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.
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 • 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.
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.
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.
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.
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.
no code implementations • 17 Apr 2024 • Mike A. Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, Tim Althoff
But it remains unknown whether non-trivial forecasting implies that language models can reason about time series.