Search Results for author:
Found 18 papers, 10 papers with code
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC.
Learning to Infer Structures of Network Games
We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function.
Learning to Infer the Structure of Network Games
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 also on those of their neighbors.
Temporal Graph Networks for Deep Learning on Dynamic Graphs
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 problems ranging from biology and particle physics to social networks and recommendation systems.
SIGN: Scalable Inception Graph Neural Networks
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.
Ranked #5 on
Node Classification
on AMZ Comp
ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.
Fake News Detection on Social Media using Geometric Deep Learning
One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.
Graph Neural Networks for IceCube Signal Classification
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 developing field of geometric deep learning.
Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
Deep learning systems have become ubiquitous in many aspects of our lives.
MotifNet: a motif-based Graph Convolutional Network for directed graphs
Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community.
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS
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 learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
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 learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Performance of fingerprint recognition depends heavily on the extraction of minutiae points.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems.
Ranked #5 on
Recommendation Systems
on YahooMusic Monti
(using extra training data)
Geometric deep learning on graphs and manifolds using mixture model CNNs
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Ranked #4 on
Document Classification
on Cora
Deep convolutional neural networks for pedestrian detection
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.
