Computing graph neural networks: A survey from algorithms to accelerators
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …
years owing to their capability to model and learn from graph-structured data. Such an ability …
Graph neural network: A comprehensive review on non-euclidean space
This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …
based networks from a deep learning perspective. Graph networks provide a generalized …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
V2vnet: Vehicle-to-vehicle communication for joint perception and prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the
perception and motion forecasting performance of self-driving vehicles. By intelligently …
perception and motion forecasting performance of self-driving vehicles. By intelligently …
An attention enhanced graph convolutional lstm network for skeleton-based action recognition
Skeleton-based action recognition is an important task that requires the adequate
understanding of movement characteristics of a human action from the given skeleton …
understanding of movement characteristics of a human action from the given skeleton …
Session-based recommendation with graph neural networks
The problem of session-based recommendation aims to predict user actions based on
anonymous sessions. Previous methods model a session as a sequence and estimate user …
anonymous sessions. Previous methods model a session as a sequence and estimate user …
Learning human-object interactions by graph parsing neural networks
This paper addresses the task of detecting and recognizing human-object interactions (HOI)
in images and videos. We introduce the Graph Parsing Neural Network (GPNN), a …
in images and videos. We introduce the Graph Parsing Neural Network (GPNN), a …
Lanercnn: Distributed representations for graph-centric motion forecasting
Forecasting the future behaviors of dynamic actors is an important task in many robotics
applications such as self-driving. It is extremely challenging as actors have latent intentions …
applications such as self-driving. It is extremely challenging as actors have latent intentions …
Skeleton-based action recognition with spatial reasoning and temporal stack learning
Skeleton-based action recognition has made great progress recently, but many problems
still remain unsolved. For example, the representations of skeleton sequences captured by …
still remain unsolved. For example, the representations of skeleton sequences captured by …
Visual semantic navigation using scene priors
How do humans navigate to target objects in novel scenes? Do we use the
semantic/functional priors we have built over years to efficiently search and navigate? For …
semantic/functional priors we have built over years to efficiently search and navigate? For …