Debaditya Shome

According to our database, Debaditya Shome authored at least 10 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-series.

Debaditya Shome, Nasim Montazeri Ghahjaverestan, Ali Etemad

CoRR, 2024

Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations.

Debaditya Shome, Ali Etemad

Proceedings of the IEEE International Conference on Acoustics, 2024

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation.

Debaditya Shome, Pritam Sarkar, Ali Etemad

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

EXnet: Efficient In-context Learning for Data-less Text classification.

Debaditya Shome, Kuldeep Yadav

CoRR, 2023

2022

Federated learning and next generation wireless communications: A survey on bidirectional relationship.

Debaditya Shome, Omer Waqar, Wali Ullah Khan

Trans. Emerg. Telecommun. Technol., 2022

2021

Sum rate maximization of D2D networks with energy constrained UAVs through deep unsupervised learning.

Benjamin Lea, Debaditya Shome, Omer Waqar, Jabed H. Tomal

Proceedings of the 12th IEEE Annual Ubiquitous Computing, 2021

FedAffect: Few-shot federated learning for facial expression recognition.

Debaditya Shome, Tejaswini Kar

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

EmoHinD: Fine-grained multilabel emotion recognition from Hindi texts with Deep learning.

Debaditya Shome

Proceedings of the 12th International Conference on Computing Communication and Networking Technologies, 2021

Deep Q-learning for 5G network slicing with diverse resource stipulations and dynamic data traffic.

Debaditya Shome, Ankit Kudeshia

Proceedings of the International Conference on Artificial Intelligence in Information and Communication, 2021

ConOffense: Multi-modal multitask Contrastive learning for offensive content identification.

Debaditya Shome, Tejaswini Kar

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021