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06/05/2023
End-to-end Differentiable Clustering with Associative Memories
Clustering is a widely used unsupervised learning technique involving an...
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06/02/2023
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Transformers use the dense self-attention mechanism which gives a lot of...
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05/26/2023
GVdoc: Graph-based Visual Document Classification
The robustness of a model for real-world deployment is decided by how we...
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02/14/2023
Energy Transformer
Transformers have become the de facto models of choice in machine learni...
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08/30/2022
Associative Learning for Network Embedding
The network embedding task is to represent the node in the network as a ...
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07/11/2022
Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
With an increased interest in the production of personal health technolo...
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06/14/2022
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
Unstructured data, especially text, continues to grow rapidly in various...
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11/13/2021
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Keyphrase extraction is the task of finding several interesting phrases ...
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08/07/2021
Edge-augmented Graph Transformers: Global Self-attention is Enough for Graphs
Transformer neural networks have achieved state-of-the-art results for u...
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01/18/2021
Can a Fruit Fly Learn Word Embeddings?
The mushroom body of the fruit fly brain is one of the best studied syst...
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01/05/2021
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...
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06/21/2020
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...
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06/20/2020
MALOnt: An Ontology for Malware Threat Intelligence
Malware threat intelligence uncovers deep information about malware, thr...
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04/13/2020
Toward Subgraph Guided Knowledge Graph Question Generation with Graph Neural Networks
Knowledge graph question generation (QG) aims to generate natural langua...
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03/31/2020
Personal Health Knowledge Graphs for Patients
Existing patient data analytics platforms fail to incorporate informatio...
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03/20/2020
A Framework for Generating Explanations from Temporal Personal Health Data
Whereas it has become easier for individuals to track their personal hea...
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12/17/2019
Deep Iterative and Adaptive Learning for Graph Neural Networks
In this paper, we propose an end-to-end graph learning framework, namely...
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10/19/2019
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Natural question generation (QG) aims to generate questions from a passa...
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08/14/2019
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Natural question generation (QG) is a challenging yet rewarding task, th...
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07/31/2019
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Conversational machine reading comprehension (MRC) has proven significan...
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03/06/2019
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
When answering natural language questions over knowledge bases (KB), dif...
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05/04/2017