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Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions.
The effects of neural resource constraints on early visual representations ,
J. Lindsey, S. A. Ocko, S. Ganguli and S. Deny, · 1901
Earlier work this paper cites.
wav2vec: Unsupervised pre-training for speech recognition (2019), https://arxiv.org/abs/1904.05862
S. Schneider, A. Baevski, R. Collobert and M. Auli, · 1904
Earlier work this paper cites.
RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019), https://arxiv.org/abs/1907.11692
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer and V. Stoyanov, · 1907
Earlier work this paper cites.
S. Kuutti, R. Bowden, Y. Jin, P. Barber and S. Fallah, · 1912
Earlier work this paper cites.
Improved protein structure prediction using potentials from deep learning ,
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen et al. , · 1923
Earlier work this paper cites.
The Nature of Explanation ,
K. Craik, · 1943
Earlier work this paper cites.
The existence of persistent states in the brain ,
W. Little, · 1974
Earlier work this paper cites.
Neural networks and physical systems with emergent collective computational abilities ,
J. J. Hopfield, · 1982
Earlier work this paper cites.
Optimal perceptual inference ,
G. E. Hinton and T. J. Sejnowski, · 1983
Earlier work this paper cites.
Self-organizing neural network that discovers surfaces in random-dot stereograms ,
S. Becker and G. E. Hinton, · 1992
Earlier work this paper cites.
Image segmentation using deep learning: A survey ,
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, · 2001
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence ,
G. E. Hinton, · 2002
Earlier work this paper cites.
Learning a similarity metric discriminatively, with application to face verification ,
S. Chopra, R. Hadsell and Y. LeCun, · 2005
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching ,
A. Hyvärinen, · 2005
Earlier work this paper cites.
A. Conneau, A. Baevski, R. Collobert, A. Mohamed and M. Auli, · 2006
Earlier work this paper cites.
A tutorial on energy-based learning ,
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato and F. J. Huang, · 2006
Earlier work this paper cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. H. Richemond, E. Buchatskaya, C. Doersch, B. A. Pires, Z. D. Guo, M. G. Azar, B. Piot, K. Kavukcuoglu et al. , · 2006
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders ,
P. Vincent, H. Larochelle, Y. Bengio and P.-A. Manzagol, · 2008
Earlier work this paper cites.
An introduction to electrocatalyst design using machine learning for renewable energy storage ,
C. L. Zitnick, L. Chanussot, A. Das, S. Goyal, J. Heras-Domingo, C. Ho, W. Hu, A. P. Thibaut Lavril and, M. Riviere, M. Shuaibi, A. Sriram, K. Tran et al. , · 2010
Earlier work this paper cites.
Thinking, Fast and Slow ,
D. Kahneman, · 2011
Earlier work this paper cites.
Natural language processing (almost) from scratch ,
R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa, · 2011
Cited alongside, same era.
Exploring simple Siamese representation learning (2020), https://arxiv.org/abs/2011.10566
X. Chen and K. He, · 2011
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks ,
A. Krizhevsky, I. Sutskever and G. E. Hinton, · 2012
Cited alongside, same era.
Overfeat: Integrated recognition, localization and detection using convolutional networks ,
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus and Y. LeCun, · 2014
Cited alongside, same era.
Mastering the game of Go with deep neural networks and tree search ,
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe et al. , · 2016
BERT: Pre-training of deep bidirectional transformers for language understanding ,
J. Devlin, M.-W. Chang, K. Lee and K. Toutanova, · 2019
Later among the works it cites.
Deep learning (ds-ga 1008) ,
Y. LeCun and A. Canziani, · 2020
Later among the works it cites.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises ,
S. K. Zhou, H. Greenspan, C. Davatzikos, J. S. Duncan, B. Van Ginneken, A. Madabhushi, J. L. Prince, D. Rueckert and R. M. Summers, · 2021
Later among the works it cites.
Accurate prediction of protein structures and interactions using a three-track neural network ,
M. Baek, F. DiMaio, I. Anishchenko, J. Dauparas, S. Ovchinnikov, G. R. Lee, J. Wang, Q. Cong, L. N. Kinch, R. D. Schaeffer, C. Millán, H. Park et al. , · 2021
Later among the works it cites.
Review on model predictive control: an engineering perspective ,
M. Schwenzer, M. Ay, T. Bergs and D. Abel, · 2021
Later among the works it cites.
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Cited alongside, same era.
Energy-based generative adversarial network (2016), https://arxiv.org/abs/1609.03126
J. Zhao, M. Mathieu and Y. LeCun, · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting (2016), https://arxiv.org/abs/1604.07379
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell and A. A. Efros, · 2016
Cited alongside, same era.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm ,
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. P. Lillicrap, K. Simonyan et al. , · 2017
Cited alongside, same era.
Wasserstein GAN (2017), https://arxiv.org/abs/1701.07875
M. Arjovsky, S. Chintala and L. Bottou, · 2017
Cited alongside, same era.
M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, B. C. Van Esesn, A. A. S. Awwal and V. K. Asari, · 2018
Cited alongside, same era.
fastMRI: An open dataset and benchmarks for accelerated MRI (2018), https://arxiv.org/abs/1811.08839
J. Zbontar, F. Knoll, A. Sriram, T. Murrell, Z. Huang, M. J. Muckley, A. Defazio, R. Stern, P. Johnson, M. Bruno, M. Parente, K. J. Geras et al. , · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding (2018), https://arxiv.org/abs/1807.03748
A. van den Oord, Y. Li, and O. Vinyals, · 2018
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár and R. Girshick, · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction ,
J. Zbontar, L. Jing, I. Misra, Y. LeCun and S. Deny, · 2021
Later among the works it cites.
NLLB Team, M. R. Costa-jussà, J. Cross, O. Çelebi, M. Elbayad, K. Heafield, K. Heffernan, E. Kalbassi, J. Lam, D. Licht, J. Maillard, A. Sun et al. , · 2022
Later among the works it cites.
Summer school on statistical physics and machine learning ,
F. Krzakala and L. Zdeborová, · 2022
Later among the works it cites.
A path towards autonomous machine intelligence ,
Y. LeCun, · 2022
Later among the works it cites.
New rules to improve road safety and enable fully driverless vehicles in the eu (2022), https://ec.europa.eu/commission/presscorner/detail/en/IP_22_4312
E. Commission, · 2022
Later among the works it cites.
Community standards enforcement report ,
Meta, · 2022
Later among the works it cites.
Accelerated MR screenings with direct k-space classification ,
R. Singhal, M. Sudarshan, L. Ginocchio, A. Tong, H. Chandarana, D. Sodickson, R. Ranganath and S. Chopra, · 2022
Later among the works it cites.
Modern applications of machine learning in quantum sciences (2022), https://arxiv.org/abs/2204.04198
A. Dawid, J. Arnold, B. Requena, A. Gresch, M. Płodzień, K. Donatella, K. A. Nicoli, P. Stornati, R. Koch, M. Büttner, R. Okuła, G. Muñoz-Gil et al. , · 2022
Later among the works it cites.
J. Hermann, J. Spencer, K. Choo, A. Mezzacapo, W. M. C. Foulkes, D. Pfau, G. Carleo and F. Noé, · 2022
Later among the works it cites.
The physics of energy-based models ,
P. Huembeli, J. M. Arrazola, N. Killoran, M. Mohseni and P. Wittek, · 2022
Later among the works it cites.
D. T. Hoffmann, N. Behrmann, J. Gall, T. Brox and M. Noroozi, · 2022
Later among the works it cites.
OPT: Open Pre-trained Transformer language models (2022), https://arxiv.org/abs/2205.01068
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. V. Lin, T. Mihaylov, M. Ott et al. , · 2022
Later among the works it cites.
VICReg: Variance-invariance-covariance regularization for self-supervised learning ,
A. Bardes, J. Ponce and Y. LeCun, · 2022
Later among the works it cites.