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Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
Pareto optimality in multiobjective problems
Y. Censor · 1977
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
G. E. Hinton · 2002
Earlier work this paper cites.
Nonlinear multiobjective optimization
S. Kaisa M. Miettinen, Sayin · 2003
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A tutorial on energy-based learning
Y. LeCun, S. Chopra, R. Hadsell, M. Ranzato, and F. Huang · 2006
Earlier work this paper cites.
A survey of recent developments in multiobjective optimization
A. Chinchuluun and P. M. Pardalos · 2007
Earlier work this paper cites.
Energy-based models in document recognition and computer vision
Y. LeCun, S. Chopra, M. Ranzato, and F.-J. Huang · 2007
Earlier work this paper cites.
Multiple-gradient descent algorithm (mgda) for multiobjective optimization
J.-A. Désidéri · 2012
Earlier work this paper cites.
A strategy for risk mitigation of antibodies with fast clearance
I. Hötzel, F.-P. Theil, L. J. Bernstein, S. Prabhu, R. Deng, L. Quintana, J. Lutman, R. Sibia, P. Chan, D. Bumbaca, et al · 2012
Earlier work this paper cites.
Revisiting frank-wolfe: Projection-free sparse convex optimization
M. Jaggi · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
A note on the evaluation of generative models
L. Theis, A. v. d. Oord, and M. Bethge · 2015
Earlier work this paper cites.
Convolutional sequence to sequence learning
J. Gehring, M. Auli, D. Grangier, D. Yarats, and Y. N. Dauphin · 2017
Earlier work this paper cites.
Biophysical properties of the clinical-stage antibody landscape
T. Jain, T. Sun, S. Durand, A. Hall, N. R. Houston, J. H. Nett, B. Sharkey, B. Bobrowicz, I. Caffry, Y. Yu, et al · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
Cited alongside, same era.
Stein variational gradient descent as gradient flow
Q. Liu · 2017
Cited alongside, same era.
Edlib: a c/c++ library for fast, exact sequence alignment using edit distance
M. Šošić and M. Šikić · 2017
Cited alongside, same era.
Tree edit distance learning via adaptive symbol embeddings
B. Paaßen, C. Gallicchio, A. Micheli, and B. Hammer · 2018
Cited alongside, same era.
Assessing generative models via precision and recall
The hypervolume indicator: Problems and algorithms
A. P. Guerreiro, C. M. Fonseca, and L. Paquete · 2020
Later among the works it cites.
pymoo: Multi-objective optimization in python
B. J and D. K · 2020
Later among the works it cites.
Function-guided protein design by deep manifold sampling
V. Gligorijevic, D. Berenberg, S. Ra, A. Watkins, S. Kelow, K. Cho, and R. Bonneau · 2021
Later among the works it cites.
Profiling pareto front with multi-objective stein variational gradient descent
X. Liu, X. Tong, and Q. Liu · 2021
Later among the works it cites.
Expanding functional protein sequence spaces using generative adversarial networks
D. Repecka, V. Jauniskis, L. Karpus, E. Rembeza, I. Rokaitis, J. Zrimec, S. Poviloniene, A. Laurynenas, S. Viknander, W. Abuajwa, et al · 2021
Later among the works it cites.
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M. Bailly, C. Mieczkowski, V. Juan, E. Metwally, D. Tomazela, J. Baker, M. Uchida, E. Kofman, F. Raoufi, S. Motlagh, et al · 2020
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