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Found 6 papers, 5 papers with code
Planning from Pixels in Environments with Combinatorially Hard Search Spaces
The ability to form complex plans based on raw visual input is a litmus test for current capabilities of artificial intelligence, as it requires a seamless combination of visual processing and abstract algorithmic execution, two traditionally separate areas of computer science.
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.
Neuro-algorithmic Policies enable Fast Combinatorial Generalization
Furthermore, we show that for a certain subclass of the MDP framework, this can be alleviated by neuro-algorithmic architectures.
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.
Ranked #2 on
Graph Matching
on PASCAL VOC
Optimizing Rank-based Metrics with Blackbox Differentiation
Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models.
Differentiation of Blackbox Combinatorial Solvers
Achieving fusion of deep learning with combinatorial algorithms promises transformative changes to artificial intelligence.
