20 citations · 42 across the 14 of their papers we have counts for
14 papers
GUESS: Generative Uncertainty Ensemble for Self Supervision
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self-supervised learning (SSL) frameworks consist of pretext task, and loss function aiming to learn useful general features from unlabeled data. The basic idea of most SSL baselin…
Rethinking Self-Supervised Learning Within the Framework of Partial Information Decomposition
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self Supervised learning (SSL) has demonstrated its effectiveness in feature learning from unlabeled data. Regarding this success, there have been some arguments on the role that m…
Direct Coloring for Self-Supervised Enhanced Feature Decoupling
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decouplin…
Heteroskedasticity as a Signature of Association for Age-Related Genes
Salman Mohamadi, Donald A. Adjeroh
Human aging is a process controlled by both genetics and environment. Many studies have been conducted to identify a subset of genes related to aging from the human genome. Biologi…
ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey
Salman Mohamadi, Ghulam Mujtaba, Ngan Le +2
ChatGPT is a large language model (LLM) created by OpenAI that has been carefully trained on a large amount of data. It has revolutionized the field of natural language processing…
More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self-supervised learning (SSL) is now a serious competitor for supervised learning, even though it does not require data annotation. Several baselines have attempted to make SSL mo…
FUSSL: Fuzzy Uncertain Self Supervised Learning
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Self supervised learning (SSL) has become a very successful technique to harness the power of unlabeled data, with no annotation effort. A number of developed approaches are evolvi…
Deep Active Ensemble Sampling For Image Classification
Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
Conventional active learning (AL) frameworks aim to reduce the cost of data annotation by actively requesting the labeling for the most informative data points. However, introducin…
Human Age Estimation from Gene Expression Data using Artificial Neural Networks
Salman Mohamadi, Gianfranco. Doretto, Nasser M. Nasrabadi +1
The study of signatures of aging in terms of genomic biomarkers can be uniquely helpful in understanding the mechanisms of aging and developing models to accurately predict the age…
An Information-Theoretic Framework for Identifying Age-Related Genes Using Human Dermal Fibroblast Transcriptome Data
Salman Mohamadi, Donald Adjeroh
Investigation of age-related genes is of great importance for multiple purposes, for instance, improving our understanding of the mechanism of ageing, increasing life expectancy, a…
Deep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition
Moktari Mostofa, Salman Mohamadi, Jeremy Dawson +1
In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at d…
Deep Bayesian Active Learning, A Brief Survey on Recent Advances
Salman Mohamadi, Hamidreza Amindavar
Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of l…
A New Framework For Spatial Modeling And Synthesis of Genome Sequence
Salman Mohamadi, Farhang Yeganegi, Hamidreza Amindavar
This paper provides a framework in order to statistically model sequences from human genome, which is allowing a formulation to synthesize gene sequences. We start by converting th…
Detection and Statistical Modeling of Birth-Death Anomaly
Salman Mohamadi, Farhang Yeganegi, Nasser M Nasrabadi
Generally, anomaly detection has a great importance particularly in applied statistical signal processing. Here we provide a general framework in order to detect anomaly through th…