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Found 8 papers, 5 papers with code
GOAL: A Generalist Combinatorial Optimization Agent Learning
Finally we showcase the strong transfer learning capacity of GOAL by fine-tuning it on several new problems.
BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
In this paper, we present a novel formulation of Combinatorial Optimization Problems (COPs) as Markov Decision Processes (MDPs) that effectively leverages common symmetries of COPs to improve out-of-distribution robustness.
Combinatorial Optimization
Out-of-Distribution Generalization
On the Generalization of Neural Combinatorial Optimization Heuristics
Neural Combinatorial Optimization approaches have recently leveraged the expressiveness and flexibility of deep neural networks to learn efficient heuristics for hard Combinatorial Optimization (CO) problems.
Structured Time Series Prediction without Structural Prior
Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.).
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Distributional Reinforcement Learning for Energy-Based Sequential Models
Global Autoregressive Models (GAMs) are a recent proposal [Parshakova et al., CoNLL 2019] for exploiting global properties of sequences for data-efficient learning of seq2seq models.
Distributional Reinforcement Learning
reinforcement-learning
+2
Global Autoregressive Models for Data-Efficient Sequence Learning
In the second step, we use this GAM to train (by distillation) a second autoregressive model that approximates the \emph{normalized} distribution associated with the GAM, and can be used for fast inference and evaluation.
Convolution, attention and structure embedding
Deep neural networks are composed of layers of parametrised linear operations intertwined with non linear activations.
A conjugate prior for the Dirichlet distribution
This note investigates a conjugate class for the Dirichlet distribution class in the exponential family.
