Fetching the paper…
Reading the bibliography…
We introduce a new family of video prediction models designed to support downstream control tasks.
Learning to predict by the methods of temporal differences
Sutton, R. S · 1988
Earlier work this paper cites.
Improving generalization for temporal difference learning: The successor representation
Dayan, P · 1993
Earlier work this paper cites.
Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
Sutton, R. S., Modayil, J., Delp, M., Degris, T., Pilarski, P. M., White, A., and Precup, D · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Deep successor reinforcement learning
Kulkarni, T. D., Saeedi, A., Gautam, S., and Gershman, S. J · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Earlier work this paper cites.
The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., and Gershman, S. J · 2017
Earlier work this paper cites.
Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
Earlier work this paper cites.
Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Nagabandi, A., Kahn, G., Fearing, R. S., and Levine, S · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Earlier work this paper cites.
Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., and Brain, G · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
Cited alongside, same era.
Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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.
gamma-models: Generative temporal difference learning for infinite-horizon prediction
Janner, M., Mordatch, I., and Levine, S · 2020
Cited alongside, same era.
Agent-controller representations: Principled offline rl with rich exogenous information
Islam, R., Tomar, M., Lamb, A., Efroni, Y., Zang, H., Didolkar, A., Misra, D., Li, X., Van Seijen, H., Combes, R. T. d., et al · 2022
Later among the works it cites.
Guaranteed discovery of controllable latent states with multi-step inverse models
Lamb, A., Islam, R., Efroni, Y., Didolkar, A., Misra, D., Foster, D., Molu, L., Chari, R., Krishnamurthy, A., and Langford, J · 2022
Later among the works it cites.
R3m: A universal visual representation for robot manipulation
Nair, S., Rajeswaran, A., Kumar, V., Finn, C., and Gupta, A · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Later among the works it cites.
Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Emerging properties in self-supervised vision transformers
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., and Joulin, A · 2021
Cited alongside, same era.
Provably filtering exogenous distractors using multistep inverse dynamics
Efroni, Y., Misra, D., Krishnamurthy, A., Agarwal, A., and Langford, J · 2021
Cited alongside, same era.
Learning one representation to optimize all rewards
Touati, A. and Ollivier, Y · 2021
Cited alongside, same era.
Videogpt: Video generation using vq-vae and transformers
Yan, W., Zhang, Y., Abbeel, P., and Srinivas, A · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R · 2022
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al · 2022
Cited alongside, same era.
Tong, Z., Song, Y., Wang, J., and Wang, L · 2022
Later among the works it cites.
Image as a foreign language: Beit pretraining for all vision and vision-language tasks
Wang, W., Bao, H., Dong, L., Bjorck, J., Peng, Z., Liu, Q., Aggarwal, K., Mohammed, O. K., Singhal, S., Som, S., et al · 2022
Later among the works it cites.
Self-supervised learning from images with a joint-embedding predictive architecture
Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., and Ballas, N · 2023
Later among the works it cites.
Reinforcement learning from passive data via latent intentions
Ghosh, D., Bhateja, C. A., and Levine, S · 2023
Later among the works it cites.
Mage: Masked generative encoder to unify representation learning and image synthesis
Li, T., Chang, H., Mishra, S., Zhang, H., Katabi, D., and Krishnan, D · 2023
Later among the works it cites.
A distributional analogue to the successor representation
Wiltzer, H., Farebrother, J., Gretton, A., Tang, Y., Barreto, A., Dabney, W., Bellemare, M. G., and Rowland, M · 2024
Closest in time.