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We introduce a framework for automatically defining and learning deep generative models with problem-specific structure.
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Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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The Design of Approximation Algorithms
Williamson, D. P. and Shmoys, D. B · 2011
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Lightweight Implementations of Probabilistic Programming Languages Via Transformational Compilation
Wingate, D., Stuhlmüller, A., and Goodman, N. D · 2011
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Amortized inference in probabilistic reasoning
Gershman, S. and Goodman, N · 2014
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Kingma, D. P. and Ba, J · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Deep Amortized Inference for Probabilistic Programs
Ritchie, D., Horsfall, P., and Goodman, N. D · 2016
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Automatic Differentiation in Machine Learning: A Survey
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M · 2017
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URL https://karpathy.medium.com/software-2-0-a64152b37c35
Karpathy, A., Nov 2017 · 2017
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Using synthetic data to train neural networks is model-based reasoning
Le, T. A., Baydin, A. G., Zinkov, R., and Wood, F · 2017
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Attention is All you Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Faithful Inversion of Generative Models for Effective Amortized Inference
Webb, S., Golinski, A., Zinkov, R., Siddharth, N., Rainforth, T., Teh, Y. W., and Wood, F · 2017
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Automatic Machine Learning: Methods, Systems, Challenges
Hutter, F., Kotthoff, L., and Vanschoren, J. (eds.) · 2018
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Recurrent Relational Networks
Palm, R., Paquet, U., and Winther, O · 2018
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An Introduction to Probabilistic Programming
van de Meent, J.-W., Paige, B., Yang, H., and Wood, F · 2018
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Generating Long Sequences with Sparse Transformers, April 2019
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
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Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, June 2019
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
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Neurosymbolic Programming
Chaudhuri, S., Ellis, K., Polozov, O., Singh, R., Solar-Lezama, A., and Yue, Y · 2021
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Evaluating Large Language Models Trained on Code, July 2021
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., and Salakhutdinov, R · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Ho, J., Chen, X., Srinivas, A., Duan, Y., and Abbeel, P · 2019
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Variational Autoencoder with Arbitrary Conditioning
Ivanov, O., Figurnov, M., and Vetrov, D · 2019
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Learning a SAT Solver from Single-Bit Supervision
Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., and Dill, D. L · 2019
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SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Wang, P.-W., Donti, P. L., Wilder, B., and Kolter, Z · 2019
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Longformer: The Long-Document Transformer, December 2020
Beltagy, I., Peters, M. E., and Cohan, A · 2020
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Probabilistic Symmetries and Invariant Neural Networks
Bloem-Reddy, B. and Teh, Y. W · 2020
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Hoogeboom, E., Gritsenko, A. A., Bastings, J., Poole, B., Berg, R. v. d., and Salimans, T · 2021
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
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CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
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Big Bird: Transformers for Longer Sequences, January 2021
Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A · 2021
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Denoising diffusion for sampling sat solutions
Freivalds, K. and Kozlovics, S · 2022
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Flexible Diffusion Modeling of Long Videos, May 2022
Harvey, W., Naderiparizi, S., Masrani, V., Weilbach, C., and Wood, F · 2022
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Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Elucidating the design space of diffusion-based generative models
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Efficient Transformers: A Survey, March 2022
Tay, Y., Dehghani, M., Bahri, D., and Metzler, D · 2022
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One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction
Tönshoff, J., Kisin, B., Lindner, J., and Grohe, M · 2022
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