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Explore neural language models for mathematical reasoning, bridging informal and formal approaches through NaturalProver and Draft, Sketch, and Prove techniques.
Explore AI-driven scientific discovery, integrating machine learning with expert knowledge to automate data analysis and accelerate research across diverse domains.
Explore advanced concepts in probabilistic programming, focusing on practical applications and techniques for scientific computing and data analysis.
Explore probabilistic programming fundamentals and applications with MIT expert Vikash Mansinghka in this comprehensive tutorial.
Explore model-based reasoning techniques and their applications in neurosymbolic programming for scientific problem-solving.
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.
Learn deductive program synthesis through hands-on examples, exploring SSL rules, recursion handling, and step-by-step derivation of provably correct pointer-manipulating programs.
Explore advanced program architecture search methods, including NEAR and DreamCoder, and their applications in neurosymbolic learning for behavioral neuroscience.
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 automated program architecture search methods, including enumerative search and RobustFill, with hands-on exercises and a survey of additional techniques.
Explore AlphaCode's groundbreaking AI system for competitive programming, learning its architecture, training, and evaluation on Codeforces platform to enhance problem-solving skills.
Explore program synthesis evolution as a learning tool, from early data-driven models to modern neurosymbolic algorithms.
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 neural approaches to automatically repair compiler errors in C programs, including pre-trained and fine-tuned models for practical error-fixing experience.
Explore brain responses to code comprehension and learn to modify programs for enhanced neural activation using backpropagation and Gumbel softmax.
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