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Explore identifiable representations for intervention extrapolation, improving generalizability in machine learning. Learn theoretical foundations and practical applications through simulations and Q&A.
Explore a novel approach to protein structure generation using continuous autoregressive language models. Learn about innovative techniques for in silico protein design and their potential applications.
Explore self-driving laboratories revolutionizing scientific discovery through AI and automation. Learn about cutting-edge applications, key elements, and opportunities in chemistry, materials science, and biotechnology.
Explore Weisfeiler Leman's application in Euclidean equivariant machine learning, focusing on graph neural networks, point clouds, and the WeLNet architecture for improved N-Body dynamics and molecular conformation generation.
Explore a novel latent factor model for estimating causal effects of combinatorial interventions, addressing challenges in factorial experiments and recommendation systems.
Explore Kolmogorov-Arnold Networks as alternatives to MLPs, offering improved accuracy and interpretability in AI. Discover their potential in data fitting, PDE solving, and scientific law discovery.
Explore D-Flow framework for controlled generation in AI models, optimizing source points through flow differentiation. Learn applications in inverse problems and conditional generation.
Explore EquiReact, an equivariant neural network for predicting chemical reaction properties using 3D structures. Learn its advantages in flexibility, extrapolation, and data efficiency.
Explore multimodal language models for mapping genotype-phenotype relationships. Learn about integrated genetics frameworks and their applications in analyzing cellular heterogeneity and gene polyfunctionality.
Explore generalized functions and their applications in causal models, including nondeterministic structural models and novel types of model reduction, leading to new definitions of causal abstraction.
Explore MolGroup: a novel approach to optimize auxiliary dataset selection in molecular machine learning, enhancing model performance through intelligent data grouping.
Explore rotational symmetrization for deep learning on point clouds, enhancing AI models for chemical and materials modeling with exact compliance to physical constraints.
Explore causal abstraction theory for uncovering and inducing interpretable structures in deep learning models, focusing on faithful explanations and interventions during analysis and training.
Explore Local Search GFlowNets, a novel algorithm enhancing sampling quality for structured sequence generation in drug discovery and other fields through improved targeting of high-reward regions.
Explore Mosaic-SDF, a novel 3D shape representation for generative models. Learn its design principles, preprocessing, and application in flow matching for efficient 3D generation.
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