Search Results for author:
Found 65 papers, 39 papers with code
Disentanglement in Implicit Causal Models via Switch Variable
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning.
Learning when to observe: A frugal reinforcement learning framework for a high-cost world
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.
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery.
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
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.
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning.
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method.
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres
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 $\{1, 2, \dots, d-1\}$.
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey
In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning.
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
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.
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing.
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
We show that in fully-observable environments, independent algorithms can perform on par with multi-agent algorithms in cooperative and competitive settings.
Multi-Agent Advisor Q-Learning
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.
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey
Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning.
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
Then, we explain second-order methods including Newton's method for unconstrained, equality constrained, and inequality constrained problems....
Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
Riemannian LBFGS (RLBFGS) is an extension of this method to Riemannian manifolds.
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey
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.
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey
This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections.
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey
Then, we introduce the structures of BM and RBM.
Generative locally linear embedding: A module for manifold unfolding and visualization
One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction.
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey
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.
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
We start with reviewing the history of kernels in functional analysis and machine learning.
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey
Versions of graph embedding are then explained which are generalized versions of Laplacian eigenmap and locality preserving projection.
Generative Locally Linear Embedding
In this work, we propose two novel generative versions of LLE, named Generative LLE (GLLE), whose linear reconstruction steps are stochastic rather than deterministic.
Magnification Generalization for Histopathology Image Embedding
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
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey
Finally, VAE is explained where the encoder, decoder and sampling from the latent space are introduced.
Partially Observable Mean Field Reinforcement Learning
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
In this paper, we first cover LLE, kernel LLE, inverse LLE, and feature fusion with LLE.
Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
This paper is a tutorial and literature review on sampling algorithms.
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
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.
Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions
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.
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach.
Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data
Road maintenance during the Winter season is a safety critical and resource demanding operation.
Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees
Climate science is critical for understanding both the causes and consequences of changes in global temperatures and has become imperative for decisive policy-making.
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Then, Sammon mapping, Isomap, and kernel Isomap are explained.
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
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
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
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
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
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.
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution
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.
Active Measure Reinforcement Learning for Observation Cost Minimization
Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training.
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study
In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning.
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment
Reinforcement learning (RL) has been demonstrated to have great potential in many applications of scientific discovery and design.
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
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.
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
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
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
We propose a novel approach to anomaly detection called Curvature Anomaly Detection (CAD) and Kernel CAD based on the idea of polyhedron curvature.
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
Generative models and inferential autoencoders mostly make use of $\ell_2$ norm in their optimization objectives.
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights.
Isolation Mondrian Forest for Batch and Online Anomaly Detection
We propose a new method, named isolation Mondrian forest (iMondrian forest), for batch and online anomaly detection.
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems.
Hidden Markov Model: Tutorial
Then, we explain how to train HMM using EM and the Baum-Welch algorithm.
Roweis Discriminant Analysis: A Generalized Subspace Learning Method
We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory.
Quantized Fisher Discriminant Analysis
This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory.
Principal Component Analysis Using Structural Similarity Index for Images
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.
Locally Linear Image Structural Embedding for Image Structure Manifold Learning
We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold.
Fisher and Kernel Fisher Discriminant Analysis: Tutorial
This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA.
Linear and Quadratic Discriminant Analysis: Tutorial
We also prove that LDA and Fisher discriminant analysis are equivalent.
Unsupervised and Supervised Principal Component Analysis: Tutorial
Then, PCA with singular value decomposition, dual PCA, and kernel PCA are covered.
The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorial
The upper bound on the generalization error of boosting is also provided to show why boosting prevents from overfitting.
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
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.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction.
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems.
BIG-bench Machine Learning
Matrix Factorization / Decomposition
Artificial Counselor System for Stock Investment
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
Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization
Two main methods for exploring patterns in data are data visualization and machine learning.
Data Visualization
Applications
Fitting A Mixture Distribution to Data: Tutorial
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.
Blue Sky Ideas in Artificial Intelligence Education from the EAAI 2017 New and Future AI Educator Program
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).
