no code implementations • FNP (LREC) 2022 • Anik Saha, Jian Ni, Oktie Hassanzadeh, Alex Gittens, Kavitha Srinivas, Bulent Yener
Causal information extraction is an important task in natural language processing, particularly in finance domain.
no code implementations • 18 Apr 2024 • Michael Katz, Harsha Kokel, Kavitha Srinivas, Shirin Sohrabi
We analyse the cost of using LLMs for planning and highlight that recent trends are profoundly uneconomical.
1 code implementation • 29 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions.
1 code implementation • 7 Aug 2023 • Anik Saha, Oktie Hassanzadeh, Alex Gittens, Jian Ni, Kavitha Srinivas, Bulent Yener
Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation.
no code implementations • 9 Jul 2023 • Kavitha Srinivas, Julian Dolby, Ibrahim Abdelaziz, Oktie Hassanzadeh, Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Subhajit Chaudhury, Horst Samulowitz
Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery.
1 code implementation • 18 May 2023 • Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B. Tenenbaum, Leslie Pack Kaelbling, Michael Katz
We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain.
no code implementations • 2 Mar 2023 • Udayan Khurana, Kavitha Srinivas, Sainyam Galhotra, Horst Samulowitz
The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection.
no code implementations • 5 Jan 2023 • Wenting Zhao, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas, Mossad Helali, Essam Mansour
We demonstrate the efficiency and usefulness of Serenity's analysis in two applications: code completion and automated machine learning.
no code implementations • SemTab@ISWC 2022 • Nora Abdelmageed, Jiaoyan Chen, Vincenzo Cutrona, Vasilis Efthymiou, Oktie Hassanzadeh, Madelon Hulsebos, Ernesto Jiménez-Ruiz, Juan Sequeda, Kavitha Srinivas
SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Matching (OM) Workshop.
Ranked #2 on Cell Entity Annotation on ToughTables-WD
no code implementations • 13 Sep 2022 • Karl Munson, Anish Savla, Chih-Kai Ting, Serenity Wade, Kiran Kate, Kavitha Srinivas
In addition to defining style, we explore the capability of a pre-trained code language model to capture information about code style.
no code implementations • 16 May 2022 • Udayan Khurana, Kavitha Srinivas, Horst Samulowitz
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
no code implementations • 25 Nov 2021 • Essam Mansour, Kavitha Srinivas, Katja Hose
Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc.
1 code implementation • 29 Oct 2021 • Mossad Helali, Essam Mansour, Ibrahim Abdelaziz, Julian Dolby, Kavitha Srinivas
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.
no code implementations • ISWC 2021 • Vincenzo Cutrona, Jiaoyan Chen, Vasilis Efthymiou, Oktie Hassanzadeh, Ernesto Jimenez-Ruiz, Juan Sequeda, Kavitha Srinivas, Nora Abdelmageed
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
1 code implementation • 15 Sep 2021 • Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas
Code understanding is an increasingly important application of Artificial Intelligence.
no code implementations • 7 Jun 2021 • Ibrahim Abdelaziz, Maxwell Crouse, Bassem Makni, Vernon Austil, Cristina Cornelio, Shajith Ikbal, Pavan Kapanipathi, Ndivhuwo Makondo, Kavitha Srinivas, Michael Witbrock, Achille Fokoue
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
1 code implementation • 21 Feb 2020 • Ibrahim Abdelaziz, Julian Dolby, Jamie McCusker, Kavitha Srinivas
We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
1 code implementation • 5 Nov 2019 • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
1 code implementation • 5 Sep 2018 • Kavitha Srinivas, Abraham Gale, Julian Dolby
Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together.