Fetching the paper…
Reading the bibliography…
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors.
The performance of universal encoding
Krichevsky, R. E. and Trofimov, V. K · 1981
Earlier work this paper cites.
Learning a synaptic learning rule
Bengio, Y., Bengio, S., and Cloutier, J · 1991
Earlier work this paper cites.
Simple principles of metalearning
Schmidhuber, J., Zhao, J., and Wiering, M · 1996
Earlier work this paper cites.
Coding for a binary independent piecewise-identically-distributed source
Willems, F. M. J · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Learning to learn: Introduction and overview
Thrun, S. and Pratt, L. Y · 1998
Earlier work this paper cites.
The generalized distributive law
Aji, S. M. and McEliece, R. J · 2000
Earlier work this paper cites.
Learning to learn using gradient descent
Hochreiter, S., Younger, A. S., and Conwell, P. R · 2001
Earlier work this paper cites.
Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability
Hutter, M · 2005
Earlier work this paper cites.
Combining expert advice efficiently
Koolen, W. M. and de Rooij, S · 2008
Earlier work this paper cites.
Partition tree weighting
Veness, J., White, M., Bowling, M., and György, A · 2013
Cited alongside, same era.
Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N. D · 2014
Cited alongside, same era.
Inferring algorithmic patterns with stack-augmented recurrent nets
Joulin, A. and Mikolov, T · 2015
Cited alongside, same era.
Rl$ˆ2$: Fast reinforcement learning via slow reinforcement learning
Duan, Y., Schulman, J., Chen, X., Bartlett, P. L., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
Deep amortized inference for probabilistic programs
Ritchie, D., Horsfall, P., and Goodman, N. D · 2016
Cited alongside, same era.
Meta-learning with memory-augmented neural networks
Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D · 2020
Later among the works it cites.
Meta-trained agents implement bayes-optimal agents
Mikulik, V., Delétang, G., McGrath, T., Genewein, T., Martic, M., Legg, S., and Ortega, P. A · 2020
Later among the works it cites.
Varibad: A very good method for bayes-adaptive deep RL via meta-learning
Zintgraf, L. M., Shiarlis, K., Igl, M., Schulze, S., Gal, Y., Hofmann, K., and Whiteson, S · 2020
Later among the works it cites.
General-purpose in-context learning by meta-learning transformers
Kirsch, L., Harrison, J., Sohl-Dickstein, J., and Metz, L · 2022
Later among the works it cites.
Transformers can do bayesian inference
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Santoro, A., Bartunov, S., Botvinick, M. M., Wierstra, D., and Lillicrap, T. P · 2016
Cited alongside, same era.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Cited alongside, same era.
Learning to reinforcement learn
Wang, J., Kurth-Nelson, Z., Soyer, H., Leibo, J. Z., Tirumala, D., Munos, R., Blundell, C., Kumaran, D., and Botvinick, M. M · 2017
Cited alongside, same era.
Transformer-xl: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J. G., Le, Q. V., and Salakhutdinov, R · 2019
Cited alongside, same era.
Meta-learning of sequential strategies
Ortega, P. A., Wang, J. X., Rowland, M., Genewein, T., Kurth-Nelson, Z., Pascanu, R., Heess, N., Veness, J., Pritzel, A., Sprechmann, P., Jayakumar, S. M., McGrath, T., Miller, K. J., Azar, M. G., Osband, I., Rabinowitz, N. C., György, A., Chiappa, S., Osindero, S., Teh, Y. W., van Hasselt, H., de Freitas, N., Botvinick, M. M., and Legg, S · 2019
Cited alongside, same era.
Müller, S., Hollmann, N., Pineda-Arango, S., Grabocka, J., and Hutter, F · 2022
Later among the works it cites.
Train short, test long: Attention with linear biases enables input length extrapolation
Press, O., Smith, N. A., and Lewis, M · 2022
Later among the works it cites.
Reed, S. E., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., Eccles, T., Bruce, J., Razavi, A., Edwards, A., Heess, N., Chen, Y., Hadsell, R., Vinyals, O., Bordbar, M., and de Freitas, N · 2022
Later among the works it cites.
An explanation of in-context learning as implicit bayesian inference
Xie, S. M., Raghunathan, A., Liang, P., and Ma, T · 2022
Later among the works it cites.
Human-timescale adaptation in an open-ended task space
Adaptive Agent Team, Bauer, J., Baumli, K., Baveja, S., Behbahani, F. M. P., Bhoopchand, A., Bradley-Schmieg, N., Chang, M., Clay, N., Collister, A., Dasagi, V., Gonzalez, L., Gregor, K., Hughes, E., Kashem, S., Loks-Thompson, M., Openshaw, H., Parker-Holder, J., Pathak, S., Nieves, N. P., Rakicevic, N., Rocktäschel, T., Schroecker, Y., Sygnowski, J., Tuyls, K., York, S., Zacherl, A., and Zhang, L · 2023
Closest in time.