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Scalable Unsupervised Domain Adaptation for Electron Microscopy

Publications associées (34)

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Adaptive sentiment-aware one-class collaborative filtering

Andrei Popescu-Belis, Nikolaos Pappas

This paper presents a novel application of sentiment analysis to recommender systems relying on explicit one-class user feedback (favorites or likes), namely joint models of unary feedback and sentiment of free-form user comments. This combination is achie ...

Elsevier2016

Dictionary learning for fast classification based on soft-thresholding

Pascal Frossard, Alhussein Fawzi

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Frank Grégoire Jean de Morsier

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Semi-Supervised Method for Multi-Category Emotion Recognition in Tweets

Pearl Pu Faltings, Claudiu-Cristian Musat, Valentina Sintsova

Each tweet is limited to 140 characters. This constraint surprisingly makes Twitter a more spontaneous platform to express our emotions. Detecting emotions and correctly classifying them automatically is an increasingly important task if we want to underst ...

2014

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Learning to Detect Objects with Minimal Supervision

Karim Ali

Many classes of objects can now be successfully detected with statistical machine learning techniques. Faces, cars and pedestrians, have all been detected with low error rates by learning their appearance in a highly generic manner from extensive training ...

EPFL2012

Open-Ended Learning of Visual and Multi-Modal Patterns

Jie Luo

A common trend in machine learning and pattern classification research is the exploitation of massive amounts of information in order to achieve an increase in performance. In particular, learning from huge collections of data obtained from the web, and us ...

EPFL2011

Open-ended Learning of Visual and Multi-modal Patterns

Jie Luo

Ecole polytechnique fédérale de Lausanne2011

Learning Pose Invariant and Covariant Classifiers from Image Sequences

Mustafa Özuysal

Object tracking and detection over a wide range of viewpoints is a long-standing problem in Computer Vision. Despite significant advance in wide-baseline sparse interest point matching and development of robust dense feature models, it remains a largely op ...

EPFL2010

Probabilistic models for multi-classifier biometric authentication using quality measures

Jonas Richiardi

Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

EPFL2008