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We introduce iterative reasoning through energy diffusion (IRED), a novel framework for learning to reason for a variety of tasks by formulating reasoning and decision-making problems with energy-based optimization.
Dynamical Systems That Sort Lists, Diagonalize Matrices, and Solve Linear Programming Problems
Brockett, R. W · 1991
Earlier work this paper cites.
SATPlan: Planning as Satisfiability
Kautz, H., Selman, B., and Hoffmann, J · 2006
Earlier work this paper cites.
A Tutorial on Energy-based Learning, 2006
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Earlier work this paper cites.
Graves, A., Wayne, G., and Danihelka, I · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
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Earlier work this paper cites.
Adaptive Computation Time for Recurrent Neural Networks
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Earlier work this paper cites.
Hybrid Computing Using a Neural Network with Dynamic External Memory
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Earlier work this paper cites.
Deep Residual Learning for Image Recognition
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
A Theory of Generative Convnet
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Earlier work this paper cites.
Optnet: Differentiable Optimization as a Layer in Neural Networks
Amos, B. and Kolter, J. Z · 2017
Earlier work this paper cites.
Adaptive Neural Networks for Efficient Inference
Bolukbasi, T., Wang, J., Dekel, O., and Saligrama, V · 2017
Earlier work this paper cites.
Making Neural Programming Architectures Generalize via Recursion
Cai, J., Shin, R., and Song, D · 2017
Earlier work this paper cites.
Hierarchical Multiscale Recurrent Neural Networks
Chung, J., Ahn, S., and Bengio, Y · 2017
Earlier work this paper cites.
Differentiable Learning of Submodular Models
Djolonga, J. and Krause, A · 2017
Earlier work this paper cites.
Task-based End-to-End Model Learning in Stochastic Optimization
Donti, P. L., Amos, B., and Kolter, J. Z · 2017
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End-to-End Differentiable Proving
Rocktäschel, T. and Riedel, S · 2017
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Differentiable Learning of Logical Rules for Knowledge Base Reasoning
Yang, F., Yang, Z., and Cohen, W. W · 2017
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Applied Optimal Control: Optimization, Estimation and Control
Bryson, A. E · 2018
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Deepproblog: Neural Probabilistic Logic Programming
Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., and De Raedt, L · 2018
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Recurrent Relational Networks
Palm, R., Paquet, U., and Winther, O · 2018
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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VAEBM: A Symbiosis Between Variational Autoencoders and Energy-based Models
Xiao, Z., Kreis, K., Kautz, J., and Vahdat, A · 2020
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NeurASP: Embracing Neural Networks into Answer Set Programming
Yang, Z., Ishay, A., and Lee, J · 2020
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Generalized Energy Based Models
Arbel, M., Zhou, L., and Gretton, A · 2021
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Pondernet: Learning to Ponder
Banino, A., Balaguer, J., and Blundell, C · 2021
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Improved Contrastive Divergence Training of Energy Based Models
Du, Y., Li, S., Tenenbaum, B. J., and Mordatch, I · 2021
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Cooperative training of descriptor and generator networks
Xie, J., Lu, Y., Gao, R., Zhu, S.-C., and Wu, Y. N · 2018
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., and Lin, D · 2018
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MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
Amini, A., Gabriel, S., Lin, S., Koncel-Kedziorski, R., Choi, Y., and Hajishirzi, H · 2019
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Deep Equilibrium Models
Bai, S., Kolter, J. Z., and Koltun, V · 2019
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Universal Transformers
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Neural Logic Machines
Dong, H., Mao, J., Lin, T., Wang, C., Li, L., and Zhou, D · 2019
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Integrated Task and Motion Planning
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Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning
Lu, P., Gong, R., Jiang, S., Qiu, L., Huang, S., Liang, X., and Zhu, S.-C · 2021
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Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Schwarzschild, A., Borgnia, E., Gupta, A., Huang, F., Vishkin, U., Goldblum, M., and Goldstein, T · 2021
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Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Anil, C., Pokle, A., Liang, K., Treutlein, J., Wu, Y., Bai, S., Kolter, J. Z., and Grosse, R. B · 2022
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Learning Iterative Reasoning through Energy Minimization
Du, Y., Li, S., Tenenbaum, J., and Mordatch, I · 2022
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Constraint-Based Graph Network Simulator
Rubanova, Y., Sanchez-Gonzalez, A., Pfaff, T., and Battaglia, P · 2022
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Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
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Inferring Relational Potentials in Interacting Systems
Comas, A., Du, Y., Lopez, C. F., Ghimire, S., Sznaier, M., Tenenbaum, J. B., and Camps, O · 2023
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Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Du, Y., Durkan, C., Strudel, R., Tenenbaum, J. B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W. S · 2023
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DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability
Fang, X., Garrett, C. R., Eppner, C., Lozano-Pérez, T., Kaelbling, L. P., and Fox, D · 2023
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Difusco: Graph-Based Diffusion Solvers for Combinatorial Optimization
Sun, Z. and Yang, Y · 2023
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