[PDF] Top 20 Meta Learning for Graph Neural Networks
Has 10000 "Meta Learning for Graph Neural Networks" found on our website. Below are the top 20 most common "Meta Learning for Graph Neural Networks".
Meta Learning for Graph Neural Networks
... 2.4 Meta Learning for Architecture search CNNs architectures have been able to achieve state-of-the-art performance on a variety of ...a neural network based prediction model for the user defined ... See full document
66
Distributed Learning with Graph Neural Networks
... This is not an issue except that it increases the cost of your simulation due the the necessity to recompute the communication graph. 3 Learning Distributed Controllers In Section 1.2 we explained the ... See full document
16
Heterogeneous Graph Structure Learning for Graph Neural Networks
... heterogeneous graph struc- ture for HGNNs rather than rely only on the raw graph struc- ...heterogeneous graph structure for HGNNs and propose a novel framework HGSL, which jointly performs ... See full document
9
Graph to Sequence Learning using Gated Graph Neural Networks
... AMR graph shown in Fig- ure 1, the ARG1 predicate between want-01 and believe-01 can be interpreted as the prepo- sition “to” in the surface form, while the ARG1 predicate connecting believe-01 and boy is realised ... See full document
11
Learning Discrete Structures for Graph Neural Networks
... 3. Learning Discrete Graph Structures With this paper we address the challenging scenarios where a graph structure is either completely missing, incomplete, or ...a graph generator so as to ... See full document
11
Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
... lutional neural networks (CNNs) which are widely applied in computer vision, GNNs use multi- layer structures and convolutional operations to aggregate local information of nodes, together with non-linear ... See full document
11
New Deep Neural Networks for Unsupervised Feature Learning on Graph Data
... analyze networks has attracted a surge of attention in data mining and machine learning community ...when learning node ...when learning node ... See full document
120
SLAPS: SELF-SUPERVISION IMPROVES STRUCTURE LEARNING FOR GRAPH NEURAL NETWORKS
... BSTRACT Graph neural networks (GNNs) work well when the graph structure is ...inferred graph. Unfortunately, the space of possible graph structures grows super-exponentially with ... See full document
13
Graph based Dependency Parsing with Graph Neural Networks
... Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vin´ıcius Flores Zam- baldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, C ¸ aglar G¨ulc¸ehre, Francis ... See full document
11
Using Graph Neural Networks to model the performance of Deep Neural Networks
... novel Graph Neural Network-based performance model to estimate the run times of deep learning pipelines implemented using the Halide ...on Graph Convolutions capable of capturing interactions ... See full document
11
Graph Neural Networks in Particle Physics
... deep learning modules do not have appropriate inductive biases to exploit this richer graphical ...structure. Graph-structured data are ubiquitous across science, engineering, and many other problem ...A ... See full document
29
Neural Networks for Power Flow : Graph Neural Solver
... However, there were some limits to this first investigation that were left for future research. First of all, the strong hypoth- esis that all power lines share the same physical characteristics was made, to simplify the ... See full document
10
Generative Causal Explanations for Graph Neural Networks
... a graph generator to generate explanations for any given ...parameterized graph auto-encoder with GCN layers to generate explanations, which, once trained, can be utilized in the inductive setting to ... See full document
14
Structured Dialogue Policy with Graph Neural Networks
... The output of dialogue policy is a summary action. Similarly, the summary actions can be divided into n + 1 sets including n slot-dependent action sets A j (1 ≤ j ≤ n), e.g. request slot j , confirm slot j , select slot ... See full document
12
Session-Based Recommendation with Graph Neural Networks
... recurrent neural network ap- proach, and then extends to an architecture with parallel RNNs (Hidasi et ...convolutional neural networks. Besides, A list-wise deep neural network (Wu and Yan ... See full document
8
Chapter 6 Graph Neural Networks: Scalability
... Graph Neural Networks: Scalability Hehuan Ma, Yu Rong, and Junzhou Huang Abstract Over the past decade, Graph Neural Networks have achieved remarkable success in modeling complex ... See full document
22
Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... social networks to biological networks, graphs are a natural way to represent a diverse set of real-world ...attributed graph convolu- tional neural network with a pooling layer (AGCP for ... See full document
124
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing
... Being the first work of its kind, there is a lot of scope for improvement. A more sophisticated model which is able to retrieve facts more ef- ficiently from millions of entries can be formu- lated. Currently we have ... See full document
10
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
... supervised learning on ...in graph kernels have emphasized scalability, focusing on techniques that bypass expensive Gram matrix computations by using explicit feature ...deep graph kernels (Ya- ... See full document
8
Densely Connected Graph Convolutional Networks for Graph to Sequence Learning
... 6 Conclusion We introduce the novel densely connected graph convolutional networks to learn structural graph representations. Experimental results show that DCGCNs can outperform state-of-the-art ... See full document
16
Related subjects