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Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Chanakya Ekbote · Moksh Jain · Payel Das · Yoshua Bengio

Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects x given a reward function R(x), indicating the utility of the object and trained independently from the GFlowNet by supervised learning to predict a desirable property y given x. We hypothesize that this can lead to incompatibility between the inductive optimization biases in training R and in training the GFlowNet, potentially leading to worse samples and slow adaptation to changes in the distribution. In this work, we build upon recent work on jointly learning energy-based models with GFlowNets and extend it to learn the joint over multiple variables, which we call Joint Energy-Based GFlowNets (JEBGFNs), such as peptide sequences and their antimicrobial activity. Joint learning of the energy-based model, used as a reward for the GFlowNet, can resolve the issues of incompatibility since both the reward function R and the GFlowNet sampler are trained jointly. We find that this joint training or joint energy-based formulation leads to significant improvements in generating anti-microbial peptides. As the training sequences arose out of evolutionary or artificial selection for high antibiotic activity, there is structure in the distribution of sequences that reveals information about the antibiotic activity, giving an advantage to modeling their joint generatively vs. pure discriminative modeling. We also evaluate JEBGFN in an active learning setting for discovering anti-microbial peptides.

Author Information

Chanakya Ekbote (Mila)
Moksh Jain (MILA / UdeM)

MSc Student at MILA interested in learning based approaches for global optimization.

Payel Das (IBM Research)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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