Publications related to Learning Activation Functions in Deep (Spline) Neural Networks

Automated Synthesis of Adversarial Workloads for Network Functions

Arseniy Zaostrovnykh, Rishabh Ramesh Iyer, Jonas Fietz, Luis David Figueiredo Mascarenhas Moreira Pedrosa

Software network functions promise to simplify the deployment of network services and reduce network operation cost. However, they face the challenge of unpredictable performance. Given this performance variability, it is imperative that during deployment, ...

ACM2018

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

Alexios Konstantinos Balatsoukas Stimming

Full-duplex systems require very strong self-interference cancellation in order to operate correctly and a significant part of the self-interference signal is due to non-linear effects created by various transceiver impairments. As such, linear cancellatio ...

IEEE2018

On The Robustness of a Neural Network

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. W ...

2017

A Formally Verified NAT

George Candea, Solal Vincenzo Pirelli, Arseniy Zaostrovnykh, Luis David Figueiredo Mascarenhas Moreira Pedrosa

We present a Network Address Translator (NAT) written in C and proven to be semantically correct according to RFC 3022, as well as crash-free and memory-safe. There exists a lot of recent work on network verification, but it mostly assumes models of networ ...

Assoc Computing Machinery2017

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016

Multilingual Deep Neural Network based Acoustic Modeling For Rapid Language Adaptation

Hervé Bourlard, Petr Motlicek, David Imseng

This paper presents a study on multilingual deep neural network (DNN) based acoustic modeling and its application to new languages. We investigate the effect of phone merging on multilingual DNN in context of rapid language adaptation. Moreover, the combin ...

2014

Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Hynek Hermansky, Joel Praveen Pinto

We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model - artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm genera ...

2007

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Hynek Hermansky, Joel Praveen Pinto

We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model - artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm genera ...

IDIAP2007

Spectral Entropy Feature in Full-Combination Multi-Stream for Robust ASR

Hervé Bourlard, Hemant Misra

In a recent paper, we reported promising automatic speech recognition results obtained by appending spectral entropy features to PLP features. In the present paper, spectral entropy features are used along with PLP features in the framework of multi-stream ...

2005

Learning Activation Functions in Deep (Spline) Neural Networks

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Automated Synthesis of Adversarial Workloads for Network Functions

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

On The Robustness of a Neural Network

A Formally Verified NAT

Learning to Assign Orientations to Feature Points

Multilingual Deep Neural Network based Acoustic Modeling For Rapid Language Adaptation

Network Simplification: the Gaussian N-Relay Diamond Network

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Spectral Entropy Feature in Full-Combination Multi-Stream for Robust ASR

Network Simplification: the Gaussian N-Relay Diamond Network

A Formally Verified NAT

Learning to Assign Orientations to Feature Points

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Non-Linear Digital Self-Interference Cancellation for In-Band Full-Duplex Radios Using Neural Networks

Automated Synthesis of Adversarial Workloads for Network Functions

Exploiting Phoneme Similarities in Hybrid HMM-ANN Keyword Spotting

Multilingual Deep Neural Network based Acoustic Modeling For Rapid Language Adaptation

On The Robustness of a Neural Network

Spectral Entropy Feature in Full-Combination Multi-Stream for Robust ASR