Xaq Pitkow | DeepAI

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∙ 03/16/2022

Understanding robustness and generalization of artificial neural networks through Fourier masks

Despite the enormous success of artificial neural networks (ANNs) in man...

0 Nikos Karantzas, et al. ∙

∙ 10/18/2021

Interpolating between sampling and variational inference with infinite stochastic mixtures

Sampling and Variational Inference (VI) are two large families of method...

0 Richard D. Lange, et al. ∙

∙ 10/13/2021

Two-argument activation functions learn soft XOR operations like cortical neurons

Neurons in the brain are complex machines with distinct functional compa...

0 KiJung Yoon, et al. ∙

∙ 07/12/2021

Generalization of graph network inferences in higher-order probabilistic graphical models

Probabilistic graphical models provide a powerful tool to describe compl...

0 Yicheng Fei, et al. ∙

∙ 05/03/2021

AvaTr: One-Shot Speaker Extraction with Transformers

To extract the voice of a target speaker when mixed with a variety of ot...

0 Shell Xu Hu, et al. ∙

∙ 12/10/2020

Flexible Few-Shot Learning with Contextual Similarity

Existing approaches to few-shot learning deal with tasks that have persi...

17 Mengye Ren, et al. ∙

∙ 11/11/2019

Learning From Brains How to Regularize Machines

Despite impressive performance on numerous visual tasks, Convolutional N...

36 Zhe Li, et al. ∙

∙ 08/13/2019

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics

Continuous control and planning remains a major challenge in robotics an...

4 Saurabh Daptardar, et al. ∙

∙ 05/31/2019

Improved memory in recurrent neural networks with sequential non-normal dynamics

Training recurrent neural networks (RNNs) is a hard problem due to degen...

0 A. Emin Orhan, et al. ∙

∙ 02/02/2019

Belief dynamics extraction

Animal behavior is not driven simply by its current observations, but is...

0 Arun Kumar, et al. ∙

∙ 05/24/2018

Inverse POMDP: Inferring What You Think from What You Do

Complex behaviors are often driven by an internal model, which integrate...

0 Zhengwei Wu, et al. ∙

∙ 03/21/2018

Inference in Probabilistic Graphical Models by Graph Neural Networks

A useful computation when acting in a complex environment is to infer th...

0 KiJung Yoon, et al. ∙

∙ 03/16/2018

Reviving and Improving Recurrent Back-Propagation

In this paper, we revisit the recurrent back-propagation (RBP) algorithm...

0 Renjie Liao, et al. ∙

∙ 01/31/2017

Skip Connections Eliminate Singularities

Skip connections made the training of very deep networks possible and ha...

0 A. Emin Orhan, et al. ∙

∙ 06/02/2011

Learning unbelievable marginal probabilities

Loopy belief propagation performs approximate inference on graphical mod...

0 Xaq Pitkow, et al. ∙

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