1 code implementation • 5 Jun 2023 • Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki, Parikshit Ram
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem.
1 code implementation • 2 Jun 2023 • Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
However, the dynamic (i. e., input-dependent) nature of these pathways makes it difficult to prune dense self-attention during training.
Ranked #10 on
Graph Regression
on PCQM4Mv2-LSC
1 code implementation • 26 May 2023 • Fnu Mohbat, Mohammed J. Zaki, Catherine Finegan-Dollak, Ashish Verma
Visual document classifiers have shown impressive performance on in-distribution test sets.
3 code implementations • NeurIPS 2023 • Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, Dmitry Krotov
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
no code implementations • 30 Aug 2022 • Yuchen Liang, Dmitry Krotov, Mohammed J. Zaki
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information.
1 code implementation • 11 Jul 2022 • Jonathan Harris, Mohammed J. Zaki
We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data.
1 code implementation • 14 Jun 2022 • Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki, Aparna Gupta
It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators (KPIs).
no code implementations • 13 Nov 2021 • 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.
3 code implementations • 7 Aug 2021 • Md Shamim Hussain, Mohammed J. Zaki, Dharmashankar Subramanian
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
no code implementations • 10 Feb 2021 • Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
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.
2 code implementations • ICLR 2021 • Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, Dmitry Krotov
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.
1 code implementation • 5 Jan 2021 • Yu Chen, Ananya Subburathinam, Ching-Hua Chen, Mohammed J. Zaki
Food recommendation has become an important means to help guide users to adopt healthy dietary habits.
2 code implementations • NeurIPS 2020 • 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 embedding.
1 code implementation • 20 Jun 2020 • Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal
The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports.
1 code implementation • 13 Apr 2020 • Yu Chen, Lingfei Wu, Mohammed J. Zaki
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
no code implementations • 31 Mar 2020 • Nidhi Rastogi, Mohammed J. Zaki
Existing patient data analytics platforms fail to incorporate information that has context, is personal, and topical to patients.
no code implementations • 20 Mar 2020 • 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 data and the generation of meaningful explanations to help users better understand what their data means to them.
1 code implementation • 17 Dec 2019 • 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 structure and graph embeddings simultaneously.
1 code implementation • 19 Oct 2019 • Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer.
no code implementations • 25 Sep 2019 • 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 learning graph structure and graph embedding simultaneously.
1 code implementation • ICLR 2020 • Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer.
1 code implementation • 31 Jul 2019 • Yu Chen, Lingfei Wu, Mohammed J. Zaki
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.
2 code implementations • NAACL 2019 • 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.
1 code implementation • 4 May 2017 • Yu Chen, Mohammed J. Zaki
Autoencoders have been successful in learning meaningful representations from image datasets.
no code implementations • 14 Oct 2015 • Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgos Siganos, Mohammed J. Zaki, Ashraf Aboulnaga
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