Search Results for author:
Found 26 papers, 6 papers with code
Demo of the Linguistic Field Data Management and Analysis System - LiFE
Since its a web-based application, it also allows for seamless collaboration among multiple persons and sharing the data, models, etc with each other.
ComMA@ICON: Multilingual Gender Biased and Communal Language Identification Task at ICON-2021
If approached as three separate classification tasks, the task includes three sub-tasks: aggression identification (sub-task A), gender bias identification (sub-task B), and communal bias identification (sub-task C).
Towards a Unified Tool for the Management of Data and Technologies in Field Linguistics and Computational Linguistics - LiFE
The tool provides a one-click interface to train NLP models for various tasks using the data stored in the system and then use it for assistance in further storage of the data (especially for the field linguists).
Anlirika: An LSTM–CNN Flow Twister for Spoken Language Identification
The paper presents Anlirika’s submission to SIGTYP 2021 Shared Task on Robust Spoken Language Identification.
Semantic Search Evaluation
We propose a novel method for evaluating the performance of a content search system that measures the semantic match between a query and the results returned by the search system.
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
Large language models can memorize and repeat their training data, causing privacy and copyright risks.
Loki: Low-rank Keys for Efficient Sparse Attention
Inference on large language models (LLMs) can be expensive in terms of the compute and memory costs involved, especially when long sequence lengths are used.
HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text
In this paper, we discuss the development of an annotation schema to build datasets for evaluating the offline harm potential of social media texts.
A Framework for Realistic Simulation of Daily Human Activity
For social robots like Astro which interact with and adapt to the daily movements of users within the home, realistic simulation of human activity is needed for feature development and testing.
Jorge: Approximate Preconditioning for GPU-efficient Second-order Optimization
The primary efficiency bottleneck in such optimizers is matrix inverse calculations in the preconditioning step, which are expensive to compute on GPUs.
A 4D Hybrid Algorithm to Scale Parallel Training to Thousands of GPUs
This 4D approach is a hybrid of 3D tensor and data parallelism, and is implemented in the AxoNN framework.
A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training
Mixture-of-Experts (MoE) is a neural network architecture that adds sparsely activated expert blocks to a base model, increasing the number of parameters without impacting computational costs.
Exploiting Sparsity in Pruned Neural Networks to Optimize Large Model Training
Parallel training of neural networks at scale is challenging due to significant overheads arising from communication.
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi
In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Demo of the Linguistic Field Data Management and Analysis System -- LiFE
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
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.
A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks
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
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
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.
Learning to Map for Active Semantic Goal Navigation
We consider the problem of object goal navigation in unseen environments.
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances.
Developing a Multilingual Annotated Corpus of Misogyny and Aggression
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).
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss
We show how agents trained with SQLoss evolve cooperative behavior in several social dilemma matrix games.
RoboNet: Large-Scale Multi-Robot Learning
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?
The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool
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
We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music.
