Reuben Feinman | DeepAI

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∙ 05/30/2023

Compositional diversity in visual concept learning

Humans leverage compositionality to efficiently learn new concepts, unde...

0 Yanli Zhou, et al. ∙

∙ 06/25/2020

Learning Task-General Representations with Generative Neuro-Symbolic Modeling

A hallmark of human intelligence is the ability to interact directly wit...

5 Reuben Feinman, et al. ∙

∙ 03/19/2020

Generating new concepts with hybrid neuro-symbolic models

Human conceptual knowledge supports the ability to generate novel yet hi...

14 Reuben Feinman, et al. ∙

∙ 07/15/2019

Robust Nonlinear Component Estimation with Tikhonov Regularization

Learning reduced component representations of data using nonlinear trans...

2 Reuben Feinman, et al. ∙

∙ 03/05/2019

Learning a smooth kernel regularizer for convolutional neural networks

Modern deep neural networks require a tremendous amount of data to train...

0 Reuben Feinman, et al. ∙

∙ 02/08/2018

Learning Inductive Biases with Simple Neural Networks

People use rich prior knowledge about the world in order to efficiently ...

0 Reuben Feinman, et al. ∙

∙ 03/01/2017

Detecting Adversarial Samples from Artifacts

Deep neural networks (DNNs) are powerful nonlinear architectures that ar...

0 Reuben Feinman, et al. ∙

∙ 10/03/2016

cleverhans v2.0.0: an adversarial machine learning library

cleverhans is a software library that provides standardized reference im...

0 Nicolas Papernot, et al. ∙

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