Playlist: 50 videos

The Unreasonable Effectiveness of SAT Solving

0:48:51

An Automated Approach to the Collatz Conjecture

Emre Yolcu (Carnegie Mellon University)
https://simons.berkeley.edu/talks/sat-math
Theoretical Foundations of SAT/SMT Solving
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0:44:19

Predicting Satisfiability at the Phase Transition via End-to-End Learning

Kevin Leyton Brown (University of British Columbia)
https://simons.berkeley.edu/talks/predicting-satisfiability-phase-transition-end-end-learning
Theoretical Foundations of SAT/SMT Solving
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0:38:1

Learning to Schedule Heuristics in Branch and Bound

Elias Khalil (University of Toronto)
https://simons.berkeley.edu/talks/learning-schedule-heuristics-branch-and-bound
Theoretical Foundations of SAT/SMT Solving
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0:33:6

Extensions of CDCL Branching Heuristics by Exploration during Conflict Depression

Md. Solimul Chowdhury (University of Alberta)
https://simons.berkeley.edu/talks/ml-solvers
Theoretical Foundations of SAT/SMT Solving
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0:31:51

Learning to Solve SMT Formulas

Mislav Balunović (ETH Zurich)
https://simons.berkeley.edu/talks/learning-solve-smt-formulas
Theoretical Foundations of SAT/SMT Solving
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0:35:55

Preprocessing SAT, MaxSAT, and QBF

Jeremias Berg (University of Helsinki)
https://simons.berkeley.edu/talks/preprocessing
Theoretical Foundations of SAT/SMT Solving
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0:41:10

Preprocessing SAT, MaxSAT, and QBF

Martina Seidl (Johannes Kepler University)
https://simons.berkeley.edu/talks/preprocessing
Theoretical Foundations of SAT/SMT Solving
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0:43:25

Preprocessing SAT, MaxSAT, and QBF

Benjamin Kiesl (SAP)
https://simons.berkeley.edu/talks/preprocessing
Theoretical Foundations of SAT/SMT Solving
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0:43:40

Unifying Logical and Statistical AI with Markov Logic

Pedro Domingos (University of Washington)
https://simons.berkeley.edu/talks/unifying-logical-and-statistical-ai-markov-logic
Theoretical Foundations of SAT/SMT Solving
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0:25:6

Constrained Gradient Descent Algorithm for Testing Neural Networks

Vineel Nagisetty (University of Waterloo)
https://simons.berkeley.edu/talks/constrained-gradient-descent-algorithm-testing-neural-networks
Theoretical Foundations of SAT/SMT Solving
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