Excited to share our new preprint on refrigerant discovery. It started as a bold idea brought by Anna Vartanyan @annadaneau, and thanks the amazing work of Adrien Goldszal, @diegocalanzone, Vincent Taboga, it became a reality.
Pierre-Luc Bacon
@pierrelux
- πCongrats to Tianwei for his remarkable intuition and dedication. Our paper provides theoretical grounding for self-predictive RL and bridges to established theories on learning approximate information states in POMDPs.Replying to @twni2016Our unification justifies *auxiliary tasks*. Training an encoder end-to-end for maximizing returns with the auxiliary task of learning ZP, as in SPR paper arxiv.org/abs/2007.05929, promises to learn self-predictive abstraction, also known as bisimulation and model-irrelevance.
- π Excited to share our new paper accepted at AISTATS 2024: "Maximum Entropy GFlowNets with Soft Q-Learning" π arxiv.org/abs/2312.14331. Kudos to Sobhan Mohammadpour for his stellar work in his MSc thesis!
- Congrats to Mahan, who is finishing his Master's thesis in beauty with this second paper.Course Correcting Koopman Representations Accepted at #ICLR2024! We identify problems with unrolling in imagination and propose an unconventional, simple, yet effective solution: periodically "πππππππ πππ" the latent. π arxiv.org/abs/2310.15386 @GoogleDeepMind 1/π§΅
- π Proud of @MahanFathi's msc work in collaboration with @GoogleDeepMind! Block-State Transformer elegantly combine SSM's long-range efficiency and Transformer's NLP advantage. π BST outperforms in language modeling and scales easily. Don't miss their poster at #NeurIPS2023Why not get the best of both worlds by combining SSMs and Transformers? Excited to share our work at #NeurIPS2023: "Block-State Transformers." BST hits new highs in long-range language modeling and LRA tasks. paper: arxiv.org/abs/2306.09539 1/
