# [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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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### 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

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