no code implementations • 27 Jun 2024 • Chayne Thrash, Ali Abbasi, Parsa Nooralinejad, Soroush Abbasi Koohpayegani, Reed Andreas, Hamed Pirsiavash, Soheil Kolouri

Through extensive experiments in computer vision and natural language processing tasks, we demonstrate that our method, MCNC, significantly outperforms state-of-the-art baselines in terms of compression, accuracy, and/or model reconstruction time.

1 code implementation • 29 May 2024 • Jeffery Dick, Saptarshi Nath, Christos Peridis, Eseoghene Benjamin, Soheil Kolouri, Andrea Soltoggio

The results suggest that optimal transport statistical methods provide an explainable and justifiable procedure for online context detection and reward optimization in lifelong reinforcement learning.

1 code implementation • 27 May 2024 • Yuxiang Gao, Soheil Kolouri, Ravindra Duddu

This cell-based MLP model also facilitates the use of a decoupled training scheme for Dirichlet boundary conditions and a parameter-sharing scheme for periodic boundary conditions, delivering superior accuracy compared to conventional PINNs.

1 code implementation • 28 Mar 2024 • Mingxing Rao, Yinhong Qin, Soheil Kolouri, Jie Ying Wu, Daniel Moyer

Purpose: Surgical video is an important data stream for gesture recognition.

no code implementations • 11 Mar 2024 • Ali Abbasi, Ashkan Shahbazi, Hamed Pirsiavash, Soheil Kolouri

However, traditional dataset distillation approaches often struggle to scale effectively with high-resolution images and more complex architectures due to the limitations in bi-level optimization.

no code implementations • 6 Feb 2024 • Yikun Bai, Rocio Diaz Martin, Abihith Kothapalli, Hengrong Du, Xinran Liu, Soheil Kolouri

We establish that PGW is a well-defined metric between mm-spaces and discuss its theoretical properties, including the existence of a minimizer for the PGW problem and the relationship between PGW and GW, among others.

1 code implementation • 4 Feb 2024 • Huy Tran, Yikun Bai, Abihith Kothapalli, Ashkan Shahbazi, Xinran Liu, Rocio Diaz Martin, Soheil Kolouri

This paper introduces a high-speed and highly parallelizable distance for comparing spherical measures using the stereographic projection and the generalized Radon transform, which we refer to as the Stereographic Spherical Sliced Wasserstein (S3W) distance.

no code implementations • 21 Nov 2023 • Ali Abbasi, Chayne Thrash, Elaheh Akbari, Daniel Zhang, Soheil Kolouri

Given a trained model and a subset of training data designated to be forgotten (i. e., the "forget set"), we introduce a three-step process, named CovarNav, to facilitate this forgetting.

no code implementations • CVPR 2024 • Ali Abbasi, Parsa Nooralinejad, Hamed Pirsiavash, Soheil Kolouri

Continual learning has gained substantial attention within the deep learning community, offering promising solutions to the challenging problem of sequential learning.

no code implementations • 9 Oct 2023 • Rocio Diaz Martin, Ivan Medri, Yikun Bai, Xinran Liu, Kangbai Yan, Gustavo K. Rohde, Soheil Kolouri

The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning.

1 code implementation • 4 Oct 2023 • Soroush Abbasi Koohpayegani, KL Navaneet, Parsa Nooralinejad, Soheil Kolouri, Hamed Pirsiavash

These methods can reduce the number of parameters needed to fine-tune an LLM by several orders of magnitude.

no code implementations • 27 Sep 2023 • Yikun Bai, Huy Tran, Steven B. Damelin, Soheil Kolouri

In this paper, we approach the point-cloud registration problem through the lens of optimal transport theory and first propose a comprehensive set of non-rigid registration methods based on the optimal partial transportation problem.

no code implementations • 25 Jul 2023 • Xinran Liu, Yikun Bai, Huy Tran, Zhanqi Zhu, Matthew Thorpe, Soheil Kolouri

In this paper, we introduce partial transport $\mathrm{L}^{p}$ distances as a new family of metrics for comparing generic signals, benefiting from the robustness of partial transport distances.

1 code implementation • 8 Jun 2023 • Abihith Kothapalli, Ashkan Shahbazi, Xinran Liu, Robert Sheng, Soheil Kolouri

Of our interest are permutation invariant networks, which are composed of a permutation equivariant backbone, permutation invariant global pooling, and regression/classification head.

2 code implementations • 18 May 2023 • Saptarshi Nath, Christos Peridis, Eseoghene Ben-Iwhiwhu, Xinran Liu, Shirin Dora, Cong Liu, Soheil Kolouri, Andrea Soltoggio

The key idea is that the isolation of specific task knowledge to specific masks allows agents to transfer only specific knowledge on-demand, resulting in robust and effective distributed lifelong learning.

no code implementations • 10 Feb 2023 • Yuzhe Lu, Zhenlin Wang, Runtian Zhai, Soheil Kolouri, Joseph Campbell, Katia Sycara

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops.

1 code implementation • 7 Feb 2023 • Yikun Bai, Ivan Medri, Rocio Diaz Martin, Rana Muhammad Shahroz Khan, Soheil Kolouri

To address these limitations, variants of the OT problem, including unbalanced OT, Optimal partial transport (OPT), and Hellinger Kantorovich (HK), have been proposed.

1 code implementation • 21 Jan 2023 • Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis, Pawel Ladosz, Jeffery Dick, Praveen K. Pilly, Soheil Kolouri

This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) variable and hierarchical reward structure, (4) multiple-task generation, (5) variable problem complexity.

no code implementations • 18 Jan 2023 • Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.

2 code implementations • 21 Dec 2022 • Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly, Soheil Kolouri, Andrea Soltoggio

The results suggest that RL with modulating masks is a promising approach to lifelong learning, to the composition of knowledge to learn increasingly complex tasks, and to knowledge reuse for efficient and faster learning.

2 code implementations • CVPR 2023 • Yikun Bai, Berhnard Schmitzer, Mathew Thorpe, Soheil Kolouri

Optimal transport (OT) has become exceedingly popular in machine learning, data science, and computer vision.

no code implementations • 26 Oct 2022 • Zihao Wu, Huy Tran, Hamed Pirsiavash, Soheil Kolouri

Moreover, it is imaginable that when learning from multiple tasks, a small subset of these tasks could behave as adversarial tasks reducing the overall learning performance in a multi-task setting.

1 code implementation • 24 Aug 2022 • Xinran Liu, Yikun Bai, Yuzhe Lu, Andrea Soltoggio, Soheil Kolouri

Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks.

no code implementations • 22 Aug 2022 • Meiyi Li, Soheil Kolouri, Javad Mohammadi

We demonstrate the performance of our proposed method in quadratic programming in the context of the optimal power dispatch (critical to the resiliency of our electric grid) and a constrained non-convex optimization in the context of image registration problems.

2 code implementations • ICCV 2023 • Parsa Nooralinejad, Ali Abbasi, Soroush Abbasi Koohpayegani, Kossar Pourahmadi Meibodi, Rana Muhammad Shahroz Khan, Soheil Kolouri, Hamed Pirsiavash

We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space.

no code implementations • 12 Mar 2022 • Ali Abbasi, Parsa Nooralinejad, Vladimir Braverman, Hamed Pirsiavash, Soheil Kolouri

Overcoming catastrophic forgetting in deep neural networks has become an active field of research in recent years.

no code implementations • 8 Feb 2022 • Xinran Liu, Yuzhe Lu, Ali Abbasi, Meiyi Li, Javad Mohammadi, Soheil Kolouri

In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP.

1 code implementation • 11 Dec 2021 • Yuzhe Lu, Xinran Liu, Andrea Soltoggio, Soheil Kolouri

This paper focuses on non-parametric and data-independent learning from set-structured data using approximate nearest neighbor (ANN) solutions, particularly locality-sensitive hashing.

1 code implementation • NeurIPS 2021 • Navid Naderializadeh, Joseph Comer, Reed Andrews, Heiko Hoffmann, Soheil Kolouri

Learning representations from sets has become increasingly important with many applications in point cloud processing, graph learning, image/video recognition, and object detection.

no code implementations • 17 Apr 2021 • Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman

In practice and due to computational constraints, most SR methods crudely approximate the importance matrix by its diagonal.

no code implementations • 5 Mar 2021 • Navid Naderializadeh, Soheil Kolouri, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann

An increasing number of machine learning tasks deal with learning representations from set-structured data.

1 code implementation • ICLR 2021 • Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann

We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks.

Ranked #3 on Graph Classification on RE-M5K

no code implementations • ICLR 2020 • Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde

The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.

no code implementations • ICLR 2020 • Soheil Kolouri, Nicholas A. Ketz, Andrea Soltoggio, Praveen K. Pilly

Deep neural networks suffer from the inability to preserve the learned data representation (i. e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training.

3 code implementations • 7 Apr 2020 • Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Shiying Li, Soheil Kolouri, Akram Aldroubi, Jonathan M. Nichols, Gustavo K. Rohde

We present a new supervised image classification method applicable to a broad class of image deformation models.

1 code implementation • NeurIPS 2020 • Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Şimşekli

The idea of slicing divergences has been proven to be successful when comparing two probability measures in various machine learning applications including generative modeling, and consists in computing the expected value of a `base divergence' between one-dimensional random projections of the two measures.

no code implementations • 28 Feb 2020 • Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Shahin Shahrampour

Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical hypothesis testing and generative modeling.

1 code implementation • 21 Sep 2019 • Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick, Yang Hu, Nicholas Ketz, Soheil Kolouri, Jeffrey L. Krichmar, Praveen Pilly, Andrea Soltoggio

This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA).

no code implementations • 4 Jul 2019 • Alex Gabourie, Mohammad Rostami, Philip Pope, Soheil Kolouri, Kyungnam Kim

We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the two source and target visual domains is minimized.

1 code implementation • 4 Jul 2019 • Soheil Kolouri, Xuwang Yin, Gustavo K. Rohde

Connections between integration along hypersufaces, Radon transforms, and neural networks are exploited to highlight an integral geometric mathematical interpretation of neural networks.

1 code implementation • CVPR 2020 • Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, Heiko Hoffmann

In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).

no code implementations • 10 Jun 2019 • Mohammad Rostami, Soheil Kolouri, Zak Murez, Yuri Owekcho, Eric Eaton, Kuyngnam Kim

Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes.

no code implementations • 10 Jun 2019 • Mohammad Rostami, Soheil Kolouri, James McClelland, Praveen Pilly

After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference with the past learned knowledge.

1 code implementation • 27 May 2019 • Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde

The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.

1 code implementation • ICLR 2019 • Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde

In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder.

no code implementations • 20 Mar 2019 • Shahin Shahrampour, Soheil Kolouri

Random features provide a practical framework for large-scale kernel approximation and supervised learning.

no code implementations • 11 Mar 2019 • Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly

We sample from this distribution and utilize experience replay to avoid forgetting and simultaneously accumulate new knowledge to the abstract distribution in order to couple the current task with past experience.

no code implementations • 6 Mar 2019 • Nicholas Ketz, Soheil Kolouri, Praveen Pilly

Here we propose a method to continually learn these internal world models through the interleaving of internally generated episodes of past experiences (i. e., pseudo-rehearsal).

no code implementations • 2 Mar 2019 • Soheil Kolouri, Nicholas Ketz, Xinyun Zou, Jeffrey Krichmar, Praveen Pilly

Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learning new tasks.

no code implementations • 16 Feb 2019 • Xinyun Zou, Soheil Kolouri, Praveen K. Pilly, Jeffrey L. Krichmar

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system.

1 code implementation • NeurIPS 2019 • Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde

The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning.

no code implementations • 1 Dec 2018 • Phillip Pope, Soheil Kolouri, Mohammad Rostrami, Charles Martin, Heiko Hoffmann

Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules.

5 code implementations • 5 Apr 2018 • Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde

In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution.

no code implementations • CVPR 2018 • Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, Kyungnam Kim

This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network.

2 code implementations • CVPR 2018 • Soheil Kolouri, Gustavo K. Rohde, Heiko Hoffmann

In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters.

no code implementations • 15 Sep 2017 • Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton

Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience.

no code implementations • 12 Sep 2017 • Soheil Kolouri, Mohammad Rostami, Yuri Owechko, Kyungnam Kim

A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e. g. visual data).

no code implementations • CVPR 2017 • Shay Deutsch, Soheil Kolouri, Kyungnam Kim, Yuri Owechko, Stefano Soatto

We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs.

no code implementations • 14 May 2017 • Shinjini Kundu, Soheil Kolouri, Kirk I Erickson, Arthur F Kramer, Edward McAuley, Gustavo K. Rohde

Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI).

no code implementations • 27 Sep 2016 • Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepčev

Transport based distances, such as the Wasserstein distance and earth mover's distance, have been shown to be an effective tool in signal and image analysis.

no code implementations • 15 Sep 2016 • Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepčev, Gustavo K. Rohde

Transport-based techniques for signal and data analysis have received increased attention recently.

no code implementations • 10 Nov 2015 • Soheil Kolouri, Se Rim Park, Gustavo K. Rohde

Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed.

no code implementations • CVPR 2016 • Soheil Kolouri, Yang Zou, Gustavo K. Rohde

Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions.

no code implementations • 21 Jul 2015 • Se Rim Park, Soheil Kolouri, Shinjini Kundu, Gustavo Rohde

Discriminating data classes emanating from sensors is an important problem with many applications in science and technology.

no code implementations • CVPR 2015 • Soheil Kolouri, Gustavo K. Rohde

Extracting high-resolution information from highly degraded facial images is an important problem with several applications in science and technology.

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