b'Vahid Tarokh'

research

∙ 06/11/2022

A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation

The goal of model compression is to reduce the size of a large neural ne...

Wenjing Yang, et al. ∙

research

∙ 05/27/2022

Inference and Sampling for Archimax Copulas

Understanding multivariate dependencies in both the bulk and the tails o...

Yuting Ng, et al. ∙

research

∙ 01/26/2022

On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections

We propose a new structured pruning framework for compressing Deep Neura...

Mohammadreza Soltani, et al. ∙

research

∙ 01/26/2022

Toward Data-Driven STAP Radar

Using an amalgamation of techniques from classical radar, computer visio...

Shyam Venkatasubramanian, et al. ∙

research

∙ 01/22/2022

Multi-Agent Adversarial Attacks for Multi-Channel Communications

Recently Reinforcement Learning (RL) has been applied as an anti-adversa...

Juncheng Dong, et al. ∙

research

∙ 01/10/2022

A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow

Analyzing large-scale data from simulations of turbulent flows is memory...

Mohammadreza Momenifar, et al. ∙

research

∙ 12/07/2021

Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models

We use a data-driven approach to model a three-dimensional turbulent flo...

Mohammadreza Momenifar, et al. ∙

research

∙ 11/26/2021

Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions

Numerous physical systems are described by ordinary or partial different...

Juncheng Dong, et al. ∙

research

∙ 11/25/2021

Characteristic Neural Ordinary Differential Equations

We propose Characteristic Neural Ordinary Differential Equations (C-NODE...

Xingzi Xu, et al. ∙

research

∙ 10/26/2021

Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders

A long-standing challenge in Recommender Systems (RCs) is the data spars...

Enmao Diao, et al. ∙

research

∙ 10/05/2021

Task Affinity with Maximum Bipartite Matching in Few-Shot Learning

We propose an asymmetric affinity score for representing the complexity ...

Cat P. Le, et al. ∙

research

∙ 06/03/2021

Semi-Empirical Objective Functions for MCMC Proposal Optimization

We introduce and demonstrate a semi-empirical procedure for determining ...

Chris Cannella, et al. ∙

research

∙ 06/02/2021

SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients

Federated Learning allows training machine learning models by using the ...

Enmao Diao, et al. ∙

research

∙ 06/02/2021

Gradient Assisted Learning

In distributed settings, collaborations between different entities, such...

Enmao Diao, et al. ∙

research

∙ 05/31/2021

Towards Explainable Convolutional Features for Music Audio Modeling

Audio signals are often represented as spectrograms and treated as 2D im...

Anna K. Yanchenko, et al. ∙

research

∙ 03/23/2021

Neural Architecture Search From Fréchet Task Distance

We formulate a Fréchet-type asymmetric distance between tasks based on F...

Cat P. Le, et al. ∙

research

∙ 03/03/2021

Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond

In this paper, we present Talaria, a novel permissioned blockchain simul...

Jiali Xing, et al. ∙

research

∙ 02/27/2021

Neural Architecture Search From Task Similarity Measure

In this paper, we propose a neural architecture search framework based o...

Cat P. Le, et al. ∙

research

∙ 02/22/2021

Generative Archimedean Copulas

We propose a new generative modeling technique for learning multidimensi...

Yuting Ng, et al. ∙

research

∙ 02/17/2021

Deep Extreme Value Copulas for Estimation and Sampling

We propose a new method for modeling the distribution function of high d...

Ali Hasan, et al. ∙

research

∙ 12/24/2020

On Statistical Efficiency in Learning

A central issue of many statistical learning problems is to select an ap...

Jie Ding, et al. ∙

research

∙ 10/27/2020

Task-Aware Neural Architecture Search

The design of handcrafted neural networks requires a lot of time and res...

Cat P. Le, et al. ∙

research

∙ 10/03/2020

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

Federated Learning (FL) is a method of training machine learning models ...

Enmao Diao, et al. ∙

research

∙ 07/13/2020

An Interpretable Baseline for Time Series Classification Without Intensive Learning

Recent advances in time series classification have largely focused on me...

Robert J. Ravier, et al. ∙

research

∙ 07/13/2020

Deep Cross-Subject Mapping of Neural Activity

In this paper, we demonstrate that a neural decoder trained on neural ac...

Marko Angjelichinoski, et al. ∙

research

∙ 07/13/2020

Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows

We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a tech...

Chris Cannella, et al. ∙

research

∙ 07/12/2020

Fisher Auto-Encoders

It has been conjectured that the Fisher divergence is more robust to mod...

Khalil Elkhalil, et al. ∙

research

∙ 07/12/2020

Learning latent stochastic differential equations with variational auto-encoders

We present a method for learning latent stochastic differential equation...

Ali Hasan, et al. ∙

research

∙ 05/15/2020

Model Linkage Selection for Cooperative Learning

Rapid developments in data collecting devices and computation platforms ...

Jiaying Zhou, et al. ∙

research

∙ 02/26/2020

Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization

Various types of parameter restart schemes have been proposed for accele...

Yi Zhou, et al. ∙

research

∙ 02/07/2020

Multimodal Controller for Generative Models

Class-conditional generative models are crucial tools for data generatio...

Enmao Diao, et al. ∙

research

∙ 01/02/2020

Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means

Marine buoys aid in the battle against Illegal, Unreported and Unregulat...

Yuting Ng, et al. ∙

research

∙ 11/12/2019

A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret

In this paper, we consider the problem of distributed online convex opti...

Yan Zhang, et al. ∙

research

∙ 11/08/2019

Cross-subject Decoding of Eye Movement Goals from Local Field Potentials

Objective. We consider the cross-subject decoding problem from local fie...

Marko Angjelichinoski, et al. ∙

research

∙ 10/23/2019

Deep Clustering of Compressed Variational Embeddings

Motivated by the ever-increasing demands for limited communication bandw...

Suya Wu, et al. ∙

research

∙ 10/22/2019

Learning Partial Differential Equations from Data Using Neural Networks

We develop a framework for estimating unknown partial differential equat...

Ali Hasan, et al. ∙

research

∙ 10/21/2019

Perception-Distortion Trade-off with Restricted Boltzmann Machines

In this work, we introduce a new procedure for applying Restricted Boltz...

Chris Cannella, et al. ∙

research

∙ 10/20/2019

Speech Emotion Recognition with Dual-Sequence LSTM Architecture

Speech Emotion Recognition (SER) has emerged as a critical component of ...

Jianyou Wang, et al. ∙

research

∙ 08/21/2019

Restricted Recurrent Neural Networks

Recurrent Neural Network (RNN) and its variations such as Long Short-Ter...

Enmao Diao, et al. ∙

research

∙ 03/23/2019

Distributed Lossy Image Compression with Recurrent Networks

We propose a new architecture for distributed image compression from a g...

Enmao Diao, et al. ∙

research

∙ 02/22/2019

Convergence Rate of Empirical Spectral Distribution of Random Matrices from Linear Codes

It is known that the empirical spectral distribution of random matrices ...

Chin Hei Chan, et al. ∙

research

∙ 02/07/2019

Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization

Two new stochastic variance-reduced algorithms named SARAH and SPIDER ha...

Yi Zhou, et al. ∙

research

∙ 01/29/2019

Minimax-optimal decoding of movement goals from local field potentials using complex spectral features

We consider the problem of predicting eye movement goals from local fiel...

Marko Angjelichinoski, et al. ∙

research

∙ 01/02/2019

SGD Converges to Global Minimum in Deep Learning via Star-convex Path

Stochastic gradient descent (SGD) has been found to be surprisingly effe...

Yi Zhou, et al. ∙

research

∙ 10/25/2018

SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization

There has been extensive research on developing stochastic variance redu...

Zhe Wang, et al. ∙

research

∙ 10/22/2018

Model Selection Techniques -- An Overview

In the era of big data, analysts usually explore various statistical mod...

Jie Ding, et al. ∙

research

∙ 10/09/2018

Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels

Nonlinear kernels can be approximated using finite-dimensional feature m...

Shahin Shahrampour, et al. ∙

research

∙ 09/02/2018

Sequential Detection of Regime Changes in Neural Data

The problem of detecting changes in firing patterns in neural data is st...

Taposh Banerjee, et al. ∙

research

∙ 07/02/2018

Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data

A framework is proposed to detect anomalies in multi-modal data. A deep ...

Taposh Banerjee, et al. ∙

research

∙ 06/10/2018

Stationary Geometric Graphical Model Selection

We consider the problem of model selection in Gaussian Markov fields in ...

Ilya Soloveychik, et al. ∙

Vahid Tarokh

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