no code implementations • 16 Feb 2024 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley

Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning.

1 code implementation • 5 Jul 2023 • Colin Bellinger, Mark Crowley, Isaac Tamblyn

The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost.

no code implementations • 23 May 2023 • Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn

This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery.

1 code implementation • 17 Feb 2023 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley

First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment.

1 code implementation • 26 Jan 2023 • Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley

This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning.

no code implementations • 25 Mar 2022 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method.

no code implementations • 3 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 $\{1, 2, \dots, d-1\}$.

no code implementations • 23 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.

no code implementations • 29 Dec 2021 • Chris Beeler, Xinkai Li, Colin Bellinger, Mark Crowley, Maia Fraser, Isaac Tamblyn

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

no code implementations • 14 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.

no code implementations • 26 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.

no code implementations • 1 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.

1 code implementation • 26 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.

no code implementations • 18 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.

no code implementations • 5 Oct 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....

no code implementations • 25 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.

1 code implementation • 25 Aug 2021 • Reza Godaz, Benyamin Ghojogh, Reshad Hosseini, Reza Monsefi, Fakhri Karray, Mark Crowley

Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.

no code implementations • 9 Aug 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.

no code implementations • 26 Jul 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, we introduce the structures of BM and RBM.

1 code implementation • Software Impacts 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.

no code implementations • 29 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.

no code implementations • 15 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

We start with reviewing the history of kernels in functional analysis and machine learning.

no code implementations • 3 Jun 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.

1 code implementation • 4 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.

1 code implementation • 18 Jan 2021 • Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.

Breast Cancer Histology Image Classification
Classification Of Breast Cancer Histology Images
**+3**

no code implementations • 4 Jan 2021 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.

1 code implementation • 31 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

1 code implementation • 22 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.

no code implementations • 2 Nov 2020 • Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh, Fakhri Karray, Mark Crowley

This paper is a tutorial and literature review on sampling algorithms.

1 code implementation • 29 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.

1 code implementation • 22 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach.

no code implementations • 22 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.

1 code implementation • 22 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.

1 code implementation • 22 Sep 2020 • Juan Carrillo, Mark Crowley, Guangyuan Pan, Liping Fu

Road maintenance during the Winter season is a safety critical and resource demanding operation.

1 code implementation • 17 Sep 2020 • Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley

Then, Sammon mapping, Isomap, and kernel Isomap are explained.

1 code implementation • 10 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**

1 code implementation • 4 Jul 2020 • Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor, Fakhri Karray, Mark Crowley, H. R. Tizhoosh

We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100, 000 patches.

Dimensionality Reduction
Histopathological Image Classification
**+1**

1 code implementation • 28 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.

1 code implementation • 19 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.

no code implementations • 26 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.

1 code implementation • 10 May 2020 • Milad Sikaroudi, Amir Safarpoor, Benyamin Ghojogh, Sobhan Shafiei, Mark Crowley, H. R. Tizhoosh

In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.

1 code implementation • 15 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.

1 code implementation • 5 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**

1 code implementation • 5 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.

1 code implementation • 5 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.

1 code implementation • 4 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.

no code implementations • 4 Apr 2020 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives.

1 code implementation • 8 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.

no code implementations • 2 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.

no code implementations • engrXiv 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

Then, we explain how to train HMM using EM and the Baum-Welch algorithm.

1 code implementation • 11 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.

1 code implementation • 6 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.

1 code implementation • 25 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.

1 code implementation • 25 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.

2 code implementations • 22 Jun 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

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

1 code implementation • 1 Jun 2019 • Benyamin Ghojogh, Mark Crowley

We also prove that LDA and Fisher discriminant analysis are equivalent.

1 code implementation • 1 Jun 2019 • Benyamin Ghojogh, Mark Crowley

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

no code implementations • 28 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.

2 code implementations • 7 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.

1 code implementation • Canadian Conference on Artificial Intelligence 2019 • Benyamin Ghojogh, Mark Crowley

Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.

1 code implementation • 25 Mar 2019 • Benyamin Ghojogh, Fakhri Karray, Mark Crowley

This paper is a tutorial for eigenvalue and generalized eigenvalue problems.

BIG-bench Machine Learning Matrix Factorization / Decomposition

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

1 code implementation • 3 Mar 2019 • Benyamin Ghojogh, Mark Crowley, Fakhri Karray

Two main methods for exploring patterns in data are data visualization and machine learning.

Data Visualization Applications

1 code implementation • 20 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.

no code implementations • 1 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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