no code implementations • 14 Feb 2024 • Shubham Gupta, Srikanta Bedathur
Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss.
1 code implementation • 11 Jan 2024 • Shubham Gupta, Nandini Saini, Suman Kundu, Debasis Das
To address these issues, we proposed CrisisKAN, a novel Knowledge-infused and Explainable Multimodal Attention Network that entails images and texts in conjunction with external knowledge from Wikipedia to classify crisis events.
no code implementations • 6 Dec 2023 • Keifer Lee, Shubham Gupta, Sunglyoung Kim, Bhargav Makwana, Chao Chen, Chen Feng
Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process remains vague with only a general rule of thumb of sampling as densely as possible.
no code implementations • 6 Jun 2023 • Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta Bedathur
In this work, we address these limitations through a novel GNN framework called GRAFENNE.
1 code implementation • 6 Jun 2023 • Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta Bedathur
Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn a good model.
no code implementations • 21 May 2023 • Moksh Shukla, Nitik Jain, Shubham Gupta
The results indicate that advanced IR models like BERT and Contriever better retrieve relevant information during a pandemic.
1 code implementation • 16 Dec 2022 • Shubham Gupta, Uma D., Ramachandra Hebbar
We also find that the Support Vector Machine (SVM) algorithm is the most favourable for single-band water segmentation.
no code implementations • 25 Nov 2022 • Shubham Gupta, Jeet Kanjani, Mengtian Li, Francesco Ferroni, James Hays, Deva Ramanan, Shu Kong
We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e. g., $>$50m.
1 code implementation • 25 Nov 2022 • Shubham Gupta, Rahul Kunigal Ravishankar, Madhoolika Gangaraju, Poojasree Dwarkanath, Natarajan Subramanyam
To improve the performance of our framework and produce more visually appealing images, we also present a novel loss function for image inpainting.
no code implementations • 15 Oct 2022 • Sheel Shah, Shubham Gupta
ConnectX is a two-player game that generalizes the popular game Connect 4.
no code implementations • 25 Aug 2022 • Shubham Gupta, Srikanta Bedathur
Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biological systems, and many more.
1 code implementation • 7 Mar 2022 • Shubham Gupta, Sahil Manchanda, Srikanta Bedathur, Sayan Ranu
There has been a recent surge in learning generative models for graphs.
no code implementations • 3 Mar 2022 • Shubham Gupta, Ambedkar Dukkipati
Our work leads to an interesting stochastic block model that not only plants the given partitions in $\mathcal{G}$ but also plants the auxiliary information encoded in the representation graph $\mathcal{R}$.
no code implementations • 17 Jan 2022 • Remy Kusters, Yusik Kim, Marine Collery, Christian de Sainte Marie, Shubham Gupta
On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.
no code implementations • 6 Nov 2021 • Aadirupa Saha, Shubham Gupta
We first study the problem of static-regret minimization for adversarial preference sequences and design an efficient algorithm with $O(\sqrt{KT})$ high probability regret.
no code implementations • 28 Sep 2021 • Mehar Bhatia, Tenzin Singhay Bhotia, Akshat Agarwal, Prakash Ramesh, Shubham Gupta, Kumar Shridhar, Felix Laumann, Ayushman Dash
This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task.
no code implementations • 23 Sep 2021 • Congbo Ma, Wei Emma Zhang, Hu Wang, Shubham Gupta, Mingyu Guo
In this work, we develop a neural network based model which leverages dependency parsing to capture cross-positional dependencies and grammatical structures.
no code implementations • 8 Sep 2021 • Ambedkar Dukkipati, Tony Gracious, Shubham Gupta
Lockdowns are one of the most effective measures for containing the spread of a pandemic.
no code implementations • 8 May 2021 • Shubham Gupta, Ambedkar Dukkipati
This graph specifies node pairs that can represent each other with respect to sensitive attributes and is observed in addition to the usual \textit{similarity graph}.
2 code implementations • 30 Apr 2021 • Carola Sundaramoorthy, Lin Ziwen Kelvin, Mahak Sarin, Shubham Gupta
In this paper, we address the problem of image captioning specifically for molecular translation where the result would be a predicted chemical notation in InChI format for a given molecular structure.
no code implementations • 12 Apr 2021 • Shubham Gupta, Aadirupa Saha, Sumeet Katariya
We consider the problem of pure exploration with subset-wise preference feedback, which contains $N$ arms with features.
no code implementations • NAACL 2021 • Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, Ambedkar Dukkipati
We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by an absolute percentage reduction of $\approx\mathbf{3-25\%}$ on multiple NLP tasks while achieving the same performance with no additional computation overhead.
no code implementations • 8 Dec 2020 • Aditya Mantha, Anirudha Sundaresan, Shashank Kedia, Yokila Arora, Shubham Gupta, Gaoyang Wang, Praveenkumar Kanumala, Stephen Guo, Kannan Achan
In production, our system resulted in an improvement in item discovery, an increase in online engagement, and a significant lift on add-to-carts (ATCs) per visitor on the homepage.
no code implementations • 6 Apr 2020 • Shubham Gupta, Rishi Hazra, Ambedkar Dukkipati
One way to coordinate is by learning to communicate with each other.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 26 Nov 2019 • Tony Gracious, Shubham Gupta, Arun Kanthali, Rui M. Castro, Ambedkar Dukkipati
These techniques are different for homogeneous and heterogeneous networks because heterogeneous networks can have multiple types of edges and nodes as opposed to a homogeneous network.
no code implementations • 11 Nov 2019 • Shubham Gupta, Gururaj K., Ambedkar Dukkipati, Rui M. Castro
Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii) explain the discovered communities by identifying the relative importance of different covariates in them.
1 code implementation • 1 Nov 2019 • Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, Ambedkar Dukkipati
We empirically demonstrate that the proposed approach is further able to reduce the data requirements of state-of-the-art AL strategies by $\approx \mathbf{3-25\%}$ on an absolute scale on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead.
no code implementations • 24 Oct 2019 • Aditya Mantha, Yokila Arora, Shubham Gupta, Praveenkumar Kanumala, Zhiwei Liu, Stephen Guo, Kannan Achan
In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user's current grocery basket.
no code implementations • 25 Sep 2019 • Shubham Gupta, Ambedkar Dukkipati
In this paper, we pose the problem of multi-agent reinforcement learning as the problem of performing inference in a particular graphical model.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 19 Feb 2019 • Shubham Gupta, Ambedkar Dukkipati
To the best of our knowledge, we are the first to explore emergence of communication for discovering and implementing strategies in a setting where agents communicate over a network.
no code implementations • 21 Mar 2018 • Jasper Friedrichs, Debanjan Mahata, Shubham Gupta
This paper describes Infosys's participation in the "2nd Social Media Mining for Health Applications Shared Task at AMIA, 2017, Task 2".
no code implementations • 11 Feb 2018 • Shubham Gupta, Gaurav Sharma, Ambedkar Dukkipati
Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i. e. nodes and edges appear and/or disappear over time.
1 code implementation • ICML 2017 • Pranav Shyam, Shubham Gupta, Ambedkar Dukkipati
Rapid learning requires flexible representations to quickly adopt to new evidence.