Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Yoshua Bengio

Yoshua Bengio

Learning GFlowNets from partial episodes for improved convergence and stability


Sep 26, 2022
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov, Emmanuel Bengio, Moksh Jain, Andrei Nica, Tom Bosc, Yoshua Bengio, Nikolay Malkin

Figure 1 for Learning GFlowNets from partial episodes for improved convergence and stability
Figure 2 for Learning GFlowNets from partial episodes for improved convergence and stability
Figure 3 for Learning GFlowNets from partial episodes for improved convergence and stability
Figure 4 for Learning GFlowNets from partial episodes for improved convergence and stability

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Interventional Causal Representation Learning


Sep 24, 2022
Kartik Ahuja, Yixin Wang, Divyat Mahajan, Yoshua Bengio

Figure 1 for Interventional Causal Representation Learning
Figure 2 for Interventional Causal Representation Learning

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection


Sep 18, 2022
Bonaventure F. P. Dossou, Dianbo Liu, Xu Ji, Moksh Jain, Almer M. van der Sloot, Roger Palou, Michael Tyers, Yoshua Bengio

Figure 1 for Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Figure 2 for Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Figure 3 for Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
Figure 4 for Graph-Based Active Machine Learning Method for Diverse and Novel Antimicrobial Peptides Generation and Selection
* Under Review at Sciences Advances 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization


Sep 13, 2022
Leo Feng, Padideh Nouri, Aneri Muni, Yoshua Bengio, Pierre-Luc Bacon

Figure 1 for Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Figure 2 for Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Figure 3 for Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization
Figure 4 for Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Unifying Generative Models with GFlowNets


Sep 06, 2022
Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

* Accepted to ICML 2022 Beyond Bayes workshop 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N


Aug 15, 2022
Tianyu Zhang, Andrew Williams, Soham Phade, Sunil Srinivasa, Yang Zhang, Prateek Gupta, Yoshua Bengio, Stephan Zheng

Figure 1 for AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Figure 2 for AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Figure 3 for AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
Figure 4 for AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
* 12 pages (21 with appendices), 5 figures. For associated working group, see https://www.ai4climatecoop.org/ 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning


Aug 10, 2022
Siba Moussa, Michael Kilgour, Clara Jans, Alex Hernandez-Garcia, Miroslava Cuperlovic-Culf, Yoshua Bengio, Lena Simine

Figure 1 for Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Figure 2 for Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Figure 3 for Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints


Aug 08, 2022
Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien

Figure 1 for Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Figure 2 for Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Figure 3 for Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Figure 4 for Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
* Code available at https://github.com/gallego-posada/constrained_sparsity 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Discrete Key-Value Bottleneck


Jul 22, 2022
Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf

Figure 1 for Discrete Key-Value Bottleneck
Figure 2 for Discrete Key-Value Bottleneck
Figure 3 for Discrete Key-Value Bottleneck
Figure 4 for Discrete Key-Value Bottleneck

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Lookback for Learning to Branch


Jun 30, 2022
Prateek Gupta, Elias B. Khalil, Didier Chetélat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar

Figure 1 for Lookback for Learning to Branch
Figure 2 for Lookback for Learning to Branch
Figure 3 for Lookback for Learning to Branch
Figure 4 for Lookback for Learning to Branch
* Under review 
   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email
1
2
3
4
5
6
7
>>