Search Results for author:
Found 48 papers, 11 papers with code
Estimation of embedding vectors in high dimensions
To study this problem, we consider a simple probability model for discrete data where there is some "true" but unknown embedding where the correlation of random variables is related to the similarity of the embeddings.
5G Edge Vision: Wearable Assistive Technology for People with Blindness and Low Vision
In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information.
Terrestrial-Satellite Spectrum Sharing in the Upper Mid-Band with Interference Nulling
The growing demand for broader bandwidth in cellular networks has turned the upper mid-band (7-24 GHz) into a focal point for expansion.
VisPercep: A Vision-Language Approach to Enhance Visual Perception for People with Blindness and Low Vision
By combining the prompt and input image, a large vision-language model (i. e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt.
An Experimental Prototype for Multistatic Asynchronous ISAC
We prototype and validate a multistatic mmWave ISAC system based on IEEE802. 11ay.
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG).
Zero-Shot Wireless Indoor Navigation through Physics-Informed Reinforcement Learning
To equip the navigation agent with sample-efficient learning and {zero-shot} generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping.
Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
We study the local dynamics of GDA for training a GAN with a kernel-based discriminator.
JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical
We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions.
Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks
Evaluation of these systems requires statistical models that can capture the joint distribution of the channel paths across multiple frequencies.
Coexistence of UAVs and Terrestrial Users in Millimeter-Wave Urban Networks
5G millimeter-wave (mmWave) cellular networks are in the early phase of commercial deployments and present a unique opportunity for robust, high-data-rate communication to unmanned aerial vehicles (UAVs).
Instability and Local Minima in GAN Training with Kernel Discriminators
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data.
Wireless Channel Prediction in Partially Observed Environments
Site-specific radio frequency (RF) propagation prediction increasingly relies on models built from visual data such as cameras and LIDAR sensors.
Parametrization and Estimation of High-Rank Line-of-Sight MIMO Channels with Reflected Paths
In these scenarios, standard channel models based on plane waves cannot capture the curvature of each wave front necessary to model spatial multiplexing.
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Empirical observation of high dimensional phenomena, such as the double descent behaviour, has attracted a lot of interest in understanding classical techniques such as kernel methods, and their implications to explain generalization properties of neural networks.
Understanding Energy Efficiency and Interference Tolerance in Millimeter Wave Receivers
Power consumption is a key challenge in millimeter wave (mmWave) receiver front-ends, due to the need to support high dimensional antenna arrays at wide bandwidths.
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster
The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment.
Millimeter-Wave UAV Coverage in Urban Environments
Additional dedicated (rooftop-mounted and uptilted) base stations strengthen the coverage provided that their density is comparable to that of the standard deployment, and would be instrumental for sparse deployments of the latter.
Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz
Wireless communication at millimeter wave frequencies has attracted considerable attention for the delivery of high-bit-rate connectivity to unmanned aerial vehicles (UAVs).
Asymptotics of Ridge Regression in Convolutional Models
We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.
On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpoint
Since the BS measurements are noisy, it is not possible to find a narrow beam that includes the angle of arrival (AoA) of the user with probability one.
Implicit Bias of Linear RNNs
The degree of this bias depends on the variance of the transition kernel matrix at initialization and is related to the classic exploding and vanishing gradients problem.
Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication
The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs).
Matrix Inference and Estimation in Multi-Layer Models
In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features, as well as training samples, grow to infinity but the number of hidden nodes stays fixed.
Millimeter Wave Channel Modeling via Generative Neural Networks
Statistical channel models are instrumental to design and evaluate wireless communication systems.
Millimeter Wave Remove UAV Control and Communications for Public Safety Scenarios
Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings.
Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex
This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals.
Generalization Error of Generalized Linear Models in High Dimensions
We provide a general framework to characterize the asymptotic generalization error for single-layer neural networks (i. e., generalized linear models) with arbitrary non-linearities, making it applicable to regression as well as classification problems.
Inference in Multi-Layer Networks with Matrix-Valued Unknowns
We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output.
Input-Output Equivalence of Unitary and Contractive RNNs
Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies.
Inference with Deep Generative Priors in High Dimensions
This paper presents a novel algorithm, Multi-Layer Vector Approximate Message Passing (ML-VAMP), for inference in multi-layer stochastic neural networks.
High-Dimensional Bernoulli Autoregressive Process with Long-Range Dependence
We derive precise upper bounds on the mean-squared estimation error in terms of the number of samples, dimensions of the process, the lag $p$ and other key statistical properties of the model.
Asymptotics of MAP Inference in Deep Networks
Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text.
Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax}+\mathbf{w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction.
Information Theory Information Theory
Inference in Deep Networks in High Dimensions
In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs.
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems
We show that the parameter estimates and mean squared error (MSE) of x in each iteration converge to deterministic limits that can be precisely predicted by a simple set of state evolution (SE) equations.
End-to-End Simulation of 5G mmWave Networks
Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems.
Networking and Internet Architecture
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP.
Information Theory Information Theory
Denoising based Vector Approximate Message Passing
The denoising-based approximate message passing (D-AMP) methodology, recently proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated denoisers like BM3D into the AMP algorithm to achieve state-of-the-art compressive image recovery.
Information Theory Information Theory
Vector Approximate Message Passing
The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i. i. d.\ sub-Gaussian matrices $\mathbf{A}$, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal.
Information Theory Information Theory
Expectation Consistent Approximate Inference: Generalizations and Convergence
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems.
A Framework for End-to-End Evaluation of 5G mmWave Cellular Networks in ns-3
In this work, we present the first-of-its-kind, open-source framework for modeling mmWave cellular networks in the ns-3 simulator.
Networking and Internet Architecture I.6.5; I.6.7
Scalable Inference for Neuronal Connectivity from Calcium Imaging
In this work, we propose a computationally fast method for the state estimation based on a hybrid of loopy belief propagation and approximate message passing (AMP).
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning
We present a method, called adaptive generalized approximate message passing (Adaptive GAMP), that enables joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector $\xbf$.
Neural Reconstruction with Approximate Message Passing (NeuRAMP)
Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a low-dimensional signal that drives subsequent nonlinear stages.
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing
It is shown that with large random linear measurements and Gaussian noise, the asymptotic behavior of the MAP estimate of an n-dimensional vector ``decouples as n scalar MAP estimators.
Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis
Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for recovering sparse vectors from linear measurements.
Resolution Limits of Sparse Coding in High Dimensions
Recent research suggests that neural systems employ sparse coding.
