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Generative Flow Networks (GFlowNets) are recently proposed models for learning stochastic policies that generate compositional objects by sequences of actions with the probability proportional to a given reward function.
Training products of experts by minimizing contrastive divergence
G. E. Hinton · 2002
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Topics in optimal transportation
C. Villani · 2003
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The cma evolution strategy: A comparing review
N. Hansen · 2006
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A fast learning algorithm for deep belief nets
G. E. Hinton, S. Osindero, and Y. W. Teh · 2006
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Training restricted boltzmann machines using approximations to the likelihood gradient
T. Tieleman · 2008
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Levenshtein Distance: Information Theory, Computer Science, String (Computer Science), String Metric, Damerau?Levenshtein Distance, Spell Checker, Hamming Distance
F. P. Miller, A. F. Vandome, and J. McBrewster · 2009
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Fast and robust earth mover’s distances
O. Pele and M. Werman · 2009
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Deep boltzmann machines
R. Salakhutdinov and G. E. Hinton · 2009
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Displacement interpolation using lagrangian mass transport
N. Bonneel, M. van de Panne, S. Paris, and W. Heidrich · 2011
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A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
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Sinkhorn distances: Lightspeed computation of optimal transportation distances
M. Cuturi · 2013
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Domain adaptation with regularized optimal transport
N. Courty, R. Flamary, and D. Tuia · 2014
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Synthesizing and mixing stationary gaussian texture models
G.-S. Xia, S. Ferradans, G. Peyré, and J.-F. Aujol · 2014
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Sliced and Radon Wasserstein barycenters of measures
N. Bonneel, J. Rabin, G. Peyré, and H. Pfister · 2015
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Survey of variation in human transcription factors reveals prevalent dna binding changes
L. A. Barrera, A. Vedenko, J. V. Kurland, J. M. Rogers, S. S. Gisselbrecht, E. J. Rossin, J. Woodard, L. Mariani, K. H. Kock, S. Inukai, T. Siggers, L. Shokri, R. Gordân, N. Sahni, C. Cotsapas, T. Hao, S. Yi, M. Kellis, M. J. Daly, M. Vidal, D. E. Hill, and M. L. Bulyk · 2016
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Local fitness landscape of the green fluorescent protein
K. S. Sarkisyan, D. Bolotin, M. V. Meer, D. R. Usmanova, A. S. Mishin, G. V. Sharonov, D. N. Ivankov, N. G. Bozhanova, M. S. Baranov, O. Soylemez, N. S. Bogatyreva, P. K. Vlasov, E. S. Egorov, M. D. Logacheva, A. S. Kondrashov, D. M. Chudakov, E. V. Putintseva, I. Z. Mamedov, D. S. Tawfik, K. A. Lukyanov, and F. A. Kondrashov · 2016
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
J. Altschuler, J. Niles-Weed, and P. Rigollet · 2017
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Multilevel clustering via Wasserstein means
N. Ho, X. Nguyen, M. Yurochkin, H. H. Bui, V. Huynh, and D. Phung · 2017
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Learning generative models with sinkhorn divergences
A. Genevay, G. Peyré, and M. Cuturi · 2018
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Biological sequence design using batched bayesian optimization
D. Belanger, S. Vora, Z. E. Mariet, R. Deshpande, D. Dohan, C. Angermueller, K. Murphy, O. Chapelle, and L. J. Colwell · 2019
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Generalized energy based models
M. Arbel, L. Zhou, and A. Gretton · 2021
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Flow network based generative models for non-iterative diverse candidate generation
E. Bengio, M. Jain, M. Korablyov, D. Precup, and Y. Bengio · 2021
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Improved contrastive divergence training of energy based models
Y. Du, S. Li, J. B. Tenenbaum, and I. Mordatch · 2021
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Oops i took a gradient: Scalable sampling for discrete distributions
W. Grathwohl, K. Swersky, M. Hashemi, D. K. Duvenaud, and C. J. Maddison · 2021
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Point-set distances for learning representations of 3D point clouds
T. Nguyen, Q.-H. Pham, T. Le, T. Pham, N. Ho, and B.-S. Hua · 2021
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Learning generative models across incomparable spaces
C. Bunne, D. Alvarez-Melis, A. Krause, and S. Jegelka · 2019
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Exponential family estimation via adversarial dynamics embedding
B. Dai, Z. Liu, H. Dai, N. He, A. Gretton, L. Song, and D. Schuurmans · 2019
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On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
T. Lin, N. Ho, and M. Jordan · 2019
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Computational optimal transport
G. Peyré and M. Cuturi · 2019
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Evaluating protein transfer learning with tape
R. Rao, N. Bhattacharya, N. Thomas, Y. Duan, X. Chen, J. F. Canny, P. Abbeel, and Y. S. Song · 2019
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Model-based reinforcement learning for biological sequence design
C. Angermueller, D. Dohan, D. Belanger, R. Deshpande, K. Murphy, and L. J. Colwell · 2020
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Learning discrete energy-based models via auxiliary-variable local exploration
H. Dai, R. Singh, B. Dai, C. Sutton, and D. Schuurmans · 2020
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DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics
M. Pirtskhalava, A. A. Amstrong, M. Grigolava, M. Chubinidze, E. Alimbarashvili, B. Vishnepolsky, A. E. Gabrielian, A. Rosenthal, D. E. Hurt, and M. Tartakovsky · 2021
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Conservative objective models for effective offline model-based optimization
B. Trabucco, A. Kumar, X. Geng, and S. Levine · 2021
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Bayesian structure learning with generative flow networks
T. Deleu, A. G’ois, C. C. Emezue, M. Rankawat, S. Lacoste-Julien, S. Bauer, and Y. Bengio · 2022
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Biological sequence design with gflownets
M. Jain, E. Bengio, A. García, J. Rector-Brooks, B. F. P. Dossou, C. A. Ekbote, J. Fu, T. Zhang, M. Kilgour, D. Zhang, L. Simine, P. Das, and Y. Bengio · 2022
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On the efficiency of entropic regularized algorithms for optimal transport
T. Lin, N. Ho, and M. I. Jordan · 2022
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Trajectory balance: Improved credit assignment in gflownets
N. Malkin, M. Jain, E. Bengio, C. Sun, and Y. Bengio · 2022
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Revisiting sliced Wasserstein on images: From vectorization to convolution
K. Nguyen and N. Ho · 2022
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Hierarchical sliced Wasserstein distance
K. Nguyen, T. Ren, H. Nguyen, L. Rout, T. Nguyen, and N. Ho · 2022
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Generative flow networks for discrete probabilistic modeling
D. Zhang, N. Malkin, Z. Liu, A. Volokhova, A. Courville, and Y. Bengio · 2022
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