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Explore reward fine-tuning for dynamical generative models through stochastic optimal control, featuring a novel Adjoint Matching algorithm for improved consistency and realism.
Explore advanced probabilistic inference techniques for language models using Twisted Sequential Monte Carlo, enhancing LLM capabilities and safety measures.
Explore a novel geometric deep learning framework for de novo genome assembly, enhancing path identification in complex assembly graphs for improved genomic sequence reconstruction.
Explore the Open MetaGenomic corpus for genomic language modeling, featuring 3.1T base pairs and 3.3B protein coding sequences. Learn about mixed-modality approaches and their impact on AI for drug discovery.
Explore protein ML representations, focusing on joint sequence-structure modeling. Learn about CHEAP embeddings and HPCT architecture for compact, flexible protein representations.
Explore Discrete Flow Matching for generating high-dimensional discrete data. Learn about probability paths, sampling techniques, and improved generative perplexity in AI applications.
Explore phenomics in drug discovery through microscopy and machine learning techniques with expert Anne Carpenter.
Explore ML-based docking techniques for predicting binding affinity in drug discovery, featuring insights from experts Stephan Thaler and Cristian Gabellini.
Explore ML-based docking for binding affinity prediction in drug discovery, featuring insights from experts Stephan Thaler and Cristian Gabellini.
Explore protein folding and design techniques with Alex Tong, delving into advanced machine learning applications for drug discovery and structural biology.
Explore target deconvolution techniques in drug discovery through hands-on lab exercises led by experts Ali Denton and Kristina Ulicna.
Explore molecular space and active learning techniques for drug discovery with AI pioneer Yoshua Bengio's insights from the 2024 ML Summer School at Mila.
Explore de novo molecule generation techniques with experts Emmanuel Bengio and Julien Roy in this recorded session from the 2024 Machine Learning for Drug Discovery Summer School.
Explore ML interatomic potentials for drug discovery with expert Gianni De Fabritiis. Gain insights into advanced techniques and applications in computational chemistry.
Explore advanced techniques for accelerating atomistic simulations, enhancing sampling methods, and optimizing dynamics in drug discovery research.
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