Publications related to Adaptive sentiment-aware one-class collaborative filtering

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

EPFL2022

Positive and Negative Critiquing for VAE-based Recommenders

Boi Faltings, Diego Matteo Antognini

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

2022

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

Pascal Fua, Mathieu Salzmann, Victor Constantin, Helge Jochen Rhodin, Sina Honari

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

2022

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

Mary-Anne Hartley, Sai Praneeth Reddy Karimireddy, Martin Jaggi, Felix Hans Michel Grimberg

Background. In medicine and other applications, the copying and sharing of data is impractical for a range of well-considered reasons. With federated learning (FL) techniques, machine learning models can be trained on data spread across several locations w ...

Springer, Cham2020

Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...

EPFL2016

Acquiring Broad Commonsense Knowledge for Sentiment Analysis Using Human Computation

Marina Boia

While artificial intelligence is successful in many applications that cover specific domains, for many commonsense problems there is still a large gap with human performance. Automated sentiment analysis is a typical example: while there are techniques tha ...

EPFL2016

Scalable Unsupervised Domain Adaptation for Electron Microscopy

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón

While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always repres ...

2016

Sentiment Classification of Tweets using Hierarchical Classification

Afroze Ibrahim Baqapuri

This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them in ...

Ieee2016

Scalable Collaborative Bayesian Preference Learning

Young Jun Ko, Matthias Seeger, Mohammad Emtiyaz Khan

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Barbara Caputo

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

Sentiment analysis of informal textual communication in cyberspace

Daniel Thalmann, Stéphane Gobron

The ability to correctly identify the existence and polarity of emotion in informal, textual communication is a very important part of a realistic and immersive 3D environment where people communicate with one another through avatars or with an automated s ...

2010

Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

Boi Faltings, Vincent Jean Fabrice Schickel

To make accurate recommendations, recommendation systems currently require more data about a customer than is usually available. We conjecture that the weaknesses are due to a lack of inductive bias in the learning methods used to build the prediction mode ...

Springer2006