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Found 7 papers, 3 papers with code
Compositional Generalization by Factorizing Alignment and Translation
Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution.
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
We aim instead at learning a policy that generalizes across heterogeneous MILPs: our main hypothesis is that parameterizing the state of the B&B search tree can aid this type of generalization.
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution.
Modularity Matters: Learning Invariant Relational Reasoning Tasks
We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: the MNIST Parity task and the colorized Pentomino task.
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
A commonly-used nonlinear operator is the so-called rectified linear unit (ReLU), whose output is just the maximum between its input value and zero.
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset.
Learning Parameters for Weighted Matrix Completion via Empirical Estimation
These numerical experiments show that for a variety of easily computable empirical weights, weighted nuclear norm minimization outperforms unweighted nuclear norm minimization in the non-uniform sampling regime.
