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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...
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05/25/2023
Characterizing Out-of-Distribution Error via Optimal Transport
Out-of-distribution (OOD) data poses serious challenges in deployed mach...
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05/18/2023
Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks
Lifelong learning agents aim to learn multiple tasks sequentially over a...
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02/10/2023
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Out-of-distribution (OOD) data poses serious challenges in deployed mach...
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01/18/2023
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, ...
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12/21/2022
Lifelong Reinforcement Learning with Modulating Masks
Lifelong learning aims to create AI systems that continuously and increm...
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10/26/2022
Is Multi-Task Learning an Upper Bound for Continual Learning?
Continual and multi-task learning are common machine learning approaches...
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06/16/2022
PRANC: Pseudo RAndom Networks for Compacting deep models
Communication becomes a bottleneck in various distributed Machine Learni...
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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...
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02/08/2022
Teaching Networks to Solve Optimization Problems
Leveraging machine learning to optimize the optimization process is an e...
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12/11/2021
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
Learning from set-structured data is an essential problem with many appl...
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04/17/2021
Lifelong Learning with Sketched Structural Regularization
Preventing catastrophic forgetting while continually learning new tasks ...
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03/05/2021
Set Representation Learning with Generalized Sliced-Wasserstein Embeddings
An increasing number of machine learning tasks deal with learning repres...
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06/16/2020
Wasserstein Embedding for Graph Learning
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and ...
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04/07/2020
Radon cumulative distribution transform subspace modeling for image classification
We present a new supervised image classification method for problems whe...
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03/12/2020
Statistical and Topological Properties of Sliced Probability Divergences
The idea of slicing divergences has been proven to be successful when co...
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02/28/2020
Generalized Sliced Distances for Probability Distributions
Probability metrics have become an indispensable part of modern statisti...
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09/21/2019
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
This paper introduces the modulated Hebbian plus Q network architecture ...
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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...
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07/04/2019
Neural Networks, Hypersurfaces, and Radon Transforms
Connections between integration along hypersufaces, Radon transforms, an...
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06/26/2019
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
The unprecedented success of deep neural networks in various application...
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06/10/2019
Generative Continual Concept Learning
After learning a concept, humans are also able to continually generalize...
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05/27/2019
Divide-and-Conquer Adversarial Detection
The vulnerabilities of deep neural networks against adversarial examples...
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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...
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03/11/2019
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Despite huge success, deep networks are unable to learn effectively in s...
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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...
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03/02/2019
Attention-Based Structural-Plasticity
Catastrophic forgetting/interference is a critical problem for lifelong ...
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02/16/2019
Neuromodulated Goal-Driven Perception in Uncertain Domains
In uncertain domains, the goals are often unknown and need to be predict...
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02/01/2019
Generalized Sliced Wasserstein Distances
The Wasserstein distance and its variations, e.g., the sliced-Wasserstei...
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12/01/2018
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks
Functional groups (FGs) serve as a foundation for analyzing chemical pro...
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04/05/2018
Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model
In this paper we study generative modeling via autoencoders while using ...
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12/01/2017
Image to Image Translation for Domain Adaptation
We propose a general framework for unsupervised domain adaptation, which...
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11/15/2017
Sliced Wasserstein Distance for Learning Gaussian Mixture Models
Gaussian mixture models (GMM) are powerful parametric tools with many ap...
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09/12/2017
Joint Dictionaries for Zero-Shot Learning
A classic approach toward zero-shot learning (ZSL) is to map the input d...
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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,...
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09/27/2016
A Transportation L^p Distance for Signal Analysis
Transport based distances, such as the Wasserstein distance and earth mo...
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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...
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11/10/2015
The Radon cumulative distribution transform and its application to image classification
Invertible image representation methods (transforms) are routinely emplo...
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11/10/2015
Sliced Wasserstein Kernels for Probability Distributions
Optimal transport distances, otherwise known as Wasserstein distances, h...
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07/21/2015
