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Found 8 papers, 6 papers with code
Compositional diversity in visual concept learning
In few shot classification tasks, we find that people and the program induction model can make a range of meaningful compositional generalizations, with the model providing a strong account of the experimental data as well as interpretable parameters that reveal human assumptions about the factors invariant to category membership (here, to rotation and changing part attachment).
Learning Task-General Representations with Generative Neuro-Symbolic Modeling
We develop a generative neuro-symbolic (GNS) model of handwritten character concepts that uses the control flow of a probabilistic program, coupled with symbolic stroke primitives and a symbolic image renderer, to represent the causal and compositional processes by which characters are formed.
Generating new concepts with hybrid neuro-symbolic models
A second tradition has emphasized statistical knowledge, viewing conceptual knowledge as an emerging from the rich correlational structure captured by training neural networks and other statistical models.
A Linear Systems Theory of Normalizing Flows
Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables.
Learning a smooth kernel regularizer for convolutional neural networks
We propose a smooth kernel regularizer that encourages spatial correlations in convolution kernel weights.
Learning Inductive Biases with Simple Neural Networks
People use rich prior knowledge about the world in order to efficiently learn new concepts.
Detecting Adversarial Samples from Artifacts
Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
