Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

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Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

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, ...

Smartphones present many interesting opportunities for survey research, particularly through the use of mobile data collection applications (apps). There is still much to learn, however, about how to integrate apps in general population surveys. Recent stu ...

Learning to predict accurately from a few data samples is a central challenge in modern data-hungry machine learning. On natural images, human vision typically outperforms deep learning approaches on few-shot learning. However, we hypothesize that aerial a ...

Thanks to Deep Learning Text-To-Speech (TTS) has achieved high audio quality with large databases. But at the same time the complex models lost any ability to control or interpret the generation process. For the big challenge of affective TTS it is infeasi ...

Remote sensing visual question answering (RSVQA) opens new avenues to promote the use of satellites data, by interfacing satellite image analysis with natural language processing. Capitalizing on the remarkable advances in natural language processing and c ...

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture t ...

Countless signal processing applications include the reconstruction of signals from few indirect linear measurements. The design of effective measurement operators is typically constrained by the underlying hardware and physics, posing a challenging and of ...

To operate the railway system safely and efficiently, a multitude of assets need to me monitored. Railway sleepers are one of these infrastructure assets, that are safety critical. To automate the monitoring process, data-driven fault diagnostics models ha ...

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

Neural methods have become the de-facto choice for the vast majority of data analysis tasks, and entity alignment (EA) is no exception. Not surprisingly, more than 50 different neural EA methods have been published since 2017. However, surprisingly, an ana ...

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

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 ...

In this dissertation, we propose gradient-based methods for characterizing model behaviour for the purposes of knowledge transfer and post-hoc model interpretation. Broadly, gradients capture the variation of some output feature of the model upon unit vari ...

This work studies the class of algorithms for learning with side-information that emerges by extending generative models with embedded context-related variables. Using finite mixture models (FMMs) as the prototypical Bayesian network, we show that maximum- ...

Many ecological studies rely on count data and involve manual counting of objects of interest, which is time-consuming and especially disadvantageous when time in the field or lab is limited. However, an increasing number of works uses digital imagery, whi ...

We address the task of automatically detecting and counting seabirds in unmanned aerial vehicle (UAV) imagery using deep convolutional neural networks (CNNs). Our study area, the coast of West Africa, harbours significant breeding colonies of terns and gul ...

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Chattez avec Graph Search

Machine Learning for Modeling Stock Returns

Why is the winner the best?

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Scalable Collaborative Bayesian Preference Learning

Data Privacy Concerns as a Source of Resistance to Complete Mobile Data Collection Tasks Via a Smartphone App

Humans are Poor Few-Shot Classifiers for Sentinel-2 Land Cover

Controllability and Interpretability in Affective Speech Synthesis

Language Transformers for Remote Sensing Visual Question Answering

Enforcing Connectivity of 3D Linear Structures Using Their 2D Projections

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Contrastive Feature Learning for Railway Infrastructure Fault Diagnostic

TEE-based decentralized recommender systems: The raw data sharing redemption

A Critical Re-evaluation of Neural Methods for Entity Alignment

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Gradient-based Methods for Deep Model Interpretability

Context-Aware Learning for Generative Models

Counting using deep learning regression gives value to ecological surveys

21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning