High-Order Recommendation with Heterophilic Hypergraph Diffusion

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Satellite remote sensing has become a key technology for monitoring Earth and the processes occurring at its surface. It relies on state-of-the-art machine learning models that require large annotated datasets to capture the extreme diversity of the proble ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

The field of computational topology has developed many powerful tools to describe the shape of data, offering an alternative point of view from classical statistics. This results in a variety of complex structures that are not always directly amenable for ...

Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel r ...

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 ...

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

In diffusion social learning over weakly-connected graphs, it has been shown recently that influential agents shape the beliefs of non-influential agents. This paper analyzes this mechanism more closely and addresses two main questions. First, the article ...

High-Order Recommendation with Heterophilic Hypergraph Diffusion

Chat with Graph Search

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Robust Collaborative Learning with Linear Gradient Overhead

Meteor: Meta-learning connecting earth problems observed from space

Democratizing Machine Learning

Reduced representations of complexes, signals, and multifiltrations

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios*

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

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

Automatic Content Curation of Visual Heritage

Belief Control Strategies for Interactions Over Weakly-Connected Graphs