Search Results for author: Christoph Studer

Found 44 papers, 11 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

Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer

no code implementations21 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.

Dimensionality Reduction

A Resolution-Adaptive 8 mm$^\text{2}$ 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS

no code implementations23 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.

Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

no code implementations23 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.

Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication

no code implementations14 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.

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

Distortion-Aware Linear Precoding for Massive MIMO Downlink Systems with Nonlinear Power Amplifiers

no code implementations24 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

Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison

no code implementations15 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.

Data Compression

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.

CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

no code implementations6 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.

Outdoor Positioning

Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDM

1 code implementation4 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.

Quantization

Optimal Data Detection and Signal Estimation in Systems with Input Noise

no code implementations5 Aug 2020 Ramina Ghods, Charles Jeon, Arian Maleki, Christoph Studer

Practical systems often suffer from hardware impairments that already appear during signal generation.

Compressive Sensing

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

Identifying Unused RF Channels Using Least Matching Pursuit

no code implementations6 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.

Compressive Sensing

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.

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

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

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

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

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

An Estimation and Analysis Framework for the Rasch Model

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.

Collaborative Filtering Recommendation Systems

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.

Linearized Binary Regression

no code implementations1 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.

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.

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.

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.

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

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

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

Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models

no code implementations9 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.

Compressive Sensing Optical Flow Estimation +1

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

SPRITE: A Response Model For Multiple Choice Testing

no code implementations12 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.

Multiple-choice

Quantized Matrix Completion for Personalized Learning

no code implementations18 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.

Matrix Completion

Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics

no code implementations18 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.

Collaborative Filtering TAG

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

Time-varying Learning and Content Analytics via Sparse Factor Analysis

no code implementations19 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.

Collaborative Filtering Knowledge Tracing

Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data

no code implementations8 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.

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