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Found 26 papers, 19 papers with code
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Ranked #1 on
Drug Discovery
on LIT-PCBA(KAT2A)
End-to-end Differentiable Clustering with Associative Memories
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem.
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
However, the dynamic (i. e., input-dependent) nature of these pathways makes it difficult to prune dense self-attention during training.
Ranked #12 on
Graph Regression
on PCQM4Mv2-LSC
GVdoc: Graph-based Visual Document Classification
Visual document classifiers have shown impressive performance on in-distribution test sets.
Energy Transformer
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
Associative Learning for Network Embedding
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information.
Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data.
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs).
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document.
Global Self-Attention as a Replacement for Graph Convolution
The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data.
Ranked #1 on
Graph Regression
on PCQM4Mv2-LSC
TINKER: A framework for Open source Cyberthreat Intelligence
The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts.
Can a Fruit Fly Learn Word Embeddings?
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Food recommendation has become an important means to help guide users to adopt healthy dietary habits.
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedding.
MALOnt: An Ontology for Malware Threat Intelligence
The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports.
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
In this work, we focus on a more realistic setting where we aim to generate questions from a KG subgraph and target answers.
Ranked #3 on
KG-to-Text Generation
on WebQuestions
Personal Health Knowledge Graphs for Patients
Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients.
A Framework for Generating Explanations from Temporal Personal Health Data
Whereas it has become easier for individuals to track their personal health data (e. g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them.
Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously.
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Natural question generation (QG) aims to generate questions from a passage and an answer.
Iterative Deep Graph Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly learning graph structure and graph embedding simultaneously.
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Natural question generation (QG) aims to generate questions from a passage and an answer.
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks.
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles.
KATE: K-Competitive Autoencoder for Text
Autoencoders have been successful in learning meaningful representations from image datasets.
Arabesque: A System for Distributed Graph Mining - Extended version
However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph.
Distributed, Parallel, and Cluster Computing
