Soheil Kolouri

research

∙ 06/08/2023

Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification

Learning from set-structured data, such as point clouds, has gained sign...

0 Ashkan Shahbazi, et al. ∙

research

∙ 05/25/2023

Characterizing Out-of-Distribution Error via Optimal Transport

Out-of-distribution (OOD) data poses serious challenges in deployed mach...

0 Yuzhe Lu, et al. ∙

research

∙ 05/18/2023

Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks

Lifelong learning agents aim to learn multiple tasks sequentially over a...

0 Saptarshi Nath, et al. ∙

research

∙ 02/10/2023

Predicting Out-of-Distribution Error with Confidence Optimal Transport

Out-of-distribution (OOD) data poses serious challenges in deployed mach...

0 Yuzhe Lu, et al. ∙

research

∙ 01/18/2023

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Despite the advancement of machine learning techniques in recent years, ...

9 Megan M. Baker, et al. ∙

research

∙ 12/21/2022

Lifelong Reinforcement Learning with Modulating Masks

Lifelong learning aims to create AI systems that continuously and increm...

0 Eseoghene Ben-Iwhiwhu, et al. ∙

research

∙ 10/26/2022

Is Multi-Task Learning an Upper Bound for Continual Learning?

Continual and multi-task learning are common machine learning approaches...

0 Zihao Wu, et al. ∙

research

∙ 06/16/2022

PRANC: Pseudo RAndom Networks for Compacting deep models

Communication becomes a bottleneck in various distributed Machine Learni...

0 Parsa Nooralinejad, et al. ∙

research

∙ 03/12/2022

Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations

Continual/lifelong learning from a non-stationary input data stream is a...

0 Ali Abbasi, et al. ∙

research

∙ 02/08/2022

Teaching Networks to Solve Optimization Problems

Leveraging machine learning to optimize the optimization process is an e...

0 Xinran Liu, et al. ∙

research

∙ 12/11/2021

SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings

Learning from set-structured data is an essential problem with many appl...

0 Yuzhe Lu, et al. ∙

research

∙ 04/17/2021

Lifelong Learning with Sketched Structural Regularization

Preventing catastrophic forgetting while continually learning new tasks ...

9 Haoran Li, et al. ∙

research

∙ 03/05/2021

Set Representation Learning with Generalized Sliced-Wasserstein Embeddings

An increasing number of machine learning tasks deal with learning repres...

0 Navid NaderiAlizadeh, et al. ∙

research

∙ 06/16/2020

Wasserstein Embedding for Graph Learning

We present Wasserstein Embedding for Graph Learning (WEGL), a novel and ...

0 Soheil Kolouri, et al. ∙

research

∙ 04/07/2020

Radon cumulative distribution transform subspace modeling for image classification

We present a new supervised image classification method for problems whe...

3 Mohammad Shifat-E-Rabbi, et al. ∙

research

∙ 03/12/2020

Statistical and Topological Properties of Sliced Probability Divergences

The idea of slicing divergences has been proven to be successful when co...

0 Kimia Nadjahi, et al. ∙

research

∙ 02/28/2020

Generalized Sliced Distances for Probability Distributions

Probability metrics have become an indispensable part of modern statisti...

0 Soheil Kolouri, et al. ∙

research

∙ 09/21/2019

Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture

This paper introduces the modulated Hebbian plus Q network architecture ...

0 Pawel Ladosz, et al. ∙

research

∙ 07/04/2019

Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

We address the problem of unsupervised domain adaptation (UDA) by learni...

0 Alex Gabourie, et al. ∙

research

∙ 07/04/2019

Neural Networks, Hypersurfaces, and Radon Transforms

Connections between integration along hypersufaces, Radon transforms, an...

6 Soheil Kolouri, et al. ∙

research

∙ 06/26/2019

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs

The unprecedented success of deep neural networks in various application...

6 Soheil Kolouri, et al. ∙

research

∙ 06/10/2019

Generative Continual Concept Learning

After learning a concept, humans are also able to continually generalize...

0 Mohammad Rostami, et al. ∙

research

∙ 05/27/2019

Divide-and-Conquer Adversarial Detection

The vulnerabilities of deep neural networks against adversarial examples...

0 Xuwang Yin, et al. ∙

research

∙ 03/20/2019

On Sampling Random Features From Empirical Leverage Scores: Implementation and Theoretical Guarantees

Random features provide a practical framework for large-scale kernel app...

0 Shahin Shahrampour, et al. ∙

research

∙ 03/11/2019

Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay

Despite huge success, deep networks are unable to learn effectively in s...

0 Mohammad Rostami, et al. ∙

research

∙ 03/06/2019

Using World Models for Pseudo-Rehearsal in Continual Learning

The utility of learning a dynamics/world model of the environment in rei...

0 Nicholas Ketz, et al. ∙

research

∙ 03/02/2019

Attention-Based Structural-Plasticity

Catastrophic forgetting/interference is a critical problem for lifelong ...

0 Soheil Kolouri, et al. ∙

research

∙ 02/16/2019

Neuromodulated Goal-Driven Perception in Uncertain Domains

In uncertain domains, the goals are often unknown and need to be predict...

0 Xinyun Zou, et al. ∙

research

∙ 02/01/2019

Generalized Sliced Wasserstein Distances

The Wasserstein distance and its variations, e.g., the sliced-Wasserstei...

0 Soheil Kolouri, et al. ∙

research

∙ 12/01/2018

Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

Functional groups (FGs) serve as a foundation for analyzing chemical pro...

0 Phillip Pope, et al. ∙

research

∙ 04/05/2018

Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model

In this paper we study generative modeling via autoencoders while using ...

2 Soheil Kolouri, et al. ∙

research

∙ 12/01/2017

Image to Image Translation for Domain Adaptation

We propose a general framework for unsupervised domain adaptation, which...

0 Zak Murez, et al. ∙

research

∙ 11/15/2017

Sliced Wasserstein Distance for Learning Gaussian Mixture Models

Gaussian mixture models (GMM) are powerful parametric tools with many ap...

0 Soheil Kolouri, et al. ∙

research

∙ 09/12/2017

Joint Dictionaries for Zero-Shot Learning

A classic approach toward zero-shot learning (ZSL) is to map the input d...

0 Soheil Kolouri, et al. ∙

research

∙ 05/14/2017

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry

Disease in the brain is often associated with subtle, spatially diffuse,...

0 Shinjini Kundu, et al. ∙

research

∙ 09/27/2016

A Transportation L^p Distance for Signal Analysis

Transport based distances, such as the Wasserstein distance and earth mo...

0 Matthew Thorpe, et al. ∙

research

∙ 09/15/2016

Transport-based analysis, modeling, and learning from signal and data distributions

Transport-based techniques for signal and data analysis have received in...

0 Soheil Kolouri, et al. ∙

research

∙ 11/10/2015

The Radon cumulative distribution transform and its application to image classification

Invertible image representation methods (transforms) are routinely emplo...

0 Soheil Kolouri, et al. ∙

research

∙ 11/10/2015

Sliced Wasserstein Kernels for Probability Distributions

Optimal transport distances, otherwise known as Wasserstein distances, h...

0 Soheil Kolouri, et al. ∙

research

∙ 07/21/2015

The Cumulative Distribution Transform and Linear Pattern Classification

Discriminating data classes emanating from sensors is an important probl...

0 Se Rim Park, et al. ∙

Soheil Kolouri

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