Search Results for author: Debarun Bhattacharjya

Found 10 papers, 2 papers with code

Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning

no code implementations2 Feb 2024 Debarun Bhattacharjya, JunKyu Lee, Don Joven Agravante, Balaji Ganesan, Radu Marinescu

Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.

Self-Supervised Contrastive Pre-Training for Multivariate Point Processes

no code implementations1 Feb 2024 Xiao Shou, Dharmashankar Subramanian, Debarun Bhattacharjya, Tian Gao, Kristin P. Bennet

Self-supervision is one of the hallmarks of representation learning in the increasingly popular suite of foundation models including large language models such as BERT and GPT-3, but it has not been pursued in the context of multivariate event streams, to the best of our knowledge.

Point Processes Representation Learning +1

Distilling Event Sequence Knowledge From Large Language Models

no code implementations14 Jan 2024 Somin Wadhwa, Oktie Hassanzadeh, Debarun Bhattacharjya, Ken Barker, Jian Ni

In this work, we explore the use of Large Language Models (LLMs) to generate event sequences that can effectively be used for probabilistic event model construction.

Language Modelling

Event Prediction using Case-Based Reasoning over Knowledge Graphs

1 code implementation21 Sep 2023 Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh

To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict.

Inductive Link Prediction Knowledge Graphs

Summary Markov Models for Event Sequences

no code implementations6 May 2022 Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh, Liza Bialik

Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora.

Time Series Time Series Analysis

Logical Credal Networks

no code implementations25 Sep 2021 Haifeng Qian, Radu Marinescu, Alexander Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel, Pravinda Sahu

This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.

A Multi-Channel Neural Graphical Event Model with Negative Evidence

no code implementations21 Feb 2020 Tian Gao, Dharmashankar Subramanian, Karthikeyan Shanmugam, Debarun Bhattacharjya, Nicholas Mattei

Event datasets are sequences of events of various types occurring irregularly over the time-line, and they are increasingly prevalent in numerous domains.

Proximal Graphical Event Models

no code implementations NeurIPS 2018 Debarun Bhattacharjya, Dharmashankar Subramanian, Tian Gao

Event datasets include events that occur irregularly over the timeline and are prevalent in numerous domains.

A Big Data Approach to Computational Creativity

1 code implementation5 Nov 2013 Lav R. Varshney, Florian Pinel, Kush R. Varshney, Debarun Bhattacharjya, Angela Schoergendorfer, Yi-Min Chee

Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process.


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