis G.1.6

Adaptive Primal-Dual Hybrid Gradient Methods for Saddle-Point Problems

1 code implementation2 May 2013 Tom Goldstein, Min Li, Xiaoming Yuan, Ernie Esser, Richard Baraniuk

The Primal-Dual hybrid gradient (PDHG) method is a powerful optimization scheme that breaks complex problems into simple sub-steps.

Numerical Analysis 65K15 G.1.6

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