Publications by Claudiu-Cristian Musat

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

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

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

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

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

Claudiu-Cristian Musat, Diego Matteo Antognini, Martin Milenkoski

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

Momentum-based Gradient Methods in Multi-Objective Recommendation

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-grad ...

2021

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Simulating online recommender system performance is no- toriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evalu- ate model performan ...

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

Addressing fairness in classification with a model-agnostic multi-objective algorithm

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

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

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

Jean-Philippe Thiran, Mohammad Saeed Rad, Claudiu-Cristian Musat, Hazim Kemal Ekenel

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

Evaluating the search phase of neural architecture search

Mathieu Salzmann, Claudiu-Cristian Musat, Kaicheng Yu, Martin Jaggi

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

Overcoming Multi-model Forgetting

Anthony Christopher Davison, Mathieu Salzmann, Yassine Benyahia, Claudiu-Cristian Musat, Kaicheng Yu, Martin Jaggi

We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, ...

JMLR2019

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Kamil Bennani-Smires, Claudiu-Cristian Musat, Martin Jaggi

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

Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles

Boi Faltings, Claudiu-Cristian Musat

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

2015

Recommendation Strategy Portfolios

Boi Faltings, Claudiu-Cristian Musat

A recommender system comprising at least one computing device which comprises one or more processors and one or more computer-readable storage media operatively coupled to at least one of the processors, wherein said computing device performs a method of a ...

2015

User Profiling Using Submitted Review Content

Boi Faltings, Yizhong Liang, Claudiu-Cristian Musat

A computer-implemented method for generating a user profile of a user of an online platform, a computer-implemented method for providing a first user with targeted information and a computer-implemented method for providing a user of an online platform wit ...

2014

Constructing Context-Aware Sentiment Lexicons with an Asynchronous Game with a Purpose

Boi Faltings, Marina Boia, Claudiu-Cristian Musat

One of the reasons sentiment lexicons do not reach human-level performance is that they lack the contexts that define the polarities of words. While obtaining this knowledge through machine learning would require huge amounts of data, context is commonsens ...

Springer-Verlag Berlin2014

Acquiring Commonsense Knowledge for Sentiment Analysis through Human Computation

Boi Faltings, Marina Boia, Claudiu-Cristian Musat

2014

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

Pearl Pu Faltings, Valentina Sintsova, Claudiu-Cristian Musat

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

Recommendation using textual opinions

Boi Faltings, Claudiu-Cristian Musat

Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize t ...

2013

A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

Boi Faltings, Pearl Pu Faltings, Marina Boia, Claudiu-Cristian Musat

Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sen ...

IEEE2013

Direct Negative Opinions in Online Discussions

Boi Faltings, Claudiu-Cristian Musat, Philippe Henri Jean Roussille

In this paper we investigate the impact of antagonism in online discussions. We define antagonism as a new class of textual opinions - direct sentiment towards the authors of previous comments. We detect the negative sentiment using aspect-based opinion mi ...

2013