241 citations · 1.2k across the 14 of their papers we have counts for
17 papers
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Dobrik Georgiev, Kheeran Naidu, Kheeran K. Naidu +4
Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval…
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
Vanessa Cai, Pradeep Prabakar, Manuel Serrano Rebuelta +8
Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are spl…
Learning to Infer Structures of Network Games
Emanuele Rossi, Federico Monti, Yan Leng +2
Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but al…
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of pro…
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard +3
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The po…
ncRNA Classification with Graph Convolutional Networks
Emanuele Rossi, Federico Monti, Michael Bronstein +1
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions. The task of ncRNA classification consists in classifying a gi…
Fake News Detection on Social Media using Geometric Deep Learning
Federico Monti, Fabrizio Frasca, Davide Eynard +2
Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the…
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Kevin McCloskey, Ankur Taly, Federico Monti +2
Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur…
Graph Neural Networks for IceCube Signal Classification
Nicholas Choma, Federico Monti, Lisa Gerhardt +7
Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly develop…
Dual-Primal Graph Convolutional Networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski +3
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Grap…
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Jan Svoboda, Jonathan Masci, Federico Monti +2
Deep learning systems have become ubiquitous in many aspects of our lives. Unfortunately, it has been shown that such systems are vulnerable to adversarial attacks, making them pro…
MotifNet: a motif-based Graph Convolutional Network for directed graphs
Federico Monti, Karl Otness, Michael M. Bronstein
Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techni…
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Jan Svoboda, Federico Monti, Michael M. Bronstein
Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step t…
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
Ron Levie, Federico Monti, Xavier Bresson +1
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learn…
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti, Michael M. Bronstein, Xavier Bresson
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pai…
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci +3
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particu…
Deep convolutional neural networks for pedestrian detection
Denis Tomè, Federico Monti, Luca Baroffio +3
Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. De…