Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

2018

Prediction of patient-reported physical activity scores from wearable accelerometer data: a feasibility study

Inès Bahej, Martin Jaggi

Many diseases are characterized by limitations in mobility, including a wide range of musculoskeletal and neurological conditions. Reduced mobility impacts a patients ability to perform activities of daily living, which in turn reduces health-related quali ...

2018

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Chenxin Ma, Martin Jaggi

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 ...

2018

Decoupling Backpropagation using Constrained Optimization Methods

Johanni Michael Brea, Martin Jaggi

We propose BlockProp, a neural network training algorithm. Unlike backpropagation, it does not rely on direct top-to-bottom propagation of an error signal. Rather, by interpreting backpropagation as a constrained optimization problem we split the neural ne ...

2018

On Matching Pursuit and Coordinate Descent

Sai Praneeth Reddy Karimireddy, Martin Jaggi, Sebastian Urban Stich, Anant Raj

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers ...

2018

COLA: Decentralized Linear Learning

Lie He, Martin Jaggi

Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over ma ...

2018

Training DNNs with Hybrid Block Floating Point

Babak Falsafi, Tao Lin, Mario Paulo Drumond Lages De Oliveira, Martin Jaggi

The wide adoption of DNNs has given birth to unrelenting computing requirements, forcing datacenter operators to adopt domain-specific accelerators to train them. These accelerators typically employ densely packed full-precision floating-point arithmetic t ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

A Distributed Second-Order Algorithm You Can Trust

Thomas Hofmann, Aurélien Lucchi, Matilde Gargiani, Martin Jaggi

Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potenti ...

2018

Distributed Optimization with Arbitrary Local Solvers

Chenxin Ma, Martin Jaggi

With the growth of data and necessity for distributed optimization methods, solvers that work well on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing high ...

Taylor & Francis Ltd2017

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Matteo Pagliardini, Prakhar Gupta, Martin Jaggi

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...

2017

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

Thomas Hofmann, Aurélien Lucchi, Simon Müller, Martin Jaggi

2017

A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe

Martin Jaggi

2017

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Faster Coordinate Descent via Adaptive Importance Sampling

Volkan Cevher, Dmytro Perekrestenko, Martin Jaggi

Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our ...

PMLR2017

Generating Steganographic Text with LSTMs

Aikaterini Argyraki, Tina Fang, Martin Jaggi

Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem ba ...

2017

Learning Aerial Image Segmentation from Online Maps

Thomas Hofmann, Aurélien Lucchi, Martin Jaggi, Jan Dirk Wegner

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

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