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Combinatorial Optimization Edit

36 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.

3,613

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.

250

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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Reinforcement Learning for Solving the Vehicle Routing Problem

Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance.

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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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Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets

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

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Exact Combinatorial Optimization with Graph Convolutional Neural Networks

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

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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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Learning to Perform Local Rewriting for Combinatorial Optimization

Search-based methods for hard combinatorial optimization are often guided by heuristics.

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