Search Results for author: Mark Crowley

Found 60 papers, 36 papers with code

On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres

no code implementations3 Feb 2022 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Using an induction in a pyramid structure, we also extend the embedding dimensionality to lower embedding dimensionalities to show the validity of manifold hypothesis for embedding dimensionalities $\{1, 2, \dots, d-1\}$.

Dimensionality Reduction

Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey

no code implementations23 Jan 2022 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning.

Dimensionality Reduction Metric Learning

Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment

no code implementations29 Dec 2021 Chris Beeler, Xinkai Li, Mark Crowley, Maia Fraser, Isaac Tamblyn

Using a toy nautical navigation environment, we show that dynamic programming can be used when only partial information about a partially observed Markov decision process (POMDP) is known.

Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning

no code implementations14 Dec 2021 Colin Bellinger, Andriy Drozdyuk, Mark Crowley, Isaac Tamblyn

The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing.

reinforcement-learning

Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey

no code implementations26 Nov 2021 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.

Dimensionality Reduction Face Generation +3

Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments

no code implementations1 Nov 2021 Ken Ming Lee, Sriram Ganapathi Subramanian, Mark Crowley

We show that in fully-observable environments, independent algorithms can perform on par with multi-agent algorithms in cooperative and competitive settings.

reinforcement-learning

Multi-Agent Advisor Q-Learning

1 code implementation26 Oct 2021 Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible.

Decision Making Multi-agent Reinforcement Learning +2

Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey

no code implementations25 Aug 2021 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with UMAP algorithm where we explain probabilities of neighborhood in the input and embedding spaces, optimization of cost function, training algorithm, derivation of gradients, and supervised and semi-supervised embedding by UMAP.

Data Visualization Dimensionality Reduction

Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey

no code implementations29 Jun 2021 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU) or Semidefinite Embedding (SDE), and its variants.

Dimensionality Reduction

Generative Locally Linear Embedding

1 code implementation4 Apr 2021 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.

Dimensionality Reduction Variational Inference

Partially Observable Mean Field Reinforcement Learning

1 code implementation31 Dec 2020 Sriram Ganapathi Subramanian, Matthew E. Taylor, Mark Crowley, Pascal Poupart

Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents.

Multi-agent Reinforcement Learning Q-Learning Multiagent Systems

Locally Linear Embedding and its Variants: Tutorial and Survey

1 code implementation22 Nov 2020 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE.

Dimensionality Reduction

Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling

1 code implementation29 Sep 2020 Parisa Abdolrahim Poorheravi, Benyamin Ghojogh, Vincent Gaudet, Fakhri Karray, Mark Crowley

Many triplet mining methods have been developed for Siamese networks; however, these techniques have not been applied on the triplets of large margin metric learning for nearest neighbor classification.

Metric Learning

Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions

no code implementations22 Sep 2020 Juan Carrillo, Mark Crowley

Previous research has evaluated the potential of image classification methods for detecting road snow coverage by processing images from roadside cameras installed in RWIS (Road Weather Information System) stations.

Image Classification Spatial Interpolation

Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees

1 code implementation22 Sep 2020 Juan Carrillo, Daniel Garijo, Mark Crowley, Rober Carrillo, Yolanda Gil, Katherine Borda

Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making.

Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

1 code implementation22 Sep 2020 Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space.

Dimensionality Reduction

Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem

1 code implementation10 Jul 2020 Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

However, sampling from stochastic distributions of data rather than sampling merely from the existing embedding instances can provide more discriminative information.

Dimensionality Reduction Histopathological Image Classification +1

Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition

1 code implementation28 Jun 2020 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition.

3D Action Recognition Face Recognition

Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution

1 code implementation19 Jun 2020 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution.

Dimensionality Reduction Metric Learning

Active Measure Reinforcement Learning for Observation Cost Minimization

no code implementations26 May 2020 Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training.

Decision Making Q-Learning +1

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

1 code implementation15 Apr 2020 Colin Bellinger, Rory Coles, Mark Crowley, Isaac Tamblyn

Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design.

reinforcement-learning

Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks

1 code implementation5 Apr 2020 Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

The FDT and FDC loss functions are designed based on the statistical formulation of the Fisher Discriminant Analysis (FDA), which is a linear subspace learning method.

Classification Of Breast Cancer Histology Images Dimensionality Reduction +3

Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces

1 code implementation5 Apr 2020 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

After the tremendous development of neural networks trained by backpropagation, it is a good time to develop other algorithms for training neural networks to gain more insights into networks.

Dimensionality Reduction

Anomaly Detection and Prototype Selection Using Polyhedron Curvature

1 code implementation5 Apr 2020 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature.

Anomaly Detection Image Denoising +2

Weighted Fisher Discriminant Analysis in the Input and Feature Spaces

1 code implementation4 Apr 2020 Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.

Dimensionality Reduction Face Recognition

Isolation Mondrian Forest for Batch and Online Anomaly Detection

1 code implementation8 Mar 2020 Haoran Ma, Benyamin Ghojogh, Maria N. Samad, Dongyu Zheng, Mark Crowley

We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection.

Anomaly Detection Ensemble Learning +1

A review of machine learning applications in wildfire science and management

no code implementations2 Mar 2020 Piyush Jain, Sean C P Coogan, Sriram Ganapathi Subramanian, Mark Crowley, Steve Taylor, Mike D Flannigan

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems.

Fire Detection

Roweis Discriminant Analysis: A Generalized Subspace Learning Method

1 code implementation11 Oct 2019 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory.

Dimensionality Reduction Face Recognition

Quantized Fisher Discriminant Analysis

1 code implementation6 Sep 2019 Benyamin Ghojogh, Ali Saheb Pasand, Fakhri Karray, Mark Crowley

This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory.

Dimensionality Reduction Quantization

Locally Linear Image Structural Embedding for Image Structure Manifold Learning

1 code implementation25 Aug 2019 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold.

Dimensionality Reduction Image Quality Assessment +1

Principal Component Analysis Using Structural Similarity Index for Images

1 code implementation25 Aug 2019 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop.

Dimensionality Reduction Image Quality Assessment +1

Fisher and Kernel Fisher Discriminant Analysis: Tutorial

2 code implementations22 Jun 2019 Benyamin Ghojogh, Fakhri Karray, Mark Crowley

This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA.

Dimensionality Reduction

Unsupervised and Supervised Principal Component Analysis: Tutorial

1 code implementation1 Jun 2019 Benyamin Ghojogh, Mark Crowley

Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered.

Dimensionality Reduction

The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial

no code implementations28 May 2019 Benyamin Ghojogh, Mark Crowley

The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting.

L2 Regularization

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

2 code implementations7 May 2019 Benyamin Ghojogh, Maria N. Samad, Sayema Asif Mashhadi, Tania Kapoor, Wahab Ali, Fakhri Karray, Mark Crowley

Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way.

Dimensionality Reduction Feature Engineering +3

Artificial Counselor System for Stock Investment

2 code implementations Proceedings of the AAAI Conference on Artificial Intelligence 2019 Hadi NekoeiQachkanloo, Benyamin Ghojogh, Ali Saheb Pasand, Mark Crowley

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.

Portfolio Optimization Stock Prediction +1 General Finance Computational Engineering, Finance, and Science General Economics Economics

Fitting A Mixture Distribution to Data: Tutorial

1 code implementation20 Jan 2019 Benyamin Ghojogh, Aydin Ghojogh, Mark Crowley, Fakhri Karray

In explaining the main algorithm, first, fitting a mixture of two distributions is detailed and examples of fitting two Gaussians and Poissons, respectively for continuous and discrete cases, are introduced.

Bayesian Inference Bayesian Optimisation

Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program

no code implementations1 Feb 2017 Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams

The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia).

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