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While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.
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Manifold alignment without correspondence
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Learning word vectors for sentiment analysis
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Scikit-learn: Machine learning in Python
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Representation learning: A review and new perspectives
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Testing the manifold hypothesis
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Representation learning: A review and new perspectives, 2014
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2014
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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
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Normalized word embedding and orthogonal transform for bilingual word translation
Chao Xing, Dong Wang, Chao Liu, and Yiye Lin · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun · 2015
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Convergent learning: Do different neural networks learn the same representations?
Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John E. Hopcroft · 2016
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Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung, Sergey Ioffe, and Saurabh Singh · 2017
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Offline bilingual word vectors, orthogonal transformations and the inverted softmax
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Fashion-mnist: a novel image dataset for benchmarking machine learnin
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Towards reusable network components by learning compatible representations
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Generalized shape metrics on neural representations
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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nn-template bootstraps pytorch projects by advocating reproducibility & best practices in deep learning, 2021
GrokAI · 2021
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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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Word translation without parallel data
Guillaume Lample, Alexis Conneau, Marc’Aurelio Ranzato, Ludovic Denoyer, and Hervé Jégou · 2018
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Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey E. Hinton · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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PyTorch Lightning, 2019
William Falcon and The PyTorch Lightning team · 2019
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The shape of data: Intrinsic distance for data distributions
Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alexander M. Bronstein, Ivan V. Oseledets, and Emmanuel Müller · 2020
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Are all good word vector spaces isomorphic?
Ivan Vulić, Sebastian Ruder, and Anders Søgaard · 2020
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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How do variational autoencoders learn? insights from representational similarity
Lisa Bonheme and Marek Grzes · 2022
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Representation topology divergence: A method for comparing neural network representations
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The geometry of multilingual language model representations
Tyler A Chang, Zhuowen Tu, and Benjamin K Bergen · 2022
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Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective
Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, and Tom Goldstein · 2022
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You only need a good embeddings extractor to fix spurious correlations
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Muammer Y. Yaman, Sergei V. Kalinin, Kathryn N. Guye, David Ginger, and Maxim Ziatdinov · 2022
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N24news: A new dataset for multimodal news classification
Zhen Wang, Xu Shan, Xiangxie Zhang, and Jie Yang · 2022
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Norm of word embedding encodes information gain
Momose Oyama, Sho Yokoi, and Hidetoshi Shimodaira · 2022
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Lit: Zero-shot transfer with locked-image text tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer · 2022
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Dvc: Data version control - git for data & models, 2022
Ruslan Kuprieiev, skshetry, Dmitry Petrov, Paweł Redzyński, Peter Rowlands, Casper da Costa-Luis, Alexander Schepanovski, Ivan Shcheklein, Batuhan Taskaya, Gao, Jorge Orpinel, David de la Iglesia Castro, Fábio Santos, Aman Sharma, Dave Berenbaum, Zhanibek, Dani Hodovic, daniele, Nikita Kodenko, Andrew Grigorev, Earl, Nabanita Dash, George Vyshnya, Ronan Lamy, maykulkarni, Max Hora, Vera, and Sanidhya Mangal · 2022
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Relative representations enable zero-shot latent space communication
Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà · 2023
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ASIF: Coupled data turns unimodal models to multimodal without training
Antonio Norelli, Marco Fumero, Valentino Maiorca, Luca Moschella, Emanuele Rodolà, and Francesco Locatello · 2023
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On the direct alignment of latent spaces
Zorah Lähner and Michael Moeller · 2023
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From bricks to bridges: Product of invariances to enhance latent space communication
Irene Cannistraci, Luca Moschella, Marco Fumero, Valentino Maiorca, and Emanuele Rodolà · 2023
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