Publications
My research contributions in AI, machine learning, and computer vision
Research Papers
Mobile User-Activity Prediction Utilizing LSTM Recurrent Neural Network
R. Sharifi, M. M. Majdabadi and V. Tabataba Vakili
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), 2019, pp. 1-7
DOI: 10.1109/PACRIM47961.2019.8985068
Zero-skipping in CapsNet. Is it worth it?
R. Sharifi, P. Shiri, and A. Baniasadi
CATA 2020, 2020, pp. 355–361
Quick-CapsNet (QCN): A Fast Alternative to Capsule Networks
P. Shiri, R. Sharifi, and A. Baniasadi
2020 IEEE/ACS 17th International Conference on Computer Systems and Applications (AICCSA), 2020, pp. 1–7
DOI: 10.1109/AICCSA50499.2020.9316525
PrunedCaps: A case for Primary Capsules Discrimination
R. Sharifi, P. Shiri, and A. Baniasadi
ICMLA 2021, 2021
Resource-Aware Capsule Network
Shiri, P., Sharifi, R., Baniasadi, A.
Deep Learning Applications, Volume 4. Advances in Intelligent Systems and Computing, vol 1434. Springer, Singapore, 2023
Research Interests
My research focuses on advancing the fields of artificial intelligence, machine learning, and computer vision. I'm particularly interested in developing resource-efficient deep learning models and exploring innovative approaches to neural network architecture design.
Current Research Focus
- Large Language Models for code generation
- Neural network compression and pruning techniques
- Capsule network optimization
- Resource-aware deep learning
Applied Research Areas
- Software development automation
- Computer vision for aerial imagery analysis
- Anomaly detection in telecommunications
- Edge computing for ML applications