Search Results for author: Shubham Gupta

Found 33 papers, 8 papers with code

Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives

no code implementations14 Feb 2024 Shubham Gupta, Srikanta Bedathur

Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss.

Link Prediction

CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification

1 code implementation11 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.

SO-NeRF: Active View Planning for NeRF using Surrogate Objectives

no code implementations6 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.

GSHOT: Few-shot Generative Modeling of Labeled Graphs

1 code implementation6 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.

Drug Discovery Few-Shot Learning

IR Models and the COVID-19 Pandemic: A Comparative Study of Performance and Challenges

no code implementations21 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.

Information Retrieval Retrieval

Analysis and application of multispectral data for water segmentation using machine learning

1 code implementation16 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.

Segmentation

Far3Det: Towards Far-Field 3D Detection

no code implementations25 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.

Autonomous Vehicles Philosophy

WSSL: Weighted Self-supervised Learning Framework For Image-inpainting

1 code implementation25 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.

Image Inpainting Self-Supervised Learning

A Survey on Temporal Graph Representation Learning and Generative Modeling

no code implementations25 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.

Graph Representation Learning

On consistency of constrained spectral clustering under representation-aware stochastic block model

no code implementations3 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 $\mathcal{R}$.

Clustering Computational Efficiency +1

Differentiable Rule Induction with Learned Relational Features

no code implementations17 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.

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

no code implementations6 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.

One to rule them all: Towards Joint Indic Language Hate Speech Detection

no code implementations28 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.

Hate Speech Detection

Dependency Structure for News Document Summarization

no code implementations23 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.

Dependency Parsing Document Summarization +2

Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions

no code implementations8 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}.

Attribute Clustering +3

End-to-End Attention-based Image Captioning

2 code implementations30 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.

Image Captioning Translation

Pure Exploration with Structured Preference Feedback

no code implementations12 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.

Decision Making

Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

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.

Active Learning Machine Translation +1

A Real-Time Whole Page Personalization Framework for E-Commerce

no code implementations8 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.

Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

no code implementations26 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.

Knowledge Graphs Representation Learning +1

Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates

no code implementations11 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.

Community Detection Link Prediction

Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation

1 code implementation1 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.

Active Learning Machine Translation +1

A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations

no code implementations24 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.

Probabilistic View of Multi-agent Reinforcement Learning: A Unified Approach

no code implementations25 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

Winning an Election: On Emergent Strategic Communication in Multi-Agent Networks

no code implementations19 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.

A Generative Model for Dynamic Networks with Applications

no code implementations11 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.

Community Detection Link Prediction

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