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.
no code implementations • 8 Apr 2024 • Gian Marti, Flurin Arquint, Christoph Studer
JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer.
no code implementations • 30 Nov 2023 • Jonas Roth, Domenic Keller, Oscar Castañeda, Christoph Studer
At the heart of the synthesizer is the big Fourier oscillator (BFO), a novel digital very-large scale integration (VLSI) design that utilizes additive synthesis to generate a wide variety of aliasing-free waveforms.
no code implementations • 17 Apr 2023 • Paul Ferrand, Maxime Guillaud, Christoph Studer, Olav Tirkkonen
Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart.
1 code implementation • 27 Feb 2023 • Alexandra Gallyas-Sanhueza, Christoph Studer
Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms.
1 code implementation • 15 Dec 2022 • Reinhard Wiesmayr, Chris Dick, Jakob Hoydis, Christoph Studer
We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer.
no code implementations • 15 Nov 2022 • Pengzhi Huang, Emre Gönültaş, Maximilian Arnold, K. Pavan Srinath, Jakob Hoydis, Christoph Studer
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information.
2 code implementations • 25 Oct 2022 • Reinhard Wiesmayr, Gian Marti, Chris Dick, Haochuan Song, Christoph Studer
Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention.
no code implementations • 21 Oct 2021 • Brian Rappaport, Emre Gönültaş, Jakob Hoydis, Maximilian Arnold, Pavan Koteshwar Srinath, Christoph Studer
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points.
no code implementations • 23 Jul 2021 • Oscar Castañeda, Zachariah Boynton, Seyed Hadi Mirfarshbafan, Shimin Huang, Jamie C. Ye, Alyosha Molnar, Christoph Studer
All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption.
no code implementations • 23 Jul 2021 • Oscar Castañeda, Seyed Hadi Mirfarshbafan, Shahaboddin Ghajari, Alyosha Molnar, Sven Jacobsson, Giuseppe Durisi, Christoph Studer
All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions.
no code implementations • 14 Jul 2021 • Sueda Taner, Christoph Studer
Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multiple-input multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies.
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.
no code implementations • 24 Dec 2020 • Sina Rezaei Aghdam, Sven Jacobsson, Ulf Gustavsson, Giuseppe Durisi, Christoph Studer, Thomas Eriksson
By studying the spatial characteristics of the distortion, we demonstrate that conventional linear precoding techniques steer nonlinear distortions towards the users.
Information Theory Signal Processing Information Theory
no code implementations • 15 Sep 2020 • Zephan M. Enciso, Seyed Hadi Mirfarshbafan, Oscar Castañeda, Clemens JS. Schaefer, Christoph Studer, Siddharth Joshi
Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine learning inference, data compression, audio and ultrasound applications, among many others.
no code implementations • 8 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.
no code implementations • 6 Sep 2020 • Emre Gönültaş, Eric Lei, Jack Langerman, Howard Huang, Christoph Studer
Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions.
1 code implementation • 4 Sep 2020 • Seyed Hadi Mirfarshbafan, Mahdi Shabany, Seyed Alireza Nezamalhosseini, Christoph Studer
Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm.
no code implementations • 5 Aug 2020 • Ramina Ghods, Charles Jeon, Arian Maleki, Christoph Studer
Practical systems often suffer from hardware impairments that already appear during signal generation.
no code implementations • 26 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.
no code implementations • 6 May 2020 • Emre Gönültaş, Milad Taghavi, Sweta Soni, Alyssa B. Apsel, Christoph Studer
Cognitive radio aims at identifying unused radio-frequency (RF) bands with the goal of re-using them opportunistically for other services.
no code implementations • 20 Apr 2020 • Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor.
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.
1 code implementation • 28 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.
no code implementations • 29 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.
no code implementations • 7 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).
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.
1 code implementation • 15 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.
6 code implementations • NeurIPS 2019 • Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, Tom Goldstein
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
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.
1 code implementation • 13 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.
no code implementations • ICML 2018 • Andrew S. Lan, Mung Chiang, Christoph Studer
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance.
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.
6 code implementations • NeurIPS 2018 • Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
no code implementations • 1 Feb 2018 • Andrew S. Lan, Mung Chiang, Christoph Studer
We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements.
11 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.
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.
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.
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.
no code implementations • 10 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.
1 code implementation • 31 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.
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.
no code implementations • 9 Mar 2015 • Aswin C. Sankaranarayanan, Lina Xu, Christoph Studer, Yun Li, Kevin Kelly, Richard G. Baraniuk
In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs.
2 code implementations • 16 Jan 2015 • Tom Goldstein, Christoph Studer, Richard Baraniuk
This is a user manual for the software package FASTA.
Mathematical Software Numerical Analysis Numerical Analysis
no code implementations • 12 Jan 2015 • Ryan Ning, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk
In this work, we propose a novel methodology for unordered categorical IRT that we call SPRITE (short for stochastic polytomous response item model) that: (i) analyzes both ordered and unordered categories, (ii) offers interpretable outputs, and (iii) provides improved data fitting compared to existing models.
no code implementations • 18 Dec 2014 • Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e. g., correct/incorrect) graded learner responses to questions.
no code implementations • 18 Dec 2014 • Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk
SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner's knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts.
4 code implementations • 13 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
no code implementations • 19 Dec 2013 • Andrew S. Lan, Christoph Studer, Richard G. Baraniuk
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications.
no code implementations • 8 May 2013 • Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk
In order to better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e. g., topics or keywords) available for each question.
no code implementations • 22 Mar 2013 • Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk
We estimate these factors given the graded responses to a collection of questions.