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We argue that representations in AI models, particularly deep networks, are converging.
A theoretical analysis of contrastive unsupervised representation learning
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Theory of reproducing kernels
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Possible principles underlying the transformation of sensory messages
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A formal theory of inductive inference. part i
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A solution for the best rotation to relate two sets of vectors
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A discussion of the solution for the best rotation to relate two sets of vectors
Kabsch, W · 1978
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Multidimensional scaling, tree-fitting, and clustering
Shepard, R. N · 1980
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The Rationality of Science
Newton-Smith, W · 1981
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In defense of convergent realism
Hardin, C. L. and Rosenberg, A · 1982
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Three kinds of scientific realism
Putnam, H · 1982
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Learning how the world works: Specifications for predictive networks in robots and brains
Werbos, P. J · 1987
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Least-squares estimation of transformation parameters between two point patterns
Umeyama, S · 1991
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The Quark and the Jaguar: Adventures in the Simple and the Complex
Gell-Mann, M · 1995
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A. and Field, D. J · 1996
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Sparse coding with an overcomplete basis set: A strategy employed by v1?
Olshausen, B. A. and Field, D. J · 1997
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Guns, germs and steel: a short history of everybody for the last 13,000 years
Diamond, J. M · 1998
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Learning with kernels , volume 4
Smola, A. J. and Schölkopf, B · 1998
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Measuring statistical dependence with hilbert-schmidt norms
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B · 2005
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Reconstructing scientific realism to rebut the pessimistic meta-induction
Doppelt, G · 2007
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Unsupervised learning of narrative event chains
Chambers, N. and Jurafsky, D · 2008
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80 million tiny images: A large data set for nonparametric object and scene recognition
Torralba, A., Fergus, R., and Freeman, W. T · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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The newly sighted fail to match seen with felt
Held, R., Ostrovsky, Y., de Gelder, B., Gandhi, T., Ganesh, S., Mathur, U., and Sinha, P · 2011
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Feature selection via dependence maximization
Song, L., Smola, A., Gretton, A., Bedo, J., and Borgwardt, K · 2012
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Crisp boundary detection using pointwise mutual information
Isola, P., Zoran, D., Krishnan, D., and Adelson, E. H · 2014
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The color lexicon of american english
Lindsey, D. T. and Brown, A. M · 2014
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Conditional generative adversarial nets
Mirza, M. and Osindero, S · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J · 2014
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The discovery of perceptual structure from visual co-occurrences in space and time
Isola, P · 2015
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Understanding image representations by measuring their equivariance and equivalence
Lenc, K. and Vedaldi, A · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Learning visual groups from co-occurrences in space and time
Isola, P., Zoran, D., Krishnan, D., and Adelson, E. H · 2016
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Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M. M. A., Yang, Y., and Zhou, Y · 2017
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
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Best-buddies similarity—robust template matching using mutual nearest neighbors
Oron, S., Dekel, T., Xue, T., Freeman, W. T., and Avidan, S · 2017
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Learning to generate reviews and discovering sentiment
Radford, A., Jozefowicz, R., and Sutskever, I · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
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Feature-matching auto-encoders
Tran, D., Burda, Y., and Sutskever, I · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
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A spline theory of deep learning
Balestriero, R. and Baraniuk, R. G · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Input–output maps are strongly biased towards simple outputs
Dingle, K., Camargo, C. Q., and Louis, A. A · 2018
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Loss surfaces, mode connectivity, and fast ensembling of dnns
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D. P., and Wilson, A. G · 2018
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Implicit bias of gradient descent on linear convolutional networks
Gunasekar, S., Lee, J. D., Soudry, D., and Srebro, N · 2018
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Ha, D. and Schmidhuber, J · 2018
Cited alongside, same era.
Phrase-based & neural unsupervised machine translation
Lample, G., Ott, M., Conneau, A., Denoyer, L., and Ranzato, M · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Cited alongside, same era.
Brain-score: Which artificial neural network for object recognition is most brain-like?
Schrimpf, M., Kubilius, J., Hong, H., Majaj, N. J., Rajalingham, R., Issa, E. B., Kar, K., Bashivan, P., Prescott-Roy, J., Geiger, F., et al · 2018
Cited alongside, same era.
A systematic study of bias amplification
Hall, M., van der Maaten, L., Gustafson, L., Jones, M., and Adcock, A · 2022
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Repair: Renormalizing permuted activations for interpolation repair
Jordan, K., Sedghi, H., Saukh, O., Entezari, R., and Neyshabur, B · 2022
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Linearly mapping from image to text space
Merullo, J., Castricato, L., Eickhoff, C., and Pavlick, E · 2022
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Relative representations enable zero-shot latent space communication
Moschella, L., Maiorca, V., Fumero, M., Norelli, A., Locatello, F., and Rodolà, E · 2022
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StylegGAN-XL: Scaling StyleGAN to large diverse datasets
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
Cited alongside, same era.
Openwebtext corpus
Gokaslan, A. and Cohen, V · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
Cited alongside, same era.
Uniform convergence may be unable to explain generalization in deep learning
Nagarajan, V. and Kolter, J. Z · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
Cited alongside, same era.
Deep learning generalizes because the parameter-function map is biased towards simple functions
Valle-Perez, G., Camargo, C. Q., and Louis, A. A · 2019
Cited alongside, same era.
Sauer, A., Schwarz, K., and Geiger, A · 2022
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Neural representational geometry underlies few-shot concept learning
Sorscher, B., Ganguli, S., and Sompolinsky, H · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Wortsman, M., Ilharco, G., Gadre, S. Y., Roelofs, R., Gontijo-Lopes, R., Morcos, A. S., Namkoong, H., Farhadi, A., Carmon, Y., Kornblith, S., et al · 2022
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Unsupervised image-to-image translation with density changing regularization
Xie, S., Ho, Q., and Zhang, K · 2022
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Improving image generation with better captions
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al · 2023
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Rosetta neurons: Mining the common units in a model zoo
Dravid, A., Gandelsman, Y., Efros, A. A., and Shocher, A · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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OpenLLaMA: An open reproduction of LLaMA, May 2023
Geng, X. and Liu, H · 2023
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Goldblum, M., Finzi, M., Rowan, K., and Wilson, A. G · 2023
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Gemini: a family of highly capable multimodal models
Google · 2023
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The low-rank simplicity bias in deep networks
Huh, M., Mobahi, H., Zhang, R., Cheung, B., Agrawal, P., and Isola, P · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Similarity of neural network models: A survey of functional and representational measures
Klabunde, M., Schumacher, T., Strohmaier, M., and Lemmerich, F · 2023
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Grounding language models to images for multimodal inputs and outputs
Koh, J. Y., Salakhutdinov, R., and Fried, D · 2023
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Return of unconditional generation: A self-supervised representation generation method
Li, T., Katabi, D., and He, K · 2023
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Visual instruction tuning
Liu, H., Li, C., Wu, Q., and Lee, Y. J · 2023
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Mechanistic mode connectivity
Lubana, E. S., Bigelow, E. J., Dick, R. P., Krueger, D., and Tanaka, H · 2023
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Large language models as general pattern machines
Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., and Zeng, A · 2023
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Can language models learn to listen?
Ng, E., Subramanian, S., Klein, D., Kanazawa, A., Darrell, T., and Ginosar, S · 2023
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OpenAI · 2023
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Dinov2: Learning robust visual features without supervision, 2023
Oquab, M., Darcet, T., Moutakanni, T., Vo, H. V., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Howes, R., Huang, P.-Y., Xu, H., Sharma, V., Li, S.-W., Galuba, W., Rabbat, M., Assran, M., Ballas, N., Synnaeve, G., Misra, I., Jegou, H., Mairal, J., Labatut, P., Joulin, A., and Bojanowski, P · 2023
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Zipit! merging models from different tasks without training
Stoica, G., Bolya, D., Bjorner, J., Hearn, T., and Hoffman, J · 2023
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Getting aligned on representational alignment, 2023
Sucholutsky, I., Muttenthaler, L., Weller, A., Peng, A., Bobu, A., Kim, B., Love, B. C., Grant, E., Groen, I., Achterberg, J., Tenenbaum, J. B., Collins, K. M., Hermann, K. L., Oktar, K., Greff, K., Hebart, M. N., Jacoby, N., Zhang, Q., Marjieh, R., Geirhos, R., Chen, S., Kornblith, S., Rane, S., Konkle, T., O’Connell, T. P., Unterthiner, T., Lampinen, A. K., Müller, K.-R., Toneva, M., and Griffiths, T. L · 2023
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LLaMA 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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A picture is worth more than 77 text tokens: Evaluating CLIP-style models on dense captions, 2023
Urbanek, J., Bordes, F., Astolfi, P., Williamson, M., Sharma, V., and Romero-Soriano, A · 2023
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Self-correcting LLM-controlled diffusion models
Wu, T.-H., Lian, L., Gonzalez, J. E., Li, B., and Darrell, T · 2023
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Predictive coding or just feature discovery? an alternative account of why language models fit brain data
Antonello, R. and Huth, A · 2024
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Explanatory models in neuroscience: Part 2–constraint-based intelligibility
Cao, R. and Yamins, D · 2024
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Olmo: Accelerating the science of language models
Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Segment anything in medical images
Ma, J., He, Y., Li, F., Han, L., You, C., and Wang, B · 2024
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Do vision and language encoders represent the world similarly?
Maniparambil, M., Akshulakov, R., Djilali, Y. A. D., El Amine Seddik, M., Narayan, S., Mangalam, K., and O’Connor, N. E · 2024
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Meta LLaMA 3, 2024
Meta · 2024
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What do language models hear? probing for auditory representations in language models, 2024
Ngo, J. and Kim, Y · 2024
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Quantifying representation reliability in self-supervised learning models
Park, Y.-J., Wang, H., Ardeshir, S., and Azizan, N · 2024
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Robust agents learn causal world models
Richens, J. and Everitt, T · 2024
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A vision check-up for language models
Sharma, P., Rott Shaham, T., Baradad, M., Fu, S., Rodriguez-Munoz, A., Duggal, S., Isola, P., and Torralba, A · 2024
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Diffusion schrödinger bridge matching
Shi, Y., De Bortoli, V., Campbell, A., and Doucet, A · 2024
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Gemma: Open models based on gemini research and technology
Team, G., Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al · 2024
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