Vinyals, Oriol

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Flamingo: a visual language model for few-shot learning

Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35

Domain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

Cited by 966

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingNatural Language Processing Techniques

Cited by 932

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 929

Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 875

Deep Audio-Visual Speech Recognition

Afouras, T., Chung, J. S., Senior, A., Vinyals, O., & Zisserman, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Speech and Audio ProcessingSpeech Recognition and Synthesis

Cited by 801

Generating diverse high-fidelity images with vq-vae-2

Razavi, A., Oord, A. V. D., & Vinyals, O. (2019) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 709

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 651

Universal Transformers

Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., & Kaiser, Ł. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 467

Grammar as a Foreign Language

Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., & Hinton, G. (2014) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 464

Perceiver: General Perception with Iterative Attention

Jaegle, A., Gimeno, F., Brock, A., Zisserman, A., Vinyals, O., & Carreira, J. (2021) · arXiv

Advanced Neural Network ApplicationsMultimodal Machine Learning Applications

Cited by 414

Improving language models by retrieving from trillions of tokens

Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Driessche, G. V. D., Lespiau, J.-B., Damoc, B., Clark, A., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 382

Multimodal Few-Shot Learning with Frozen Language Models

Tsimpoukelli, M., Menick, J., Cabi, S., Eslami, S. M. A., Vinyals, O., & Hill, F. (2021) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 355

Qualitatively characterizing neural network optimization problems

Goodfellow, I. J., Vinyals, O., & Saxe, A. M. (2014) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 341

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

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

Population Based Training of Neural Networks

Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al. (2017) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 284

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 235

Faster sorting algorithms discovered using deep reinforcement learning

Mankowitz, D. J., Michi, A., Zhernov, A., Gelmi, M., Selvi, M., Paduraru, C., Leurent, E., Iqbal, S., Lespiau, J.-B., Ahern, A., et al. (2023) · Nature

Evolutionary Algorithms and ApplicationsArtificial Intelligence in Games

Cited by 177

GraphCast: Learning skillful medium-range global weather forecasting

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., et al. (2022) · arXiv

Meteorological Phenomena and SimulationsHydrological Forecasting Using AI

Cited by 154

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

Bayesian Recurrent Neural Networks

Fortunato, M., Blundell, C., & Vinyals, O. (2017) · arXiv

Gaussian Processes and Bayesian InferenceTopic Modeling

Cited by 145

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69