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Compositionality is believed to be fundamental to intelligence.
Three models for the description of language
Chomsky, N. (1956) · 1956
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Kolmogorov complexity
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Compositional languages emerge in a neural iterated learning model
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Kolmogorov complexity and information theory. with an interpretation in terms of questions and answers
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The algebraic mind: Integrating connectionism and cognitive science
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Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems
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The minimum description length principle
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Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language
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’all languages are equally complex’
Joseph, J. E. and Newmeyer, F. J. (2012) · 2012
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The case for compositionality
Szabó, Z. G. (2012) · 2012
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Compression and communication in the cultural evolution of linguistic structure
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An analysis of idioms and their problems found in the novel the adventures of tom sawyer by mark twain
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Neural module networks
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Neural discrete representation learning
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The description length of deep learning models
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
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Disentangling controllable and uncontrollable factors of variation by interacting with the world
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Learning invariances using the marginal likelihood
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Measuring compositionality in representation learning
Andreas, J. (2019) · 2019
Compositionality
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Chain-of-thought prompting elicits reasoning in large language models
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Gflownet foundations
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Towards monosemanticity: Decomposing language models with dictionary learning
Bricken, T., Templeton, A., Batson, J., Chen, B., Jermyn, A., Conerly, T., Turner, N., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Hatfield-Dodds, Z., Tamkin, A., Nguyen, K., McLean, B., Burke, J. E., Hume, T., Carter, S., Henighan, T., and Olah, C. (2023) · 2023
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Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
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Systematic generalization: What is required and can it be learned?
Bahdanau, D., Murty, S., Noukhovitch, M., Nguyen, T. H., de Vries, H., and Courville, A. (2019) · 2019
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Ease-of-teaching and language structure from emergent communication
Li, F. and Bowling, M. (2019) · 2019
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Generating diverse high-fidelity images with vq-vae-2
Razavi, A., Van den Oord, A., and Vinyals, O. (2019) · 2019
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Permutation equivariant models for compositional generalization in language
Gordon, J., Lopez-Paz, D., Baroni, M., and Bouchacourt, D. (2020) · 2020
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Compositionality decomposed: How do neural networks generalise?
Hupkes, D., Dankers, V., Mul, M., and Bruni, E. (2020) · 2020
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Is the discrete vae’s power stuck in its prior?
Jones, H. T. and Moore, J. (2020) · 2020
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Neurosymbolic ai: The 3 rd wave
Garcez, A. d. and Lamb, L. C. (2023) · 2023
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Goldblum, M., Finzi, M., Rowan, K., and Wilson, A. G. (2023) · 2023
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Gflownet-em for learning compositional latent variable models
Hu, E. J., Malkin, N., Jain, M., Everett, K. E., Graikos, A., and Bengio, Y. (2023) · 2023
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Abstract representations emerge naturally in neural networks trained to perform multiple tasks
Johnston, W. J. and Fusi, S. (2023) · 2023
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Synergies between disentanglement and sparsity: Generalization and identifiability in multi-task learning
Lachapelle, S., Deleu, T., Mahajan, D., Mitliagkas, I., Bengio, Y., Lacoste-Julien, S., and Bertrand, Q. (2023) · 2023
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Simplicial embeddings in self-supervised learning and downstream classification
Lavoie, S., Tsirigotis, C., Schwarzer, M., Vani, A., Noukhovitch, M., Kawaguchi, K., and Courville, A. (2023) · 2023
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Break it down: Evidence for structural compositionality in neural networks
Lepori, M. A., Serre, T., and Pavlick, E. (2023) · 2023
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Modular deep learning
Pfeiffer, J., Ruder, S., Vulić, I., and Ponti, E. (2023) · 2023
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The best game in town: The reemergence of the language-of-thought hypothesis across the cognitive sciences
Quilty-Dunn, J., Porot, N., and Mandelbaum, E. (2023) · 2023
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Compression for AGI - Jack Rae | Stanford MLSys #76
Rae, J. (2023) · 2023
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Improving compositional generalization using iterated learning and simplicial embeddings
Ren, Y., Lavoie, S., Galkin, M., Sutherland, D. J., and Courville, A. (2023) · 2023
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Neurosymbolic artificial intelligence (why, what, and how)
Sheth, A., Roy, K., and Gaur, M. (2023) · 2023
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Neural systematic binder
Singh, G., Kim, Y., and Ahn, S. (2023) · 2023
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An observation on generalization
Sutskever, I. (2023) · 2023
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Linear spaces of meanings: compositional structures in vision-language models
Trager, M., Perera, P., Zancato, L., Achille, A., Bhatia, P., and Soatto, S. (2023) · 2023
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Compositional generalization from first principles
Wiedemer, T., Mayilvahanan, P., Bethge, M., and Brendel, W. (2023) · 2023
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Investigating generalization behaviours of generative flow networks
Atanackovic, L. and Bengio, E. (2024) · 2024
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Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
Driscoll, L. N., Shenoy, K., and Sussillo, D. (2024) · 2024
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Amortizing intractable inference in large language models
Hu, E. J., Jain, M., Elmoznino, E., Kaddar, Y., Lajoie, G., Bengio, Y., and Malkin, N. (2024) · 2024
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On the specialization of neural modules
Jarvis, D., Klein, R., Rosman, B., and Saxe, A. M. (2024) · 2024
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Sources of richness and ineffability for phenomenally conscious states
Ji, X., Elmoznino, E., Deane, G., Constant, A., Dumas, G., Lajoie, G., Simon, J., and Bengio, Y. (2024) · 2024
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Additive decoders for latent variables identification and cartesian-product extrapolation
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When does compositional structure yield compositional generalization? a kernel theory
Lippl, S. and Stachenfeld, K. (2024) · 2024
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What makes models compositional? a theoretical view: With supplement
Ram, P., Klinger, T., and Gray, A. G. (2024) · 2024
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Understanding simplicity bias towards compositional mappings via learning dynamics
Ren, Y. and Sutherland, D. J. (2024) · 2024
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Discovering modular solutions that generalize compositionally
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