Search Results for author: Sriram Vishwanath

Found 30 papers, 13 papers with code

A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics

no code implementations23 Oct 2022 Oguzhan Akcin, Robert P. Streit, Benjamin Oommen, Sriram Vishwanath, Sandeep Chinchali

As such, the associated token rewards should gracefully scale with the size of the decentralized system, but should be carefully balanced with consumer demand to manage inflation and be designed to ultimately reach an equilibrium.

Spatial and Statistical Modeling of Multi-Panel Millimeter Wave Self-Interference

no code implementations14 Oct 2022 Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath, Jeffrey G. Andrews

Characterizing self-interference is essential to the design and evaluation of in-band full-duplex communication systems.

STEER: Beam Selection for Full-Duplex Millimeter Wave Communication Systems

no code implementations15 Jul 2022 Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath, Jeffrey G. Andrews

Modern millimeter wave (mmWave) communication systems rely on beam alignment to deliver sufficient beamforming gain to close the link between devices.

LoneSTAR: Analog Beamforming Codebooks for Full-Duplex Millimeter Wave Systems

no code implementations22 Jun 2022 Ian P. Roberts, Sriram Vishwanath, Jeffrey G. Andrews

This work develops LoneSTAR, a novel enabler of full-duplex millimeter wave (mmWave) communication systems through the design of analog beamforming codebooks.

Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning

1 code implementation21 Jun 2022 Ali Lotfi Rezaabad, Sidharth Kumar, Sriram Vishwanath, Jonathan I. Tamir

Pretraining on a large source data set and fine-tuning on the target samples is prone to overfitting in the few-shot regime, where only a small number of target samples are available.

Contrastive Learning Domain Adaptation +3

Beamformed Self-Interference Measurements at 28 GHz: Spatial Insights and Angular Spread

no code implementations15 Jun 2022 Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath, Jeffrey G. Andrews

We present measurements and analysis of self-interference in multi-panel millimeter wave (mmWave) full-duplex communication systems at 28 GHz.

Millimeter Wave Analog Beamforming Codebooks Robust to Self-Interference

no code implementations27 May 2021 Ian P. Roberts, Hardik B. Jain, Sriram Vishwanath, Jeffrey G. Andrews

This paper develops a novel methodology for designing analog beamforming codebooks for full-duplex millimeter wave (mmWave) transceivers, the first such codebooks to the best of our knowledge.

Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization

1 code implementation2 Mar 2021 Marius Arvinte, Sriram Vishwanath, Ahmed H. Tewfik, Jonathan I. Tamir

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning.

MRI Reconstruction

EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization

no code implementations23 Dec 2020 Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath

In this work, we introduce EQ-Net: the first holistic framework that solves both the tasks of log-likelihood ratio (LLR) estimation and quantization using a data-driven method.


Hybrid Beamforming for Millimeter Wave Full-Duplex under Limited Receive Dynamic Range

no code implementations21 Dec 2020 Ian P. Roberts, Jeffrey G. Andrews, Sriram Vishwanath

To prevent self-interference from saturating the receiver of a full-duplex device having limited dynamic range, our design addresses saturation on a per-antenna and per-RF chain basis.

Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference

1 code implementation31 Oct 2020 Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan Tamir

We show that the existing semi-implicit variational inference objective provably reduces information in the observed graph.

Graph Embedding Link Prediction +2

Millimeter Wave Full-Duplex Radios: New Challenges and Techniques

no code implementations13 Sep 2020 Ian P. Roberts, Jeffrey G. Andrews, Hardik B. Jain, Sriram Vishwanath

Equipping millimeter wave (mmWave) systems with full-duplex capability would accelerate and transform next-generation wireless applications and forge a path for new ones.


Long Short-Term Memory Spiking Networks and Their Applications

1 code implementation9 Jul 2020 Ali Lotfi Rezaabad, Sriram Vishwanath

The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs.

Time Series Analysis

Interpretable Factorization for Neural Network ECG Models

no code implementations26 Jun 2020 Christopher Snyder, Sriram Vishwanath

A generalizable impact beyond ECGs lies in the ability to provide a rich test-bed for the development of interpretive techniques in medicine.

Robust Face Verification via Disentangled Representations

1 code implementation5 Jun 2020 Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath

Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs.

Adversarial Robustness Face Verification

Deep Networks as Logical Circuits: Generalization and Interpretation

no code implementations25 Mar 2020 Christopher Snyder, Sriram Vishwanath

We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components the logical circuit that is the DNN.

Generalization Bounds

Detecting Patch Adversarial Attacks with Image Residuals

1 code implementation28 Feb 2020 Marius Arvinte, Ahmed Tewfik, Sriram Vishwanath

We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks.


Learning Representations by Maximizing Mutual Information in Variational Autoencoders

1 code implementation21 Dec 2019 Ali Lotfi Rezaabad, Sriram Vishwanath

Variational autoencoders (VAEs) have ushered in a new era of unsupervised learning methods for complex distributions.

Representation Learning

Beamforming Cancellation Design for Millimeter-Wave Full-Duplex

no code implementations18 Aug 2019 Ian P. Roberts, Sriram Vishwanath

In recent years, there has been extensive research on millimeter-wave (mmWave) communication and on in-band full-duplex (FD) communication, but work on the combination of the two is relatively lacking.

Deep Learning-Based Quantization of L-Values for Gray-Coded Modulation

1 code implementation18 Jun 2019 Marius Arvinte, Sriram Vishwanath, Ahmed H. Tewfik

In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced.


Deep Log-Likelihood Ratio Quantization

1 code implementation11 Mar 2019 Marius Arvinte, Ahmed H. Tewfik, Sriram Vishwanath

In this work, a deep learning-based method for log-likelihood ratio (LLR) lossy compression and quantization is proposed, with emphasis on a single-input single-output uncorrelated fading communication setting.


Sample Compression, Support Vectors, and Generalization in Deep Learning

no code implementations5 Nov 2018 Christopher Snyder, Sriram Vishwanath

The paper shows that the number of support vectors s relates with learning guarantees for neural networks through sample compression bounds, yielding a sample complexity of O(ns/epsilon) for networks with n neurons.

Experimental Design for Cost-Aware Learning of Causal Graphs

no code implementations NeurIPS 2018 Erik M. Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph.

Experimental Design

Applications of Common Entropy for Causal Inference

no code implementations NeurIPS 2020 Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath

We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent.

Causal Inference

Compressed Sensing with Deep Image Prior and Learned Regularization

1 code implementation17 Jun 2018 Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, Alexandros G. Dimakis

We propose a novel method for compressed sensing recovery using untrained deep generative models.

CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

2 code implementations ICLR 2018 Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath

We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph.

Face Generation

Cost-Optimal Learning of Causal Graphs

no code implementations ICML 2017 Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath

We consider the problem of learning a causal graph over a set of variables with interventions.

Graph Learning

Entropic Causality and Greedy Minimum Entropy Coupling

no code implementations28 Jan 2017 Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi

This framework requires the solution of a minimum entropy coupling problem: Given marginal distributions of m discrete random variables, each on n states, find the joint distribution with minimum entropy, that respects the given marginals.

Entropic Causal Inference

1 code implementation12 Nov 2016 Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi

We show that the problem of finding the exogenous variable with minimum entropy is equivalent to the problem of finding minimum joint entropy given $n$ marginal distributions, also known as minimum entropy coupling problem.

Causal Inference

Learning Causal Graphs with Small Interventions

2 code implementations NeurIPS 2015 Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath

We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case.

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