4 citations · 7 across the 37 of their papers we have counts for
8 papers · 1 filter
ReDiF: Resource-Efficient Few-Step Diffusion Distillation via Reinforcement Learning
Amirhossein Tighkhorshid, Zahra Dehghanian, Gholamali Aminian +2
Step distillation accelerates diffusion sampling by training a few-step student to imitate a many-step teacher, but distillation itself remains expensive. Typically, this requires…
Machine Learning and CPU (Central Processing Unit) Scheduling Co-Optimization over a Network of Computing Centers
Mohammadreza Doostmohammadian, Zulfiya R. Gabidullina, Hamid R. Rabiee
In the rapidly evolving research on artificial intelligence (AI) the demand for fast, computationally efficient, and scalable solutions has increased in recent years. The problem o…
Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning
Armin Behnamnia, Gholamali Aminian, Alireza Aghaei +3
Off-policy learning and evaluation leverage logged bandit feedback datasets, which contain context, action, propensity score, and feedback for each data point. These scenarios face…
UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification
Mohammad T. Teimuri, Zahra Dehghanian, Gholamali Aminian +1
Graph-structured datasets often suffer from class imbalance, which complicates node classification tasks. In this work, we address this issue by first providing an upper bound on p…
Cueless EEG imagined speech for subject identification: dataset and benchmarks
Ali Derakhshesh, Zahra Dehghanian, Reza Ebrahimpour +1
Electroencephalogram (EEG) signals have emerged as a promising modality for biometric identification. While previous studies have explored the use of imagined speech with semantica…
Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning
Mina Rafiei, Mohammadmahdi Maheri, Hamid R. Rabiee
Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally lea…
HGTDR: Advancing Drug Repurposing with Heterogeneous Graph Transformers
Ali Gharizadeh, Karim Abbasi, Amin Ghareyazi +2
Motivation: Drug repurposing is a viable solution for reducing the time and cost associated with drug development. However, thus far, the proposed drug repurposing approaches still…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…