Mina Rezaei

Orcid: 0000-0001-6994-6345

According to our database1, Mina Rezaei authored at least 43 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
FinerCut: Finer-grained Interpretable Layer Pruning for Large Language Models.
CoRR, 2024

Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Attention-Driven Dropout: A Simple Method to Improve Self-supervised Contrastive Sentence Embeddings.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

Uncertainty-Aware Vision Transformers for Medical Image Analysis.
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2024

Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Hyperbolic Contrastive Learning for Document Representations - A Multi-View Approach with Paragraph-level Similarities.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023
Deep Bregman divergence for self-supervised representations learning.
Comput. Vis. Image Underst., October, 2023

Stochastic Vision Transformers with Wasserstein Distance-Aware Attention.
CoRR, 2023

AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments.
CoRR, 2023

Probabilistic Self-supervised Learning via Scoring Rules Minimization.
CoRR, 2023

Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning.
CoRR, 2023

A Dual-Perspective Approach to Evaluating Feature Attribution Methods.
CoRR, 2023

Young Humans Make Change, Young Users Click: Creating Youth-Centered Networked Social Movements.
CoRR, 2023

Uncertainty Quantification for Deep Learning Models Predicting the Regulatory Activity of DNA Sequences.
Proceedings of the International Conference on Machine Learning and Applications, 2023

Neural Architecture Search for Genomic Sequence Data.
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2023

Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
Joint Debiased Representation and Image Clustering Learning with Self-Supervision.
CoRR, 2022

Evaluating User Experience in Literary and Film Geography-based Apps with a Cartographical User-Centered Design Lens.
CoRR, 2022

Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision.
CoRR, 2022

Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models.
CoRR, 2022

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection.
CoRR, 2022

FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep variational clustering framework for self-labeling large-scale medical images.
Proceedings of the Medical Imaging 2022: Image Processing, 2022

Bayesian uncertainty estimation for detection of long-tail and unseen conditions in abdominal images.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

Joint Debiased Representation Learning and Imbalanced Data Clustering.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2022

2021
Deep Bregman Divergence for Contrastive Learning of Visual Representations.
CoRR, 2021

Learning Statistical Representation with Joint Deep Embedded Clustering.
CoRR, 2021

2020
Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation.
Multim. Tools Appl., 2020

Generative multi-adversarial network for striking the right balance in abdominal image segmentation.
Int. J. Comput. Assist. Radiol. Surg., 2020

Generative synthetic adversarial network for internal bias correction and handling class imbalance problem in medical image diagnosis.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020

2019
Deep representation learning from imbalanced medical imaging
PhD thesis, 2019

Conditional Generative Adversarial Refinement Networks for Unbalanced Medical Image Semantic Segmentation.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2019

Learning imbalanced semantic segmentation through cross-domain relations of multi-agent generative adversarial networks.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 2019

2018
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data.
CoRR, 2018

Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation.
CoRR, 2018

voxel-GAN: Adversarial Framework for Learning Imbalanced Brain Tumor Segmentation.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018

Instance Tumor Segmentation using Multitask Convolutional Neural Network.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Generative Adversarial Framework for Learning Multiple Clinical Tasks.
Proceedings of the 2018 Digital Image Computing: Techniques and Applications, 2018

Whole Heart and Great Vessel Segmentation with Context-aware of Generative Adversarial Networks.
Proceedings of the Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme, 2018

2017
Deep Learning for Medical Image Analysis.
CoRR, 2017

Brain Abnormality Detection by Deep Convolutional Neural Network.
CoRR, 2017

A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor.
Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017

Deep Neural Network with l2-Norm Unit for Brain Lesions Detection.
Proceedings of the Neural Information Processing - 24th International Conference, 2017


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