
Explore model-based reasoning techniques and their applications in neurosymbolic programming for scientific problem-solving.

Explore neurosymbolic programming's fusion of deep learning and program synthesis, discovering how it enhances AI robustness, interpretability, and efficiency through symbolic and neural approaches.

Explore Scallop, a language combining deep learning and logical reasoning for efficient neurosymbolic programming. Learn its key features and applications in diverse AI tasks.

Explore computational imaging techniques used to capture black hole images and leverage data structure to extract evolving details of the Milky Way's supermassive black hole.

Explore AI-driven scientific discovery, integrating machine learning with expert knowledge to automate data analysis and accelerate research across diverse domains.

Explore data-efficient neurosymbolic generative models for molecular generation using learnable graph grammars, optimized for small datasets and physically synthesizable molecules.

Explore neural surrogates for program optimization, including case studies on CPU simulators and techniques like the Renamer and Turaco for constructing efficient program surrogates.

Explore symbolic structure inference from visual data using neuro-symbolic methods. Learn to leverage natural supervision for discovering repetitive patterns, object intrinsics, and grounded visual concepts.

Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.

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