9 citations · 40 across the 14 of their papers we have counts for
14 papers
ATOM: Attention Mixer for Efficient Dataset Distillation
Samir Khaki, Ahmad Sajedi, Kai Wang +3
Recent works in dataset distillation seek to minimize training expenses by generating a condensed synthetic dataset that encapsulates the information present in a larger real datas…
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
Ahmad Sajedi, Samir Khaki, Yuri A. Lawryshyn +1
Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and tra…
DataDAM: Efficient Dataset Distillation with Attention Matching
Ahmad Sajedi, Samir Khaki, Ehsan Amjadian +3
Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation a…
A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction
Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
This paper presents a new distance metric to compare two continuous probability density functions. The main advantage of this metric is that, unlike other statistical measurements,…
Subclass Knowledge Distillation with Known Subclass Labels
Ahmad Sajedi, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
This work introduces a novel knowledge distillation framework for classification tasks where information on existing subclasses is available and taken into consideration. In classi…
Statistical network isomorphism
Pierre Miasnikof, Alexander Y. Shestopaloff, Cristián Bravo +1
Graph isomorphism is a problem for which there is no known polynomial-time solution. Nevertheless, assessing (dis)similarity between two or more networks is a key task in many area…
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis +1
Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real w…
Energy-based Surprise Minimization for Multi-Agent Value Factorization
Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis +1
Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success in training decentralised policies in a centralised manner by making use of value factorization metho…
Implicit Feedback Deep Collaborative Filtering Product Recommendation System
Karthik Raja Kalaiselvi Bhaskar, Deepa Kundur, Yuri Lawryshyn
In this paper, several Collaborative Filtering (CF) approaches with latent variable methods were studied using user-item interactions to capture important hidden variations of the…
Maximum Mutation Reinforcement Learning for Scalable Control
Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis +1
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance. Genetic methods, on the o…
Market Graph Clustering Via QUBO and Digital Annealing
Seo Hong, Pierre Miasnikof, Roy Kwon +1
Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our…
Graph Distances and Clustering
Pierre Miasnikof, Alexander Y. Shestopaloff, Leonidas Pitsoulis +1
With a view on graph clustering, we present a definition of vertex-to-vertex distance which is based on shared connectivity. We argue that vertices sharing more connections are clo…
Graph Clustering Via QUBO and Digital Annealing
Pierre Miasnikof, Seo Hong, Yuri Lawryshyn
This article empirically examines the computational cost of solving a known hard problem, graph clustering, using novel purpose-built computer hardware. We express the graph cluste…
A Statistical Density-Based Analysis of Graph Clustering Algorithm Performance
Pierre Miasnikof, Alexander Y. Shestopaloff, Anthony J. Bonner +2
Measuring graph clustering quality remains an open problem. To address it, we introduce quality measures based on comparisons of intra- and inter-cluster densities, an accompanying…