Search Results for author: Siddharth Singh

Found 13 papers, 3 papers with code

Demo of the Linguistic Field Data Management and Analysis System -- LiFE

1 code implementation22 Mar 2022 Siddharth Singh, Ritesh Kumar, Shyam Ratan, Sonal Sinha

The interface allows creation of multiple projects that could be shared with the other users.

The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse

no code implementations19 Nov 2021 Ritesh Kumar, Enakshi Nandi, Laishram Niranjana Devi, Shyam Ratan, Siddharth Singh, Akash Bhagat, Yogesh Dawer

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the "context" in which they occur.

Aggression Identification

How to Train Your Neural Network: A Comparative Evaluation

no code implementations9 Nov 2021 Shu-Huai Lin, Daniel Nichols, Siddharth Singh, Abhinav Bhatele

This phenomenon has spurred the development of algorithms for distributed training of neural networks over a larger number of hardware accelerators.

AxoNN: An asynchronous, message-driven parallel framework for extreme-scale deep learning

no code implementations25 Oct 2021 Siddharth Singh, Abhinav Bhatele

This has necessitated the development of efficient algorithms to train these neural networks in parallel on large-scale GPU-based clusters.

Domain Adaptation for Real-World Single View 3D Reconstruction

no code implementations24 Aug 2021 Brandon Leung, Siddharth Singh, Arik Horodniceanu

Results are performed with ShapeNet as the source domain and domains within the Object Dataset Domain Suite (ODDS) dataset as the target, which is a real world multiview, multidomain image dataset.

3D Reconstruction Object Reconstruction +2

Stance Detection in Web and Social Media: A Comparative Study

1 code implementation12 Jul 2020 Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, Saptarshi Ghosh

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances.

Stance Detection

Developing a Multilingual Annotated Corpus of Misogyny and Aggression

no code implementations LREC 2020 Shiladitya Bhattacharya, Siddharth Singh, Ritesh Kumar, Akanksha Bansal, Akash Bhagat, Yogesh Dawer, Bornini Lahiri, Atul Kr. Ojha

In this paper, we discuss the development of a multilingual annotated corpus of misogyny and aggression in Indian English, Hindi, and Indian Bangla as part of a project on studying and automatically identifying misogyny and communalism on social media (the ComMA Project).

RoboNet: Large-Scale Multi-Robot Learning

no code implementations24 Oct 2019 Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn

This leads to a frequent tension in robotic learning: how can we learn generalizable robotic controllers without having to collect impractically large amounts of data for each separate experiment?

Video Prediction

The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool

no code implementations11 Aug 2018 Tsung-Ting Kuo, Jina Huh, Ji-Hoon Kim, Robert El-Kareh, Siddharth Singh, Stephanie Feudjio Feupe, Vincent Kuri, Gordon Lin, Michele E. Day, Lucila Ohno-Machado, Chun-Nan Hsu

Our study introduces CLEAN (CLinical note rEview and ANnotation), a pre-annotation-based cNLP annotation system to improve clinical note annotation of data elements, and comprehensively compares CLEAN with the widely-used annotation system Brat Rapid Annotation Tool (BRAT).

THE EFFECTIVENESS OF A TWO-LAYER NEURAL NETWORK FOR RECOMMENDATIONS

no code implementations ICLR 2018 Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric Nalisnick, Leo Dirac, Runfei Luo

We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music.

Recommendation Systems

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