Search Results for author:
Found 60 papers, 27 papers with code
GenLayNeRF: Generalizable Layered Representations with 3D Model Alignment for Multi-Human View Synthesis
Generalizable human view synthesis methods combine the pre-fitted 3D human meshes with image features to reach generalization, yet they are mainly designed to operate on single-human scenes.
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation
We perform further validation on real English dysarthric speech showing a WER improvement of 124% compared to the baseline trained only on healthy English LJSpeech dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Clip21: Error Feedback for Gradient Clipping
Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipping, i. e., clipping applied to the gradients computed from local information at the nodes.
Multi-Plane Neural Radiance Fields for Novel View Synthesis
Multi-plane Neural Radiance Fields (MINE) open the door for combining implicit and explicit scene representations.
Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments
Although IoT applications are helpful in smart building applications, they present a real risk as the large number of interconnected devices in those buildings, using heterogeneous networks, increases the number of potential IoT attacks.
Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse
In this article, we discuss some of the key issues required in order to attain realization of metaverse services.
Internet of Things Device Capabilities, Architectures, Protocols, and Smart Applications in Healthcare Domain: A Review
In this paper, the most common and popular IoT device capabilities, architectures, and protocols are demonstrated in brief to provide a clear overview of the IoT technology to the researchers in this area.
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 $\{1, 2, \dots, d-1\}$.
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects
Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices.
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.
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.
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....
Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior
The scenario results showed a decrease of 522. 2 kW of active power when compared to original consumption over a 200-hours period.
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.
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.
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.
Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects
The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems.
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.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
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.
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters
Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling.
On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)
Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies.
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.
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.
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.
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Then, Sammon mapping, Isomap, and kernel Isomap are explained.
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years.
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.
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.
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
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.
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.
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.
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.
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.
Fisher and Kernel Fisher Discriminant Analysis: Tutorial
This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA.
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.
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems.
BIG-bench Machine Learning
Matrix Factorization / Decomposition
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.
SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction
This paper presents a practical approach for detecting non-stationarity in time series prediction.
New Results on Multi-Step Traffic Flow Prediction
Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest.
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion
The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and class labels is maximized.
Driver distraction detection and recognition using RGB-D sensor
Based on active sensor Kinect and computer vision tools, we have built an efficient module for detecting driver distraction and recognizing the type of distraction.
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means.
