Bengio, Yoshua

Training deep neural networks with low precision multiplications

Courbariaux, M., Bengio, Y., & David, J.-P. (2014) · arXiv

Numerical Methods and AlgorithmsAdvanced Neural Network Applications

Cited by 407

Combining modality specific deep neural networks for emotion recognition in video

Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction

Emotion and Mood RecognitionFace recognition and analysis

Cited by 391

Hierarchical Multiscale Recurrent Neural Networks

Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 386

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses

Lowe, R., Noseworthy, M., Serban, I. V., Angelard-Gontier, N., Bengio, Y., & Pineau, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 341

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys

Climate Change and Environmental ImpactArtificial Intelligence Applications

Cited by 336

HeMIS: Hetero-Modal Image Segmentation

Havaei, M., Guizard, N., Chapados, N., & Bengio, Y. (2016) · Lecture Notes in Computer Science

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 332

Generalized Denoising Auto-Encoders as Generative Models

Bengio, Y., Yao, L., Alain, G., & Vincent, P. (2013) · arXiv

Neural Networks and ApplicationsGaussian Processes and Bayesian Inference

Cited by 324

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Brundage, M., Avin, S., Wang, J., Belfield, H., Krueger, G., Hadfield, G., Khlaaf, H., Yang, J., Toner, H., Fong, R., et al. (2020) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 306

Inductive biases for deep learning of higher-level cognition

Goyal, A., & Bengio, Y. (2022) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

Domain Adaptation and Few-Shot LearningPsychiatry, Mental Health, Neuroscience

Cited by 300

Learning deep physiological models of affect

Martinez, H. P., Bengio, Y., & Yannakakis, G. N. (2013) · IEEE Computational Intelligence Magazine

Emotion and Mood RecognitionContext-Aware Activity Recognition Systems

Cited by 287

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Serban, I. V., García-Durán, A., Gulcehre, C., Ahn, S., Chandar, S., Courville, A., & Bengio, Y. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 280

BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices

Wagstyl, K., Larocque, S., Cucurull, G., Lepage, C., Cohen, J. P., Bludau, S., Palomero-Gallagher, N., Lewis, L. B., Funck, T., Spitzer, H., et al. (2020) · PLOS Biology

Advanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation Techniques

Cited by 218

Disentangling Factors of Variation for Facial Expression Recognition

Rifai, S., Bengio, Y., Courville, A., Vincent, P., & Mirza, M. (2012) · Lecture Notes in Computer Science

Emotion and Mood RecognitionFace and Expression Recognition

Cited by 215

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

Subramanian, S., Trischler, A., Bengio, Y., & Pal, C. J. (2018) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 207

Global optimization of a neural network-hidden Markov model hybrid

Bengio, Y., De Mori, R., Flammia, G., & Kompe, R. (1992) · IEEE Transactions on Neural Networks

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 203

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S. M., Frith, C., Ji, X., et al. (2023) · arXiv

Philosophy and Theoretical SciencePsychiatry, Mental Health, Neuroscience

Cited by 196

Machine learning for combinatorial optimization: A methodological tour d'horizon

Bengio, Y., Lodi, A., & Prouvost, A. (2021) · European Journal of Operational Research

Complexity and Algorithms in GraphsStochastic Gradient Optimization Techniques

Cited by 195

On the Expressive Power of Deep Architectures

Bengio, Y., & Delalleau, O. (2011) · Lecture Notes in Computer Science

Neural Networks and ApplicationsMachine Learning and Algorithms

Cited by 182

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Bengio, Y., Deleu, T., Rahaman, N., Ke, R., Lachapelle, S., Bilaniuk, O., Goyal, A., & Pal, C. (2019) · arXiv

Bayesian Modeling and Causal InferenceExplainable Artificial Intelligence (XAI)

Cited by 182

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

Nguyen, E., Poli, M., Faizi, M., Thomas, A., Birch-Sykes, C., Wornow, M., Patel, A., Rabideau, C., Massaroli, S., Bengio, Y., et al. (2023) · arXiv

Machine Learning in BioinformaticsGenomics and Phylogenetic Studies

Cited by 168

The Consciousness Prior

Bengio, Y. (2017) · arXiv

Topic ModelingPhilosophy and Theoretical Science

Cited by 155

On the number of response regions of deep feed forward networks with piece-wise linear activations

Pascanu, R., Montufar, G., & Bengio, Y. (2013) · arXiv

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 153

Artificial Neural Networks Applied to Taxi Destination Prediction

de Brébisson, A., Simon, É., Auvolat, A., Vincent, P., & Bengio, Y. (2015) · arXiv

Traffic Prediction and Management TechniquesData Management and Algorithms

Cited by 137