no code implementations • 8 Jun 2024 • George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang

GFlowNets have exhibited promising performance in generating diverse candidates with high rewards.

no code implementations • 2 Jun 2024 • Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh Susskind, Navdeep Jaitly, Shuangfei Zhai

Diffusion models have become the de-facto approach for generating visual data, which are trained to match the distribution of the training dataset.

no code implementations • 27 May 2024 • Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu

To this end, we formulate an learnable energy-based latent space, and propose Noise-intensified Telescoping density-Ratio Estimation (NTRE) scheme for variational learning of an accurate latent space model without costly Markov Chain Monte Carlo.

no code implementations • 29 Oct 2023 • Zhenggqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning

Next, it estimates the rare event probability by utilizing importance sampling in conjunction with the last proposal.

1 code implementation • 12 Oct 2023 • Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio

Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities.

2 code implementations • 4 Oct 2023 • Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine learning and statistics.

2 code implementations • 4 Oct 2023 • Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.

1 code implementation • 4 Oct 2023 • Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio

We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly.

1 code implementation • 3 Oct 2023 • Jean-Pierre Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Thomas Jiralerspong, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio

We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call $\Delta$-amortized inference ($\Delta$-AI).

no code implementations • CVPR 2023 • Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang

We identify a phenomenon named player domination in the bargaining game, namely that the existing max-based approaches, such as MAX and MSD, do not converge.

1 code implementation • 26 May 2023 • Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan

In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space.

1 code implementation • 19 Feb 2023 • Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures through the lens of "inference as control".

1 code implementation • 11 Feb 2023 • Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a series of decision-making steps.

2 code implementations • 3 Feb 2023 • Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio

Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood).

1 code implementation • 30 Jan 2023 • Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.

no code implementations • 24 Oct 2022 • Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio

These methods face two important challenges: (a) the posterior distribution over masks can be highly multi-modal which can be difficult to approximate with standard variational inference and (b) it is not trivial to fully utilize sample-dependent information and correlation among dropout masks to improve posterior estimation.

no code implementations • 7 Oct 2022 • Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio

We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments.

1 code implementation • 3 Oct 2022 • Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity.

1 code implementation • 2 Oct 2022 • Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs.

1 code implementation • 1 Oct 2022 • Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan

Conformal prediction is a distribution-free technique for establishing valid prediction intervals.

no code implementations • 6 Sep 2022 • Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

Our framework provides a means for unifying training and inference algorithms, and provides a route to shine a unifying light over many generative models.

1 code implementation • 7 Jun 2022 • Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

Consequently, an emerging line of work has focused on learning an ensemble of neural networks to defend against adversarial attacks.

1 code implementation • 2 Mar 2022 • Moksh Jain, Emmanuel Bengio, Alex-Hernandez Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Micheal Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

In this work, we propose an active learning algorithm leveraging epistemic uncertainty estimation and the recently proposed GFlowNets as a generator of diverse candidate solutions, with the objective to obtain a diverse batch of useful (as defined by some utility function, for example, the predicted anti-microbial activity of a peptide) and informative candidates after each round.

2 code implementations • 3 Feb 2022 • Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio

We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.

no code implementations • ICLR 2022 • Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

Black-box optimization formulations for biological sequence design have drawn recent attention due to their promising potential impact on the pharmaceutical industry.

no code implementations • 17 Aug 2021 • Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng

In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution.

2 code implementations • NeurIPS 2021 • Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish

To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD.

no code implementations • 5 Jun 2021 • Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron Courville

Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization?

no code implementations • ICLR 2021 • Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for likelihood-free inference where the evaluation of likelihood function is intractable but sampling / simulating data from the model is possible.

1 code implementation • 20 Oct 2020 • Yanzhi Chen, Dinghuai Zhang, Michael Gutmann, Aaron Courville, Zhanxing Zhu

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible.

1 code implementation • ICML 2020 • Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

Specifically, we discriminate texture from shape based on local self-information in an image, and adopt a Dropout-like algorithm to decorrelate the model output from the local texture.

4 code implementations • 2 Mar 2020 • David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world.

no code implementations • NeurIPS 2020 • Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Randomized classifiers have been shown to provide a promising approach for achieving certified robustness against adversarial attacks in deep learning.

2 code implementations • NeurIPS 2019 • Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks.

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