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Claudiu-Cristian Musat

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Publications associées (28)

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T-RECS: a Transformer-based Recommender Generating Textual Explanations and Integrating Unsupervised Language-based Critiquing

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Supporting recommendations with personalized and relevant explanations increases trust and perceived quality, and helps users make better decisions. Prior work attempted to generate a synthetic review or review segment as an explanation, but they were not ...

2022

Benefiting From Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution

Jean-Philippe Thiran, Thomas Yu, Seyedbehzad Bozorgtabar, Claudiu-Cristian Musat, Hazim Kemal Ekenel, Mohammad Saeed Rad

Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsampling. However, in real-world SR, there is a large variety of realistic image de ...

2021

Multi-Dimensional Explanation of Target Variables from Documents

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2021

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The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

2021

Interacting with Explanations through Critiquing

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

2021

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In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. T ...

2021

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Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are ...

2020

We experiment with subword segmentation approaches that are widely used to address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR). For morphologically rich languages such as German which has many rare words main ...

2020

COMPARISON OF SUBWORD SEGMENTATION METHODS FOR OPEN-VOCABULARYEND-TO-END SPEECH RECOGNITION

Philip Neil Garner, Claudiu-Cristian Musat

To address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR), we experiment with subword segmentation approaches, specifically byte-pair encoding and unigram language model. Such approaches are attractive in genera ...

Idiap2020

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Neural Architecture Search (NAS) aims to facilitate the design of deep networks fornew tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely b ...

2020