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Polynomial-time universality and limitations of deep learning

Related publications (79)

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Geographical Feature Extraction for Entities in Location-based Social Networks

Pearl Pu Faltings

Location-based embedding is a fundamental problem to solve in location-based social network (LBSN). In this paper, we propose a geographical convolutional neural tensor network (GeoCNTN) as a generic embedding model. GeoCNTN first takes the raw location da ...

ASSOC COMPUTING MACHINERY2018

Geodesic Convolutional Shape Optimization

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Edoardo Remelli

Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes ...

2018

Universal adversarial perturbations

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for co ...

Ieee2017

Distributed Learning for Stochastic Generalized Nash Equilibrium Problems

Ali H. Sayed

This paper examines a stochastic formulation of the generalized Nash equilibrium problem where agents are subject to randomness in the environment of unknown statistical distribution. We focus on fully distributed online learning by agents and employ penal ...

IEEE2017

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

Robust Online Time Series Prediction with Recurrent Neural Networks

Karl Aberer, Tian Guo

Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which ...

Ieee2016

ProteusTM: Abstraction Meets Performance in Transactional Memory

Rachid Guerraoui, Anne-Marie Kermarrec, Diego Didona

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no un ...

Assoc Computing Machinery2016

Preconditioned Spectral Descent for Deep Learning

Volkan Cevher, Ya-Ping Hsieh, Edo Collins

Deep learning presents notorious computational challenges. These challenges in- clude, but are not limited to, the non-convexity of learning objectives and estimat- ing the quantities needed for optimization algorithms, such as gradients. While we do not a ...

2015

Discriminative Learning of Deep Convolutional Feature Point Descriptors

Pascal Fua, Eduard Trulls Fortuny

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patc ...

2015

Exponential Functionals of Brownian Motion and Explosion Times of a System of Semilinear SPDEs

We investigate lower and upper bounds for the blowup times of a system of semilinear SPDEs. Under certain conditions on the system parameters, we obtain explicit solutions of a related system of random PDEs, which allows us to use a formula due to Yor to o ...

2013