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Improving Generalization of Pretrained Language Models

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Phrase-based Image Captioning

Rémi Philippe Lebret, Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This m ...

2015

Phrase-based Image Captioning

Rémi Philippe Lebret, Ronan Collobert, Pedro Henrique Oliveira Pinheiro

JMLR2015

Simple Image Description Generator via a Linear Phrase-based Model

Rémi Philippe Lebret, Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Idiap2015

Adaptive regularized diffusion adaptation over multitask networks

Ali H. Sayed, Sadaf Monajemi

The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn ab ...

2015

Word Embeddings through Hellinger PCA

Rémi Philippe Lebret, Ronan Collobert

Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically simplify the word embe ...

2014

Is Deep Learning Really Necessary for Word Embeddings?

Rémi Philippe Lebret, Ronan Collobert, Joël Yvon Roland Legrand

Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically sim- plify the word embed ...

Idiap2013

A Survey on Language Modeling using Neural Networks

Nikolaos Pappas, Thomas Meyer

A Language Model (LM) is a helpful component of a variety of Natural Language Processing (NLP) systems today. For speech recognition, machine translation, information retrieval, word sense disambiguation etc., the contribution of an LM is to provide featur ...

Idiap2012

Pseudo-Syntactic Language Modeling for Disfluent Speech Recognition

Language models for speech recognition are generally trained on text corpora. Since these corpora do not contain the disfluencies found in natural speech, there is a train/test mismatch when these models are applied to conversational speech. In this work w ...

IDIAP2004

Pseudo-Syntactic Language Modeling for Disfluent Speech Recognition

2004

A recurrent model for perceptual learning

Michael Herzog

Perceptual learning has received enhanced interest during the last years both from theoreticians and experimentalists. Recent experimental results reveal that mechanisms underlying perceptual learning are more complex than previously expected, thereby ruli ...

Optical Society of America1999