De, Soham

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 395

Advancing regulatory variant effect prediction with AlphaGenome

Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature

Genomics and Chromatin DynamicsGenetic Associations and Epidemiology

Cited by 373

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Resurrecting Recurrent Neural Networks for Long Sequences

Orvieto, A., Smith, S. L., Gu, A., Fernando, A., Gulcehre, C., Pascanu, R., & De, S. (2023) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 76

BYOL works even without batch statistics

Richemond, P. H., Grill, J.-B., Altché, F., Tallec, C., Strub, F., Brock, A., Smith, S., De, S., Pascanu, R., Piot, B., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 51

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Botev, A., De, S., Smith, S. L., Fernando, A., Muraru, G.-C., Haroun, R., Berrada, L., Pascanu, R., Sessa, P. G., Dadashi, R., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

A study on the plasticity of neural networks

Berariu, T., Czarnecki, W., De, S., Bornschein, J., Smith, S., Pascanu, R., & Clopath, C. (2021) · arXiv

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 3