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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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4 results for “MAXSAT”

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solvercs.LG

SATNet integrates logical reasoning into deep learning with a differentiable MAXSAT solver.

problem Integrating logical reasoning into deep learning architectures.

method Differentiable MAXSAT solver based on fast coordinate descent for SDP.

result Minimally supervised learning of logical structures in deep learning systems.

deep learning logical reasoning MAXSAT differentiable sudoku

2019-05-29abs ↗pdf ↗

Techniques for Symbol Grounding with SATNetcs.AI

SATNet solves the Symbol Grounding Problem, enabling self-supervised learning.

problem Mapping visual inputs to symbolic variables without explicit supervision.

method Self-supervised pre-training pipeline and proofreading method.

result SATNet achieves full accuracy with no label leakage, surpassing state-of-the-art.

SATNet Symbol Grounding Problem Neurosymbolic Systems Self-supervised Learning Visual Sudoku

2021-06-16abs ↗pdf ↗

Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPsmath.OC

A new method solves diagonally constrained SDPs quickly and accurately.

problem Solving large-scale diagonally constrained SDPs efficiently.

method Combines momentum from convex optimization with coordinate descent and matrix factorization.

result Local linear convergence and first-order critical point convergence proved.

SDP Diagonally constrained Accelerated optimization Momentum Coordinate descent

2021-06-16abs ↗pdf ↗

Deep Optimisation: Solving Combinatorial Optimisation Problems using Deep Neural Networkscs.LG

Deep Optimisation (DO) combines evolutionary search with Deep Neural Networks (DNNs) in a novel way - not for optimising a learning algorithm, but for finding a solution to an optimisation problem. Deep learning has been successfully applied to classification, regression, decision and generative tasks and in this paper…

2018-11-02abs ↗pdf ↗