Search Results for author:
Found 33 papers, 14 papers with code
TexShape: Information Theoretic Sentence Embedding for Language Models
With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount.
Investigating Human-Identifiable Features Hidden in Adversarial Perturbations
Our study contributes to a deeper understanding of the underlying mechanisms behind adversarial attacks and offers insights for the development of more resilient defense strategies for neural networks.
iDML: Incentivized Decentralized Machine Learning
We leverage a smart contract not only for providing explicit incentives to end devices to participate in decentralized learning but also to create a fully decentralized mechanism to inspect and reflect on the behavior of the learning architecture.
A Control Theoretic Approach to Infrastructure-Centric Blockchain Tokenomics
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
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
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
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
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.
Beamformed Self-Interference Measurements at 28 GHz: Spatial Insights and Angular Spread
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
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
Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning.
EQ-Net: A Unified Deep Learning Framework for Log-Likelihood Ratio Estimation and Quantization
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
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
We show that the existing semi-implicit variational inference objective provably reduces information in the observed graph.
Millimeter Wave Full-Duplex Radios: New Challenges and Techniques
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
The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs.
Interpretable Factorization for Neural Network ECG Models
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
Our architecture uses a contrastive loss termand a disentangled generative model to sample negative pairs.
Deep Networks as Logical Circuits: Generalization and Interpretation
We improve the generalization of an already trained network by interpreting, diagnosing, and replacing components the logical circuit that is the DNN.
Detecting Patch Adversarial Attacks with Image Residuals
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
Variational autoencoders (VAEs) have ushered in a new era of unsupervised learning methods for complex distributions.
Beamforming Cancellation Design for Millimeter-Wave Full-Duplex
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
In this work, a deep learning-based quantization scheme for log-likelihood ratio (L-value) storage is introduced.
Deep Log-Likelihood Ratio Quantization
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
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
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.
Applications of Common Entropy for Causal Inference
We study the problem of discovering the simplest latent variable that can make two observed discrete variables conditionally independent.
Compressed Sensing with Deep Image Prior and Learned Regularization
We propose a novel method for compressed sensing recovery using untrained deep generative models.
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
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.
Cost-Optimal Learning of Causal Graphs
We consider the problem of learning a causal graph over a set of variables with interventions.
Entropic Causality and Greedy Minimum Entropy Coupling
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
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.
Learning Causal Graphs with Small Interventions
We prove that any deterministic adaptive algorithm needs to be a separating system in order to learn complete graphs in the worst case.
