no code implementations • 30 Apr 2021 • Siddhant Arora, Vinayak Gupta, Garima Gaur, Srikanta Bedathur
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables.
1 code implementation • 13 Sep 2021 • Vinayak Gupta, Srikanta Bedathur
Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data.
no code implementations • 13 Nov 2021 • Vinayak Gupta
The large volumes of data generated by human activities such as online purchases, health records, spatial mobility etc.
no code implementations • 16 Jan 2022 • Vinayak Gupta, Srikanta Bedathur
Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems.
1 code implementation • 17 Feb 2022 • Vinayak Gupta, Srikanta Bedathur, Abir De
To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences.
1 code implementation • 10 Jun 2022 • Vinayak Gupta, Srikanta Bedathur
In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -- next action prediction, sequence-goal prediction, and end-to-end sequence generation.
1 code implementation • 23 Jun 2022 • Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya, Abir De
In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
no code implementations • 29 Aug 2022 • Vinayak Gupta, Srikanta Bedathur
In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences.
no code implementations • 25 Dec 2022 • Vinayak Gupta
Moreover, to provide accurate sequence modeling frameworks, we design solutions for points-of-interest recommendation, i. e., models that can handle spatial mobility data of users to various POI check-ins and recommend candidate locations for the next check-in.
no code implementations • 13 Jul 2023 • Vinayak Gupta, Srikanta Bedathur, Abir De
In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus.
no code implementations • 13 Jul 2023 • Vinayak Gupta, Srikanta Bedathur
We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions.
no code implementations • 7 Feb 2024 • Vinayak Gupta, Rahul Goel, Sirikonda Dhawal, P. J. Narayanan
Our GSN representation generates new views of unseen scenes on the fly along with consistent, per-pixel semantic features.
no code implementations • 19 Feb 2024 • Reshabh K Sharma, Vinayak Gupta, Dan Grossman
However, post-deployment the chatbot definitions are fixed and are vulnerable to attacks by malicious users, emphasizing the need to prevent unethical applications and financial losses.