Publications (104)
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on unification of spectral dimensionality reduction methods, kernel learning by Semidefinite Programming (SDP), Maximum Variance Unfolding (MVU)…
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper for nonlinear dimensionality and feature extraction methods which are based on the Laplacian of graph of data. We first introduce adjacency matr…
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Jiahui Geng, Qing Li, Herbert Woisetschlaeger +6
This study investigates the machine unlearning techniques within the context of large language models (LLMs), referred to as \textit{LLM unlearning}. LLM unlearning offers a princi…
Generative Locally Linear Embedding
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embed…
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
Ahmed Elgohary, Ahmed K. Farahat, Mohamed S. Kamel +1
The kernel -means is an effective method for data clustering which extends the commonly-used -means algorithm to work on a similarity matrix over complex data structures. The…
ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions
Fatmah AlHindaassi, Mohammed Talha Alam, Fakhri Karray
Adverse weather conditions, particularly fog, pose a significant challenge to autonomous vehicles, surveillance systems, and other safety-critical applications by severely degradin…
Distributed Nonlinear Model Predictive Control and Metric Learning for Heterogeneous Vehicle Platooning with Cut-in/Cut-out Maneuvers
Mohammad Hossein Basiri, Benyamin Ghojogh, Nasser L. Azad +3
Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC)…
New Results on Multi-Step Traffic Flow Prediction
Arief Koesdwiady, Fakhri Karray
In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to…
SAUCE: Selective Concept Unlearning in Vision-Language Models with Sparse Autoencoders
Qing Li, Jiahui Geng, Derui Zhu +3
Unlearning methods for vision-language models (VLMs) have primarily adapted techniques from large language models (LLMs), relying on weight updates that demand extensive annotated…
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Ruochen Liu, Hao Chen, Yuanchen Bei +6
Large Language Model (LLM)-based cold-start recommendation systems continue to face significant computational challenges in billion-scale scenarios, as they follow a "Text-to-Judgm…
Clip21: Error Feedback for Gradient Clipping
Sarit Khirirat, Eduard Gorbunov, Samuel Horváth +3
Motivated by the increasing popularity and importance of large-scale training under differential privacy (DP) constraints, we study distributed gradient methods with gradient clipp…
Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang +10
AI agents have experienced a paradigm shift, from early dominance by reinforcement learning (RL) to the rise of agents powered by large language models (LLMs), and now further adva…
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
Parisa Abdolrahim Poorheravi, Benyamin Ghojogh, Vincent Gaudet +2
Metric learning is one of the techniques in manifold learning with the goal of finding a projection subspace for increasing and decreasing the inter- and intra-class variances, res…
Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of whic…
Principal Component Analysis Using Structural Similarity Index for Images
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. As it has b…
Fitting A Mixture Distribution to Data: Tutorial
Benyamin Ghojogh, Aydin Ghojogh, Mark Crowley +1
This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required bac…
CAST: Channel-Aware Spatial Transfer Learning with Pseudo-Image Radar for Sign Language Recognition
Md. Shakhoyat Rahman Shujon, Sheikh Md. Galib Mahim, Md. Milon Islam +4
We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the challenges of magnitude-only…
Y-Shaped Generative Flows
Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev +3
Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Al…
KineVLA: Towards Kinematics-Aware Vision-Language-Action Models with Bi-Level Action Decomposition
Gaoge Han, Zhengqing Gao, Ziwen Li +5
In this paper, we introduce a novel kinematics-rich vision-language-action (VLA) task, in which language commands densely encode diverse kinematic attributes (such as direction, tr…
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Consider a set of data points in the Euclidean space . This set is called dataset in machine learning and data science. Manifold hypothesis states that the datase…
Internet of Things Device Capabilities, Architectures, Protocols, and Smart Applications in Healthcare Domain: A Review
Md. Milon Islam, Sheikh Nooruddin, Fakhri Karray +1
Nowadays, the Internet has spread to practically every country around the world and is having unprecedented effects on people's lives. The Internet of Things (IoT) is getting more…
VulnScout-C: A Lightweight Transformer for C Code Vulnerability Detection
Aymen Lassoued, Nacef Mbarek, Bechir Dardouri +3
Vulnerability detection in C programs is a critical challenge in software security. Although large language models (LLMs) achieve strong detection performance, their multi-billion-…
FusionEnsemble-Net: An Attention-Based Ensemble of Spatiotemporal Networks for Multimodal Sign Language Recognition
Md. Milon Islam, Md Rezwanul Haque, S M Taslim Uddin Raju +1
Accurate recognition of sign language in healthcare communication poses a significant challenge, requiring frameworks that can accurately interpret complex multimodal gestures. To…
MDD-Net: Multimodal Depression Detection through Mutual Transformer
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a major mental health condition that severely impacts the emotional and physical well-being of individuals. The simple nature of data collection from social media pla…
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Generative models and inferential autoencoders mostly make use of norm in their optimization objectives. In order to generate perceptually better images, this short paper…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces
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 int…
Quantized Fisher Discriminant Analysis
Benyamin Ghojogh, Ali Saheb Pasand, Fakhri Karray +1
This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory. There is a…
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then…
Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse
Moayad Aloqaily, Ouns Bouachir, Fakhri Karray +2
The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with A…
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects
Md. Milon Islam, Sheikh Nooruddin, Fakhri Karray +1
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…
Sampling Algorithms, from Survey Sampling to Monte Carlo Methods: Tutorial and Literature Review
Benyamin Ghojogh, Hadi Nekoei, Aydin Ghojogh +2
This paper is a tutorial and literature review on sampling algorithms. We have two main types of sampling in statistics. The first type is survey sampling which draws samples from…
Fisher and Kernel Fisher Discriminant Analysis: Tutorial
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
This is a detailed tutorial paper which explains the Fisher discriminant Analysis (FDA) and kernel FDA. We start with projection and reconstruction. Then, one- and multi-dimensiona…
VSCBench: Bridging the Gap in Vision-Language Model Safety Calibration
Jiahui Geng, Qing Li, Zongxiong Chen +7
The rapid advancement of vision-language models (VLMs) has brought a lot of attention to their safety alignment. However, existing methods have primarily focused on model undersafe…
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
Benyamin Ghojogh, Maria N. Samad, Sayema Asif Mashhadi +4
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 rep…
Projected Gradient Unlearning for Text-to-Image Diffusion Models: Defending Against Concept Revival Attacks
Aljalila Aladawi, Mohammed Talha Alam, Fakhri Karray
Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining. Current unlearning methods…
Roweis Discriminant Analysis: A Generalized Subspace Learning Method
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA), is named after…
SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary Time Series Prediction
Arief Koesdwiady, Fakhri Karray
This paper presents a practical approach for detecting non-stationarity in time series prediction. This method is called SAFE and works by monitoring the evolution of the spectral…
AstroSpy: On detecting Fake Images in Astronomy via Joint Image-Spectral Representations
Mohammed Talha Alam, Raza Imam, Mohsen Guizani +1
The prevalence of AI-generated imagery has raised concerns about the authenticity of astronomical images, especially with advanced text-to-image models like Stable Diffusion produc…
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distanc…
AdaptPrompt: Parameter-Efficient Adaptation of VLMs for Generalizable Deepfake Detection
Yichen Jiang, Mohammed Talha Alam, Sohail Ahmed Khan +2
Recent advances in image generation have led to the widespread availability of highly realistic synthetic media, increasing the difficulty of reliable deepfake detection. A key cha…
Expert or not? assessing data quality in offline reinforcement learning
Arip Asadulaev, Fakhri Karray, Martin Takac
Offline reinforcement learning (RL) learns exclusively from static datasets, without further interaction with the environment. In practice, such datasets vary widely in quality, of…
In-Context Learning Operates as Concept Subspace Learning
Wei Tang, Xinyan Jiang, Fakhri Karray +1
Regression and Bayesian accounts of in-context learning (ICL) explain how demonstrations can induce predictors, while mechanistic analyses often identify compact activation directi…
Convex Compositional Reasoning Models
Meir Roketlishvili, Semyon Semenov, Maksim Bobrin +7
Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show…
REMONI: An Autonomous System Integrating Wearables and Multimodal Large Language Models for Enhanced Remote Health Monitoring
Thanh Cong Ho, Farah Kharrat, Abderrazek Abid +1
With the widespread adoption of wearable devices in our daily lives, the demand and appeal for remote patient monitoring have significantly increased. Most research in this field h…
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on factor analysis, probabilistic Principal Component Analysis (PCA), variational inference, and Variational Autoencoder (VAE). These methods, w…
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embed…
Weighted Fisher Discriminant Analysis in the Input and Feature Spaces
Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh +2
Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the…
Anomaly Detection and Prototype Selection Using Polyhedron Curvature
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. Using the nearest neighbors for…
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs
Qing Li, Jiahui Geng, Zongxiong Chen +5
In recent years, large language models (LLMs) have made remarkable advancements, yet hallucination, where models produce inaccurate or non-factual statements, remains a significant…
Reference-free Hallucination Detection for Large Vision-Language Models
Qing Li, Jiahui Geng, Chenyang Lyu +3
Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conver…
Zero-Shot Off-Policy Learning
Arip Asadulaev, Maksim Bobrin, Salem Lahlou +3
Off-policy learning methods seek to derive an optimal policy directly from a fixed dataset of prior interactions. This objective presents significant challenges, primarily due to t…
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial autoencoders, and their variants. We start with explaining adversarial learning and the van…
Addressing the Mystery of Population Decline of the Rose-Crested Blue Pipit in a Nature Preserve using Data Visualization
Benyamin Ghojogh, Mark Crowley, Fakhri Karray
Two main methods for exploring patterns in data are data visualization and machine learning. The former relies on humans for investigating the patterns while the latter relies on m…
Driver distraction detection and recognition using RGB-D sensor
Céline Craye, Fakhri Karray
Driver inattention assessment has become a very active field in intelligent transportation systems. Based on active sensor Kinect and computer vision tools, we have built an effici…
Bridging Brain with Foundation Models through Self-Supervised Learning
Hamdi Altaheri, Fakhri Karray, Md. Milon Islam +2
Foundation models (FMs), powered by self-supervised learning (SSL), have redefined the capabilities of artificial intelligence, demonstrating exceptional performance in domains lik…
Internet of Behavior (IoB) and Explainable AI Systems for Influencing IoT Behavior
Haya Elayan, Moayad Aloqaily, Fakhri Karray +1
Pandemics and natural disasters over the years have changed the behavior of people, which has had a tremendous impact on all life aspects. With the technologies available in each e…
GenLayNeRF: Generalizable Layered Representations with 3D Model Alignment for Multi-Human View Synthesis
Youssef Abdelkareem, Shady Shehata, Fakhri Karray
Novel view synthesis (NVS) of multi-human scenes imposes challenges due to the complex inter-human occlusions. Layered representations handle the complexities by dividing the scene…
NL-MambaXCT: Self-Supervised Nested-Learning Mamba for Nomex Honeycomb X-ray CT Defect Classification
Ghaleb Aldoboni, Lobna Nassar, Fakhri Karray +1
X-ray computed tomography (XCT) is widely used for non-destructive testing of Nomex honeycomb structures in aerospace manufacturing, but industrial inspection still relies heavily…
Locally Linear Image Structural Embedding for Image Structure Manifold Learning
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
Most of existing manifold learning methods rely on Mean Squared Error (MSE) or norm. However, for the problem of image quality assessment, these are not promising measure.…
Advances in Preference-based Reinforcement Learning: A Review
Youssef Abdelkareem, Shady Shehata, Fakhri Karray
Reinforcement Learning (RL) algorithms suffer from the dependency on accurately engineered reward functions to properly guide the learning agents to do the required tasks. Preferen…
FLARE up your data: Diffusion-based Augmentation Method in Astronomical Imaging
Mohammed Talha Alam, Raza Imam, Mohsen Guizani +1
The intersection of Astronomy and AI encounters significant challenges related to issues such as noisy backgrounds, lower resolution (LR), and the intricate process of filtering an…
Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches
Milad Sikaroudi, Benyamin Ghojogh, Amir Safarpoor +3
We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest an…
UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction
Ziwen Li, Jiaxin Huang, Runnan Chen +5
Reconstructing urban scenes is challenging due to their complex geometries and the presence of potentially dynamic objects. 3D Gaussian Splatting (3DGS)-based methods have shown st…
Eigenvalue and Generalized Eigenvalue Problems: Tutorial
Benyamin Ghojogh, Fakhri Karray, Mark Crowley
This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized ei…
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Md. Milon Islam, Fakhri Karray, Reda Alhajj +1
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. The…
On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)
Yingxu Wang, Fakhri Karray, Sam Kwong +12
Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine inter…
Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution
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 distribu…
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on Karush-Kuhn-Tucker (KKT) conditions, first-order and second-order numerical optimization, and distributed optimization. After a brief review…
Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion
Mehrdad J. Gangeh, Safaa M. A. Bedawi, Ali Ghodsi +1
In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learnin…
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation
Massa Baali, Ibrahim Almakky, Shady Shehata +1
Despite major advancements in Automatic Speech Recognition (ASR), the state-of-the-art ASR systems struggle to deal with impaired speech even with high-resource languages. In Arabi…
CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval
Jiahui Geng, Qing Li, Fengyu Cai +1
Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG), improving code discovery, reu…
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese Networks
Benyamin Ghojogh, Milad Sikaroudi, Sobhan Shafiei +3
Siamese neural network is a very powerful architecture for both feature extraction and metric learning. It usually consists of several networks that share weights. The Siamese conc…
PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention
Ziwen Li, Xin Wang, Hanlue Zhang +8
The Vision-Language-Action (VLA) models have demonstrated remarkable performance on embodied tasks and shown promising potential for real-world applications. However, current VLAs…
Internal Activation Revision: Safeguarding Vision Language Models Without Parameter Update
Qing Li, Jiahui Geng, Zongxiong Chen +3
Vision-language models (VLMs) demonstrate strong multimodal capabilities but have been found to be more susceptible to generating harmful content compared to their backbone large l…
A Signer-Invariant Conformer and Multi-Scale Fusion Transformer for Continuous Sign Language Recognition
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +1
Continuous Sign Language Recognition (CSLR) faces multiple challenges, including significant inter-signer variability and poor generalization to novel sentence structures. Traditio…
MMFformer: Multimodal Fusion Transformer Network for Depression Detection
Md Rezwanul Haque, Md. Milon Islam, S M Taslim Uddin Raju +3
Depression is a serious mental health illness that significantly affects an individual's well-being and quality of life, making early detection crucial for adequate care and treatm…
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Moloud Abdar, Soorena Salari, Sina Qahremani +7
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems th…
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters
Mohita Chaudhary, Mohamed Sadok Gastli, Lobna Nassar +1
Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alte…
Enhance Hyperbolic Representation Learning via Second-order Pooling
Kun Song, Ruben Solozabal, Li hao +5
Hyperbolic representation learning is well known for its ability to capture hierarchical information. However, the distance between samples from different levels of hierarchical cl…
Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of…
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on various methods for Sufficient Dimension Reduction (SDR). We cover these methods with both statistical high-dimensional regression perspectiv…
Dual Advantage Fields
Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin +5
Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons. Dual goal representations provide value fields that…
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Uniform Manifold Approximation and Projection (UMAP) is one of the state-of-the-art methods for dimensionality reduction and data visualization. This is a tutorial and survey paper…
Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings
Abderrazek Abid, Thanh-Cong Ho, Fakhri Karray
As generative AI continues to evolve, Vision Language Models (VLMs) have emerged as promising tools in various healthcare applications. One area that remains relatively underexplor…
Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, met…
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui +8
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance…
Latent Reasoning in TRMs is Secretly a Policy Improvement Operator
Arip Asadulaev, Rayan Banerjee, Fakhri Karray +1
Recently, small models with latent recursion have obtained promising results on complex reasoning tasks. These results are typically explained by the theory that such recursion inc…
Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects
Mahmoud Nasr, MD. Milon Islam, Shady Shehata +2
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…
Towards Robust Reinforcement Learning for Small-Scale Language Model Agents
Md Rezwanul Haque, Md. Milon Islam, Fakhri Karray
The alignment of Small Language Models (SLMs) in the 70--500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms ha…
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey
Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray +1
This is a tutorial and survey paper on the Johnson-Lindenstrauss (JL) lemma and linear and nonlinear random projections. We start with linear random projection and then justify its…
Magnification Generalization for Histopathology Image Embedding
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a use…
Large Language Model Simulator for Cold-Start Recommendation
Feiran Huang, Yuanchen Bei, Zhenghang Yang +6
Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely sol…
Multi-Plane Neural Radiance Fields for Novel View Synthesis
Youssef Abdelkareem, Shady Shehata, Fakhri Karray
Novel view synthesis is a long-standing problem that revolves around rendering frames of scenes from novel camera viewpoints. Volumetric approaches provide a solution for modeling…
PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
Md. Shakhoyat Rahman Shujon, MD Jahid Hasan Jim, Md. Milon Islam +2
We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We rede…
Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Definite Matrix Manifolds
Reza Godaz, Benyamin Ghojogh, Reshad Hosseini +3
This work concentrates on optimization on Riemannian manifolds. The Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm is a commonly used quasi-Newton method for num…
Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments
Neveen Hijazi, Moayad Aloqaily, Bassem Ouni +2
The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emer…
Batch-Incremental Triplet Sampling for Training Triplet Networks Using Bayesian Updating Theorem
Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray +2
Variants of Triplet networks are robust entities for learning a discriminative embedding subspace. There exist different triplet mining approaches for selecting the most suitable t…
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
S M Taslim Uddin Raju, Md. Milon Islam, Md Rezwanul Haque +2
Microscopic assessment of histopathology images is vital for accurate cancer diagnosis and treatment. Whole Slide Image (WSI) classification and captioning have become crucial task…
FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing
Mohammed Talha Alam, Fahad Shamshad, Fakhri Karray +1
Advancements in face recognition (FR) technologies have amplified privacy concerns, necessitating methods that protect identity while maintaining recognition utility. Existing face…