Publications related to Softmax function

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Volkan Cevher, Paul Thierry Yves Rolland, Ali Kavis

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate ...

2019

Deep Spline Networks With Control Of Lipschitz Regularity

Michaël Unser, Shayan Aziznejad

The motivation for this work is to improve the performance of deep neural networks through the optimization of the individual activation functions. Since the latter results in an infinite-dimensional optimization problem, we resolve the ambiguity by search ...

IEEE2019

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Matthieu Wyart, Mario Geiger, Marco Baity-Jesi, Stefano Spigler, Levent Dogus Sagun

Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understandin ...

AMER PHYSICAL SOC2019

Deep Visual Re-Identification with Confidence

Alexandre Massoud Alahi, Sven Kreiss, George Adaimi

Transportation systems often rely on understanding the flow of vehicles or pedestrian. From traffic monitoring at the city scale, to commuters in train terminals, recent progress in sensing technology make it possible to use cameras to better understand th ...

2019

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

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Martin Jaggi, Chenxin Ma

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme a ...

MICROTOME PUBL2018

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

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Softmax function

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Deep Spline Networks With Control Of Lipschitz Regularity

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Deep Visual Re-Identification with Confidence

A Representer Theorem for Deep Neural Networks

Novelty Detection in Convolutional Neural Networks Using Density Forests

Automated Synthesis of Adversarial Workloads for Network Functions

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

A Formally Verified NAT

Graph Chatbot

Chat with Graph Search

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Novelty Detection in Convolutional Neural Networks Using Density Forests

A Representer Theorem for Deep Neural Networks

Deep Spline Networks With Control Of Lipschitz Regularity

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Automated Synthesis of Adversarial Workloads for Network Functions

Deep Visual Re-Identification with Confidence

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

A Formally Verified NAT

CoCoA: A General Framework for Communication-Efficient Distributed Optimization