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Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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Gradient-based learning applied to document recognition
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Collective Classification in Network Data
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Learning multiple layers of features from tiny images
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What makes different people’s representations alike: neural similarity space solves the problem of across-subject fmri decoding
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Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
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Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E. Hopcroft · 2016
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Instance normalization: The missing ingredient for fast stylization
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Prototypical networks for few-shot learning
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Attention is all you need
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Insights on representational similarity in neural networks with canonical correlation
Ari S. Morcos, Maithra Raghu, and Samy Bengio · 2018
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The implicit bias of gradient descent on separable data
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Low-dimensional structure in the space of language representations is reflected in brain responses
Richard Antonello, Javier S Turek, Vy Vo, and Alexander Huth · 2021
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Revisiting model stitching to compare neural representations
Yamini Bansal, Preetum Nakkiran, and Boaz Barak · 2021
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Cores: Compatible representations via stationarity
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Similarity and matching of neural network representations, 2021
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PyTorch Lightning, 2019
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Experiment tracking with weights and biases, 2020
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GrokAI · 2021
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Representation topology divergence: A method for comparing neural network representations
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