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[学科前沿] 今年物理学诺奖者的发表论文下载 [推广有奖]

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dnq 发表于 2024-10-10 16:30:41 |只看作者 |坛友微信交流群|倒序 |AI写论文

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如需批Geoffrey E. Hinton's Publications

in Reverse Chronological Order

2022  2021  2020  2019  2018  2017  2016  2015  2014  2013  2012  2011  2010  2009  2008  2007  2006  2005  2004  2003  2002  2001  2000  1999  1998  1997  1996  1995  1994  1993  1992  1991  1990  1989  1988  1987  1986  1985  1984  1983-1976

2022Hinton, G. E.
The Forward-Forward Algorithm: Some Preliminary Investigations
[pdf]
2022Chen, T., Zhang, R., & Hinton, G.
Analog bits: Generating discrete data using diffusion models with self-conditioning
arXiv preprint arXiv:2208.04202 [pdf]
2022Ren, M., Kornblith, S., Liao, R., & Hinton, G.
Scaling Forward Gradient With Local Losses
arXiv preprint arXiv:2210.03310 [pdf]
2022Chen, T., Saxena, S., Li, L., Lin, T. Y., Fleet, D. J., & Hinton, G.
A unified sequence interface for vision tasks
arXiv preprint arXiv:2206.07669 [pdf]
2022Chen, T., Li, L., Saxena, S., Hinton, G., & Fleet, D. J.
A generalist framework for panoptic segmentation of images and videos
arXiv preprint arXiv:2210.06366 [pdf]
2022Liao, R., Kornblith, S., Ren, M., Fleet, D. J., & Hinton, G.
Gaussian-Bernoulli RBMs Without Tears
arXiv preprint arXiv:2210.10318 [pdf]
2022Culp, L., Sabour, S., & Hinton, G. E.
Testing GLOM's ability to infer wholes from ambiguous parts
arXiv preprint arXiv: 2211.16564 [pdf]
2021Hinton, G. E.
How to represent part-whole hierarchies in a neural network
[pdf]
2021Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., & Hinton, G. E.
Neural additive models:Interpretable machine learning with neural nets
Advances in Neural Information Processing Systems, 34, 4699-4711. [pdf]
2021Bengio, Y., Lecun, Y., & Hinton, G.
Deep learning for AI
Communications of the ACM, 64(7), 58-65. [pdf]
2021Sun, W., Tagliasacchi, A., Deng, B., Sabour, S., Yazdani, S., Hinton, G. E., Yi, K. M.
Canonical Capsules: Unsupervised Capsules in Canonical Pose
arXiv preprint arXiv:2012.04718 [pdf]
2021Sabour, S., Tagliasacchi, A., Yazdani, S., Hinton, G. E., Fleet, D. J.
Unsupervised part representation by Flow Capsules
arXiv preprint arXiv:2011.13920 [pdf]
2020Deng, B., Lewis, J. P., Jeruzalski, T., Pons-Moll, G., Hinton, G. E., Norouzi, M., Tagliasacchi, A.
NASA: Neural Articulated Shape Approximation
ECCV [pdf]
2020Müller, R., Kornblith, S., Hinton, G. E.
Subclass distillation
arXiv preprint arXiv:2002.03936 [pdf]
2020Chen, T., Kornblith, S., Swersky, K., Norouzi, M., and Hinton, G. E.
Big Self-Supervised Models are Strong Semi-Supervised Learners
34th Conference on Neural Information Processing Systems (NeurIPS 2020)
[pdf]
2020Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. E.
A Simple Framework for Contrastive Learning of Visual Representations
Proceedings of the 37th International Conference on Machine Learning.
Eds. Hal Daume III and Aarti Singh, pp 1597--1607. [pdf]
2020Lillicrap, T. P., Santoro, A., Marris, C. J,. Akerman, C., and Hinton, G. E.
Backpropagation and the Brain
Nature Reviews Neuroscience volume 21, pp 335--346. [pdf]
2020Qin, Y., Frosst, N., Sabour, S., Raffel, C., Cottrell, C. and Hinton, G.
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
ICLR-2020 [pdf]
2019Kosiorek, A. R., Sabour, S., Teh, Y. W. and Hinton, G. E.
Stacked Capsule Autoencoders
Advances in Neural Information Processing Systems 32 [pdf]
2019Zhang, M., Lucas, J., Ba, J., and Hinton, G. E.
Lookahead Optimizer: k steps forward, 1 step back
Advances in Neural Information Processing Systems 32 [pdf]
2019Müller, R., Kornblith, S. and Hinton G.
When Does Label Smoothing Help?
Advances in Neural Information Processing Systems 32 [pdf]
2019Deng, B., Kornblith, S. and Hinton, G. (2019)
Cerberus: A multi-headed derenderer
3D Scene Understanding Workshop, CVPR 2019 [pdf]
2019Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G. and Tagliasacchi, A. (2019)
Cvxnet: Learnable convex decomposition
Perception as Generative Reasoning Workshop, NeurIPS 2019 [pdf]
2019Kornblith, S., Norouzi, M., Lee, H. and Hinton, G.
Similarity of neural network representations revisited
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