Smith, Samuel L

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

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The future of quantum biology

Marais, A., Adams, B., Ringsmuth, A. K., Ferretti, M., Gruber, J. M., Hendrikx, R., Schuld, M., Smith, S. L., Sinayskiy, I., Krüger, T. P. J., et al. (2018) · Journal of The Royal Society Interface

Spectroscopy and Quantum Chemical StudiesBiofield Effects and Biophysics

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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

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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

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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

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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

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