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Emergent Communication (EmCom) investigates how agents develop symbolic communication through interaction without predefined language.
Convention: A Philosophical Study
Lewis, D., 1969 · 1969
Earlier work this paper cites.
Joint attention and early language
Tomasello, M., Farrar, M.J., 1986 · 1986
Earlier work this paper cites.
Signature verification using a "siamese" time delay neural network, in: Advances in Neural Information Processing Systems (NIPS)
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R., 1994 · 1994
Earlier work this paper cites.
A self-organizing spatial vocabulary
Steels, L., 1995 · 1995
Earlier work this paper cites.
Rates of convergence of the hastings and metropolis algorithms
Mengersen, K.L., Tweedie, R.L., 1996 · 1996
Earlier work this paper cites.
Weak convergence and optimal scaling of random walk metropolis algorithms
Gelman, A., Gilks, W.R., Roberts, G.O., 1997 · 1997
Earlier work this paper cites.
The emergence of a "language" in an evolving population of neural networks
Cangelosi, A., Parisi, D., 1998 · 1998
Earlier work this paper cites.
The origins of ontologies and communication conventions in multi-agent systems, in: Autonomous Agents, pp. 597–598
Steels, L., 1998 · 1998
Earlier work this paper cites.
The information bottleneck method, in: Annual Allerton Conference on Communication, Control and Computing, pp. 368–377
Tishby, N., Pereira, F.C., Bialek, W., 1999 · 1999
Earlier work this paper cites.
Directional Statistics
Mardia, K.V., Jupp, P.E., 2000 · 2000
Earlier work this paper cites.
Optimal scaling for various metropolis-hastings algorithms
Roberts, G.O., Rosenthal, J.S., 2001 · 2001
Earlier work this paper cites.
Simulating the Evolution of Language
Cangelosi, A., Parisi, D., 2002 · 2002
Earlier work this paper cites.
Semiotics: The Basics
Chandler, D., 2002 · 2002
Earlier work this paper cites.
The emergence of linguistic structure: An overview of the iterated learning model, in: Cangelosi, A., Parisi, D. (Eds.), Simulating the Evolution of Language, Springer. pp. 121–147
Kirby, S., 2002 · 2002
Earlier work this paper cites.
Progress in the simulation of emergent communication and language
Wagner, K., Reggia, J.A., Uriagereka, J., Wilkinson, G.S., 2003 · 2003
Earlier work this paper cites.
Monte Carlo Statistical Methods
Robert, C.P., Casella, G., 2004 · 2004
Earlier work this paper cites.
Emergent multi-agent communication in the deep learning era
Lazaridou, A., Baroni, M., 2020 · 2006
Earlier work this paper cites.
Cumulative cultural evolution in the laboratory: An experimental approach to the origins of structure in human language
Kirby, S., Cornish, H., Smith, K., 2008 · 2008
Earlier work this paper cites.
Visualizing data using t-sne
van der Maaten, L., Hinton, G., 2008 · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A., 2009 · 2009
Earlier work this paper cites.
The Grounded Naming Game. John Benjamins Publishing Company
Steels, L., Loetzsch, M., 2012 · 2012
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., Courville, A., 2013 · 2013
Earlier work this paper cites.
Learning to communicate with deep multi-agent reinforcement learning, in: International Conference on Neural Information Processing Systems (NeurIPS), p. 2145–2153
Foerster, J.N., Assael, Y.M., de Freitas, N., Whiteson, S., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Learning multiagent communication with backpropagation, in: International Conference on Neural Information Processing Systems (NeurIPS), p. 2252–2260
Sukhbaatar, S., Szlam, A., Fergus, R., 2016 · 2016
Earlier work this paper cites.
Symbol emergence in robotics: a survey
Taniguchi, T., Nagai, T., Nakamura, T., Iwahashi, N., Ogata, T., Asoh, H., 2016 · 2016
Earlier work this paper cites.
Variational inference: A review for statisticians
Blei, D.M., Kucukelbir, A., McAuliffe, J.D., 2017 · 2017
Earlier work this paper cites.
Learning cooperative visual dialog agents with deep reinforcement learning, in: IEEE International Conference on Computer Vision (ICCV), pp. 2970–2979
Das, A., Kottur, S., Moura, J.M.F., Lee, S., Batra, D., 2017 · 2017
Cited alongside, same era.
Emergence of language with multi-agent games: Learning to communicate with sequences of symbols, in: Advances in Neural Information Processing Systems (NeurIPS)
Havrylov, S., Titov, I., 2017 · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax, in: International Conference on Learning Representations (ICLR)
Jang, E., Gu, S., Poole, B., 2017 · 2017
Cited alongside, same era.
Natural language does not emerge ‘naturally’ in multi-agent dialog, in: Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. pp. 2962–2967
Kottur, S., Moura, J., Lee, S., Batra, D., 2017 · 2017
Cited alongside, same era.
Learning transferable visual models from natural language supervision, in: International Conference on Machine Learning (ICML)
Radford, A., Kim, J.W., Hallacy, C., et al., 2021 · 2021
Later among the works it cites.
Understanding self-supervised learning dynamics without contrastive pairs, in: International Conference on Machine Learning (IMCL)
Tian, Y., Chen, X., Ganguli, S., 2021 · 2021
Later among the works it cites.
Barlow twins: Self-supervised learning via redundancy reduction, in: International Conference on Machine Learning (ICML)
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S., 2021 · 2021
Later among the works it cites.
Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods, in: Advances in Neural Information Processing Systems (NeurIPS), pp. 26671–26685
Balestriero, R., LeCun, Y., 2022 · 2022
Later among the works it cites.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning, in: International Conference on Learning Representations (ICLR)
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Lazaridou, A., Peysakhovich, A., Baroni, M., 2017 · 2017
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables, in: International Conference on Learning Representations (ICLR)
Maddison, C.J., Mnih, A., Teh, Y.W., 2017 · 2017
Cited alongside, same era.
How agents see things: On visual representations in an emergent language game, in: Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Brussels, Belgium. pp. 981–985
Bouchacourt, D., Baroni, M., 2018 · 2018
Cited alongside, same era.
Hyperspherical variational auto-encoders, in: Conference on Uncertainty in Artificial Intelligence (UAI)
Davidson, T.R., Falorsi, L., De Cao, N., Kipf, T., Tomczak, J.M., 2018 · 2018
Cited alongside, same era.
Emergence of linguistic communication from referential games with symbolic and pixel input, in: International Conference on Learning Representations (ICLR)
Lazaridou, A., Hermann, K.M., Tuyls, K., Clark, S., 2018 · 2018
Cited alongside, same era.
Emergence of grounded compositional language in multi-agent populations, in: AAAI Conference on Artificial Intelligence (AAAI), pp. 1495–1502
Mordatch, I., Abbeel, P., 2018 · 2018
Cited alongside, same era.
Community regularization of visually-grounded dialog, in: International Conference on Autonomous Agents and MultiAgent Systems, p. 1042–1050
Agarwal, A., Gurumurthy, S., Sharma, V., Lewis, M., Sycara, K., 2019 · 2019
Cited alongside, same era.
Measuring compositionality in representation learning, in: International Conference on Learning Representations (ICLR)
Andreas, J., 2019 · 2019
Cited alongside, same era.
Bardes, A., Ponce, J., LeCun, Y., 2022 · 2022
Later among the works it cites.
A path towards autonomous machine intelligence
LeCun, Y., 2022 · 2022
Later among the works it cites.
Compositional generalization in unsupervised compositional representation learning: A study on disentanglement and emergent language
Xu, Z., Niethammer, M., Raffel, C., 2022 · 2022
Later among the works it cites.
Toward more human-like ai communication: A review of emergent communication research
Brandizzi, N., 2023 · 2023
Later among the works it cites.
Recursive metropolis-hastings naming game: Symbol emergence in a multi-agent system based on probabilistic generative models
Inukai, J., Taniguchi, T., Taniguchi, A., Hagiwara, Y., 2023 · 2023
Later among the works it cites.
Emergent communication in interactive sketch question answering, in: International Conference on Neural Information Processing Systems (NeurIPS)
Lei, Z., Zhang, Y., Xiong, Y., Chen, S., 2023 · 2023
Later among the works it cites.
Representation uncertainty in self-supervised learning as variational inference, in: IEEE/CVF International Conference on Computer Vision (ICCV)
Nakamura, H., Okada, M., Taniguchi, T., 2023 · 2023
Later among the works it cites.
Emergent communication through metropolis-hastings naming game with deep generative models
Taniguchi, T., Yoshida, Y., Matsui, Y., Hoang, N.L., Taniguchi, A., Hagiwara, Y., 2023 · 2023
Later among the works it cites.
A review of the applications of deep learning-based emergent communication
Boldt, B., Mortensen, D.R., 2024 · 2024
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Concept-best-matching: Evaluating compositionality in emergent communication, in: Findings of the Association for Computational Linguistics (ACL), Association for Computational Linguistics. pp. 3186–3194
Carmeli, B., Belinkov, Y., Meir, R., 2024 · 2024
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A comprehensive survey on contrastive learning
Hu, H., Wang, X., Zhang, Y., Chen, Q., Guan, Q., 2024 · 2024
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DINOv2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H.V., Szafraniec, M., Khalidov, V., Fernandez, P., HAZIZA, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P., 2024 · 2024
Closest in time.
Stem-jepa: A joint-embedding predictive architecture for musical stem compatibility estimation, in: International Society for Music Information Retrieval Conference (ISMIR)
Riou, A., Lattner, S., Hadjeres, G., Anslow, M., Peeters, G., 2024 · 2024
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Visually grounded language learning: A review of language games, datasets, tasks, and models
Suglia, A., Konstas, I., Lemon, O., 2024 · 2024
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Collective predictive coding hypothesis: Symbol emergence as decentralized bayesian inference
Taniguchi, T., 2024 · 2024
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V-jepa 2: Self-supervised video models enable understanding, prediction and planning
Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., Sinha, K., Zholus, A., Arnaud, S., Gejji, A., Martin, A., Hogan, F.R., Dugas, D., Bojanowski, P., Khalidov, V., Labatut, P., Massa, F., Szafraniec, M., Krishnakumar, K., Li, Y., Ma, X., Chandar, S., Meier, F., LeCun, Y., Rabbat, M., Ballas, N., 2025 · 2025
Closest in time.
Meta clip 2: A worldwide scaling recipe, in: Advances in Neural Information Processing Systems (NeurIPS)
Chuang, Y.S., Li, Y., Wang, D., Yeh, C.F., Lyu, K., Raghavendra, R., Glass, J., Huang, L., Weston, J., Zettlemoyer, L., Chen, X., Liu, Z., Xie, S., tau Yih, W., Li, S.W., Xu, H., 2025 · 2025
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Emergent communication and learning pressures in˜ language˜ models: a language evolution perspective
Galke, L.P.A., Raviv, L., 2025 · 2025
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Searching for structure: Investigating emergent communication with large language models, in: International Conference on Computational Linguistics, Association for Computational Linguistics. pp. 9977–9991
Kouwenhoven, T., Peeperkorn, M., Verhoef, T., 2025 · 2025
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Matsui, Y., Yamaki, R., Ueda, R., Shinagawa, S., Taniguchi, T., 2025 · 2025
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Emergent language: a survey and taxonomy
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Rita, M., Michel, P., Chaabouni, R., Pietquin, O., Dupoux, E., Strub, F., 2025 · 2025
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Towards human-like emergent communication via utility, informativeness, and complexity
Tucker, M., Shah, J., Levy, R., Zaslavsky, N., 2025 · 2025
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A survey on self-supervised methods for visual representation learning
Uelwer, T., Robine, J., Wagner, S.S., et al., 2025 · 2025
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