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How Robust are Deep Neural Networks?
Convolutional and Recurrent, deep neural networks have been successful i...
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Conditional Autoencoders with Adversarial Information Factorization
Generative models, such as variational auto-encoders (VAE) and generativ...
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LatentPoison - Adversarial Attacks On The Latent Space
Robustness and security of machine learning (ML) systems are intertwined...
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GeoSeq2Seq: Information Geometric Sequence-to-Sequence Networks
The Fisher information metric is an important foundation of information ...
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Generative Adversarial Networks: An Overview
Generative adversarial networks (GANs) provide a way to learn deep repre...
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StackSeq2Seq: Dual Encoder Seq2Seq Recurrent Networks
A widely studied non-deterministic polynomial time (NP) hard problem lie...
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Sequence stacking using dual encoder Seq2Seq recurrent networks
A widely studied non-polynomial (NP) hard problem lies in finding a rout...
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Approximating meta-heuristics with homotopic recurrent neural networks
Much combinatorial optimisation problems constitute a non-polynomial (NP...
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Pillar Networks++: Distributed non-parametric deep and wide networks
In recent work, it was shown that combining multi-kernel based support v...
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Multi-kernel learning of deep convolutional features for action recognition
Image understanding using deep convolutional network has reached human-l...
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Bayesian Belief Updating of Spatiotemporal Seizure Dynamics
Epileptic seizure activity shows complicated dynamics in both space and ...
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Approximate Bayesian inference as a gauge theory
In a published paper [Sengupta, 2016], we have proposed that the brain (...
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