Tom Goldstein

发表

Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability

Ronen Basri, Hao Zhou, David W. Jacobs, 2017, Journal of Mathematical Imaging and Vision.

Decentralized beamforming for massive MU-MIMO on a GPU cluster

Joseph R. Cavallaro, Tom Goldstein, Christoph Studer, 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

POKEMON: A non-linear beamforming algorithm for 1-bit massive MIMO

Tom Goldstein, Christoph Studer, Oscar Castañeda, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Channel Charting: Locating Users Within the Radio Environment Using Channel State Information

Olav Tirkkonen, Tom Goldstein, Christoph Studer, 2018, IEEE Access.

Automated Inference with Adaptive Batches

David W. Jacobs, Tom Goldstein, Soham De, 2017, AISTATS.

The STOne Transform: Multi-Resolution Image Enhancement and Compressive Video

Richard G. Baraniuk, Tom Goldstein, Lina Xu, 2015, IEEE Transactions on Image Processing.

Variance Reduction for Distributed Stochastic Gradient Descent pdf

Tom Goldstein, Gavin Taylor, Soham De, 2015, ArXiv.

Adaptive Consensus ADMM for Distributed Optimization pdf

Zheng Xu, Xiaoming Yuan, Tom Goldstein, 2017, ICML.

Unsupervised Charting of Wireless Channels

Olav Tirkkonen, Tom Goldstein, Christoph Studer, 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO

Tom Goldstein, Sven Jacobsson, Giuseppe Durisi, 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

Tom Goldstein, Christoph Studer, Oscar Castañeda, 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

Quantized Precoding for Massive MU-MIMO

Tom Goldstein, Sven Jacobsson, Giuseppe Durisi, 2016, IEEE Transactions on Communications.

Sparse atomic feature learning via gradient regularization: With applications to finding sparse representations of fMRI activity patterns

Tom Goldstein, Kendrick Kay, Matthew S. Keegan, 2014, 2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB).

PhasePack User Guide pdf

Tom Goldstein, Christoph Studer, Rohan Chandra, 2017, ArXiv.

An Empirical Study of ADMM for Nonconvex Problems pdf

Zheng Xu, Tom Goldstein, Mário A. T. Figueiredo, 2016, ArXiv.

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

Ronen Basri, David W. Jacobs, Soumyadip Sengupta, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

ACE: Adapting to Changing Environments for Semantic Segmentation

Larry S. Davis, Xin Wang, Tom Goldstein, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Decentralized Baseband Processing for Massive MU-MIMO Systems

Joseph R. Cavallaro, Tom Goldstein, Christoph Studer, 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

Optical image-based thickness characterization of atomically thin nanomaterials using computer vision techniques

Tom Goldstein, Jun Yan, Daniel Cui, 2018 .

Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction

Xavier Bresson, Tom Goldstein, Stanley Osher, 2010, J. Sci. Comput..

Efficient Distributed SGD with Variance Reduction

Tom Goldstein, Soham De, T. Goldstein, 2015, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Blackbox Clean-Label Poisoning Attacks on Neural Networks

Chen Zhu, Tom Goldstein, Christoph Studer, 2019, ICML 2019.

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

Richard G. Baraniuk, Tom Goldstein, Christoph Studer, 2014, ArXiv.

PhasePack: A Phase Retrieval Library

Tom Goldstein, Christoph Studer, Rohan Chandra, 2019, 2019 13th International conference on Sampling Theory and Applications (SampTA).

Representation-Constrained Autoencoders and an Application to Wireless Positioning

Olav Tirkkonen, Tom Goldstein, Christoph Studer, 2018 .

Scalable Classifiers with ADMM and Transpose Reduction

Zheng Xu, Tom Goldstein, Gavin Taylor, 2017, AAAI Workshops.

FPGA design of approximate semidefinite relaxation for data detection in large MIMO wireless systems

Tom Goldstein, Christoph Studer, Oscar Castañeda, 2016, 2016 IEEE International Symposium on Circuits and Systems (ISCAS).

Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition

Richard G. Baraniuk, Tom Goldstein, Azalia Mirhoseini, 2016, SDM.

Adversarially Robust Distillation

Micah Goldblum, Liam Fowl, Tom Goldstein, 2019, AAAI.

PhaseLin: Linear phase retrieval

Tom Goldstein, Andrew S. Lan, Christoph Studer, 2018, 2018 52nd Annual Conference on Information Sciences and Systems (CISS).

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

Larry S. Davis, Tom Goldstein, Ser-Nam Lim, 2018, ECCV.

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

Zheng Xu, Tom Goldstein, Karthik Abinav Sankararaman, 2019, ICML.

Visualizing the Loss Landscape of Neural Nets

Hao Li, Tom Goldstein, Gavin Taylor, 2017, NeurIPS.

Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems

Tom Goldstein, Sven Jacobsson, Giuseppe Durisi, 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

PhasePack: A phase retrieval library

Tom Goldstein, Christoph Studer, Rohan Chandra, 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

Convex Phase Retrieval without Lifting via PhaseMax

Tom Goldstein, Christoph Studer, T. Goldstein, 2017, ICML.

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Zheng Xu, Tom Goldstein, Bharat Singh, 2016, ICML.

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

Tudor Dumitras, Tom Goldstein, Ali Shafahi, 2018, NeurIPS.

Understanding Generalization through Visualizations pdf

Micah Goldblum, Liam Fowl, Tom Goldstein, 2019, ICBINB@NeurIPS.

Apparatus and method for binaural quality assessment

Tom Goldstein, Antony William Rix, Paul Alexander Barrett, 2003 .

Are adversarial examples inevitable? pdf

Tom Goldstein, Ali Shafahi, Christoph Studer, 2018, ICLR.

Big Batch SGD: Automated Inference using Adaptive Batch Sizes pdf

David W. Jacobs, Tom Goldstein, Soham De, 2016, ArXiv.

Universal Adversarial Training

Larry S. Davis, Zheng Xu, Tom Goldstein, 2018, AAAI.

Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation pdf

Larry S. Davis, Tom Goldstein, Pallabi Ghosh, 2018, ArXiv.

PhaseMax: Convex Phase Retrieval via Basis Pursuit

Tom Goldstein, Christoph Studer, T. Goldstein, 2016, IEEE Transactions on Information Theory.

Son of Zorn's lemma: Targeted style transfer using instance-aware semantic segmentation

Carlos D. Castillo, Tom Goldstein, Bharat Singh, 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction pdf

Tom Goldstein, Gavin Taylor, Soham De, 2015, ArXiv.

Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction

Tom Goldstein, Gavin Taylor, Kent Sayre, 2015, AISTATS.

Perceptual speech quality assessment in acoustic and binaural applications

Tom Goldstein, Antony W. Rix, 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Nonlinear 1-bit precoding for massive MU-MIMO with higher-order modulation

Tom Goldstein, Sven Jacobsson, Giuseppe Durisi, 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

FPGA design of low-complexity joint channel estimation and data detection for large SIMO wireless systems

Tom Goldstein, Christoph Studer, Oscar Castañeda, 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

Fast Alternating Direction Optimization Methods

Richard G. Baraniuk, Tom Goldstein, Brendan O'Donoghue, 2014, SIAM J. Imaging Sci..

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

Matthias Zwicker, David W. Jacobs, Tom Goldstein, 2019, ArXiv.

Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Richard G. Baraniuk, Tom Goldstein, Andrew S. Lan, 2016, ICML.

FASTA: A Generalized Implementation of Forward-Backward Splitting pdf

Richard G. Baraniuk, Tom Goldstein, Christoph Studer, 2015, ArXiv.

Adversarial attacks on Copyright Detection Systems

Tom Goldstein, Ali Shafahi, Parsa Saadatpanah, 2019, ICML.

Linear Spectral Estimators and an Application to Phase Retrieval pdf

Tom Goldstein, Andrew S. Lan, Christoph Studer, 2018, ICML.

The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video pdf

Richard G. Baraniuk, Tom Goldstein, Lina Xu, 2013, ArXiv.

Stabilizing Adversarial Nets With Prediction Methods pdf

Zheng Xu, David W. Jacobs, Tom Goldstein, 2017, ICLR.

Adversarial Training for Free!

Larry S. Davis, Zheng Xu, Tom Goldstein, 2019, NeurIPS.

Decentralized data detection for massive MU-MIMO on a Xeon Phi cluster

Joseph R. Cavallaro, Tom Goldstein, Christoph Studer, 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

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

Min Li, Xiaoming Yuan, Tom Goldstein, 2015, NIPS.

Democratic Representations pdf

Wotao Yin, Richard G. Baraniuk, Tom Goldstein, 2014, ArXiv.

Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation

Zheng Xu, Xiaoming Yuan, Tom Goldstein, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Non-negative Factorization of the Occurrence Tensor from Financial Contracts pdf

Louiqa Raschid, Tom Goldstein, Furong Huang, 2016, ArXiv.

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

Tom Goldstein, Furong Huang, Ali Shafahi, 2019, ArXiv.

1-bit Massive MU-MIMO Precoding in VLSI

Tom Goldstein, Sven Jacobsson, Giuseppe Durisi, 2017, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

Self-Expressive Decompositions for Matrix Approximation and Clustering pdf

Richard G. Baraniuk, Tom Goldstein, Konrad P. Körding, 2015, ArXiv.

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

Tom Goldstein, Christoph Studer, Sohil Shah, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Data Detection in Large Multi-Antenna Wireless Systems via Approximate Semidefinite Relaxation

Tom Goldstein, Christoph Studer, Oscar Castañeda, 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

Training Quantized Nets: A Deeper Understanding

Hanan Samet, Tom Goldstein, Hao Li, 2017, NIPS.

Challenges for Machine Learning on Distributed Platforms (Invited Talk)

Tom Goldstein, T. Goldstein, 2018, International Symposium on Distributed Computing.

The Split Bregman Method for L1-Regularized Problems

Tom Goldstein, Stanley Osher, S. Osher, 2009, SIAM J. Imaging Sci..

Adversarially robust transfer learning pdf

David Jacobs, Chen Zhu, Tom Goldstein, 2020, ICLR.

Operator Splitting Methods in Compressive Sensing and Sparse Approximation

Tom Goldstein, Xiaoqun Zhang, T. Goldstein, 2016 .

Adaptive step size selection for optimization via the ski rental problem

Richard G. Baraniuk, Tom Goldstein, Divyanshu Vats, 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features pdf

Tom Goldstein, Jonas Mueller, David Wipf, 2021, ArXiv.

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization pdf

Tom Goldstein, Chen Zhu, John P. Dickerson, 2021, NeurIPS.

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

Tom Goldstein, Furong Huang, Peihong Yu, 2021, ArXiv.

Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models pdf

Liam Fowl, Tom Goldstein, Jonas Geiping, 2021, ArXiv.

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training pdf

Micah Goldblum, Tom Goldstein, Avi Schwarzschild, 2021, ArXiv.

Micah Goldblum, Tom Goldstein, Avi Schwarzschild, 2021 .

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

Tom Goldstein, Christoph Studer, Hong-Min Chu, 2021, ICLR.

Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images pdf

Ming C. Lin, Tom Goldstein, Manli Shu, 2021, ArXiv.

Adversarial Examples Make Strong Poisons pdf

Jonas Geiping, Micah Goldblum, Liam Fowl, 2021, NeurIPS.

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

Micah Goldblum, Liam Fowl, Tom Goldstein, 2021, ArXiv.

Technical Challenges for Training Fair Neural Networks pdf

Micah Goldblum, Tom Goldstein, John P. Dickerson, 2021, ArXiv.

Active Learning at the ImageNet Scale pdf

Wojciech Czaja, Tom Goldstein, Hong-Min Chu, 2021, ArXiv.

Hybrid Jammer Mitigation for All-Digital mmWave Massive MU-MIMO

Tom Goldstein, Oscar Castaneda, Sven Jacobsson, 2021, 2021 55th Asilomar Conference on Signals, Systems, and Computers.

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

Tom Goldstein, Ming Lin, Shivam Akhauri, 2021, ArXiv.

Stochastic Training is Not Necessary for Generalization pdf

Tom Goldstein, Phillip E. Pope, Micah Goldblum, 2021, ArXiv.

A Frequency Perspective of Adversarial Robustness pdf

Tom Goldstein, Abhinav Shrivastava, Vatsal Agarwal, 2021, ArXiv.

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems pdf

Xiaohu You, Chuan Zhang, Olav Tirkkonen, 2021, IEEE Transactions on Wireless Communications.

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

Micah Goldblum, Tom Goldstein, Manli Shu, 2021, ArXiv.

Learning Revenue-Maximizing Auctions With Differentiable Matching pdf

Tom Goldstein, Michael J. Curry, John P. Dickerson, 2021, AISTATS.

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

Uzi Vishkin, Micah Goldblum, Tom Goldstein, 2021, NeurIPS.

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

Jonas Geiping, Micah Goldblum, Liam Fowl, 2021, ArXiv.

Long-Short Transformer: Efficient Transformers for Language and Vision pdf

Anima Anandkumar, Tom Goldstein, Chen Zhu, 2021, NeurIPS.

Data Augmentation for Meta-Learning

Micah Goldblum, Tom Goldstein, Amr Sharaf, 2020, ICML.

Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems pdf

Micah Goldblum, Tom Goldstein, Avi Schwarzschild, 2021, ArXiv.

Towards Transferable Adversarial Attacks on Vision Transformers pdf

Tom Goldstein, Yu-Gang Jiang, Zuxuan Wu, 2021, AAAI.

Execute Order 66: Targeted Data Poisoning for Reinforcement Learning pdf

Liam Fowl, Tom Goldstein, Gavin Taylor, 2022, ArXiv.

Comparing Human and Machine Bias in Face Recognition pdf

Tom Goldstein, John P. Dickerson, Valeriia Cherepanova, 2021, ArXiv.

Datasets for Studying Generalization from Easy to Hard Examples pdf

Tom Goldstein, Avi Schwarzschild, Arpit Bansal, 2021, ArXiv.

Robustness Disparities in Commercial Face Detection pdf

Tom Goldstein, John P. Dickerson, Samuel Dooley, 2021, ArXiv.

Are Commercial Face Detection Models as Biased as Academic Models?

Tom Goldstein, John P. Dickerson, Samuel Dooley, 2022 .

The Intrinsic Dimension of Images and Its Impact on Learning pdf

Micah Goldblum, Tom Goldstein, Chen Zhu, 2021, ICLR.

Adversarial Differentiable Data Augmentation for Autonomous Systems

Ming C. Lin, Tom Goldstein, Manli Shu, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching pdf

Wojciech Czaja, Jonas Geiping, Liam Fowl, 2021, ICLR.

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data pdf

Micah Goldblum, Tom Goldstein, Avi Schwarzschild, 2021, ArXiv.

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release pdf

Jonas Geiping, Micah Goldblum, Liam Fowl, 2021, ArXiv.

GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training pdf

Tom Goldstein, Chen Zhu, Renkun Ni, 2021, NeurIPS.

THAT: Two Head Adversarial Training for Improving Robustness at Scale pdf

Larry S. Davis, Tom Goldstein, Zuxuan Wu, 2021, ArXiv.

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

Zhe Gan, Jingjing Liu, Tom Goldstein, 2020, ECML/PKDD.

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness pdf

Tom Goldstein, Alexander Levine, Soheil Feizi, 2020, ICML.

FLAG: Adversarial Data Augmentation for Graph Neural Networks pdf

Bernard Ghanem, Tom Goldstein, Gavin Taylor, 2020, ArXiv.