activity
20242026
collaborators

7 papers

cs.LG2026

Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning

Siddhant Dutta, Edward Tan Beng Wai, Soumick Sarker +2

Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to inte…

quant-ph2026

QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution

Siddhant Dutta, Nouhaila Innan, Khadijeh Najafi +2

Recent advancements in Single-Image Super-Resolution (SISR) using deep learning have significantly improved image restoration quality. However, the high computational cost of proce…

quant-ph2025

QAS-QTNs: Curriculum Reinforcement Learning-Driven Quantum Architecture Search for Quantum Tensor Networks

Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +1

Quantum Architecture Search (QAS) is an emerging field aimed at automating the design of quantum circuits for optimal performance. This paper introduces a novel QAS framework emplo…

quant-ph2025

MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption

Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia +2

The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often re…

cs.LG2025

Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources

Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier +2

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protect…

cs.IR2024

Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders

Saloua Zammali, Siddhant Dutta, Sadok Ben Yahia

In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve pr…