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# Combinatorial Optimization Edit

22 papers with code · Methodology

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# Pointer Networks

It differs from the previous attention attempts in that, instead of using attention to blend hidden units of an encoder to a context vector at each decoder step, it uses attention as a pointer to select a member of the input sequence as the output.

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# Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

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# Attention, Learn to Solve Routing Problems!

The recently presented idea to learn heuristics for combinatorial optimization problems is promising as it can save costly development.

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# Learning Combinatorial Optimization Algorithms over Graphs

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

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# Neural Combinatorial Optimization with Reinforcement Learning

Despite the computational expense, without much engineering and heuristic designing, Neural Combinatorial Optimization achieves close to optimal results on 2D Euclidean graphs with up to 100 nodes.

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# Model Vulnerability to Distributional Shifts over Image Transformation Sets

28 Mar 2019ricvolpi/generalize-unseen-domains

We are concerned with the vulnerability of computer vision models to distributional shifts.

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# Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms

5 Mar 2018hazimehh/L0Learn

While these methods lead to estimators with excellent statistical properties, often there is a price to pay in terms of a steep increase in computation times, especially when compared to highly efficient popular algorithms for sparse learning (e. g., based on $L_1$-regularization) that scale to much larger problem sizes.

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# Exact Combinatorial Optimization with Graph Convolutional Neural Networks

4 Jun 2019ds4dm/learn2branch

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.

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# Deep Learning as a Mixed Convex-Combinatorial Optimization Problem

Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case.

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# Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

10 Sep 2018qcappart/learning-DD

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems.

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