217 citations · 651 across the 24 of their papers we have counts for
25 papers
End-to-end Differentiable Clustering with Associative Memories
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki +1
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural network…
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible…
GVdoc: Graph-based Visual Document Classification
Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak +1
The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document…
Energy Transformer
Benjamin Hoover, Yuchen Liang, Bao Pham +5
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory. Attention is the power-house d…
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki +1
Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial da…
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Yuchen Liang, Mohammed J. Zaki
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. Most existing graph-based m…
Global Self-Attention as a Replacement for Graph Convolution
Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
We propose an extension to the transformer neural network architecture for general-purpose graph learning by adding a dedicated pathway for pairwise structural information, called…
TINKER: A framework for Open source Cyberthreat Intelligence
Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki +2
Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence…
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Yu Chen, Ananya Subburathinam, Ching-Hua Chen +1
Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' expli…
Can a Fruit Fly Learn Word Embeddings?
Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover +4
The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from mult…
MALOnt: An Ontology for Malware Threat Intelligence
Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki +2
Malware threat intelligence uncovers deep information about malware, threat actors, and their tactics, Indicators of Compromise(IoC), and vulnerabilities in different platforms fro…
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, Mohammed J. Zaki
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 embedd…
Personal Health Knowledge Graphs for Patients
Nidhi Rastogi, Mohammed J. Zaki
Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients. For a recommendation system to give a suitable res…
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to g…
A Framework for Generating Explanations from Temporal Personal Health Data
Jonathan J. Harris, Ching-Hua Chen, Mohammed J. Zaki
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 dat…
Deep Iterative and Adaptive Learning for Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
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 s…
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. In this paper, we propose a novel reinforcement learning (RL) based graph-to-sequence (Gra…
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii)…
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However…
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Yu Chen, Lingfei Wu, Mohammed J. Zaki
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based meth…
KATE: K-Competitive Autoencoder for Text
Yu Chen, Mohammed J. Zaki
Autoencoders have been successful in learning meaningful representations from image datasets. However, their performance on text datasets has not been widely studied. Traditional a…
Arabesque: A System for Distributed Graph Mining - Extended version
Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini +3
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorit…
DAGGER: A Scalable Index for Reachability Queries in Large Dynamic Graphs
Hilmi Yildirim, Vineet Chaoji, Mohammed J. Zaki
With the ubiquity of large-scale graph data in a variety of application domains, querying them effectively is a challenge. In particular, reachability queries are becoming increasi…
BitPath -- Label Order Constrained Reachability Queries over Large Graphs
Medha Atre, Vineet Chaoji, Mohammed J. Zaki
In this paper we focus on the following constrained reachability problem over edge-labeled graphs like RDF -- "given source node x, destination node y, and a sequence of edge label…