Search Results for author: Bishwaranjan Bhattacharjee

Found 16 papers, 1 papers with code

Efficient Models for the Detection of Hate, Abuse and Profanity

no code implementations8 Feb 2024 Christoph Tillmann, Aashka Trivedi, Bishwaranjan Bhattacharjee

This is unacceptable in civil discourse. The detection of Hate, Abuse and Profanity in text is a vital component of creating civil and unbiased LLMs, which is needed not only for English, but for all languages.

Document Classification named-entity-recognition +3

A Comparative Analysis of Task-Agnostic Distillation Methods for Compressing Transformer Language Models

no code implementations13 Oct 2023 Takuma Udagawa, Aashka Trivedi, Michele Merler, Bishwaranjan Bhattacharjee

Our target of study includes Output Distribution (OD) transfer, Hidden State (HS) transfer with various layer mapping strategies, and Multi-Head Attention (MHA) transfer based on MiniLMv2.

Knowledge Distillation

G2L: A Geometric Approach for Generating Pseudo-labels that Improve Transfer Learning

no code implementations7 Jul 2022 John R. Kender, Bishwaranjan Bhattacharjee, Parijat Dube, Brian Belgodere

Transfer learning is a deep-learning technique that ameliorates the problem of learning when human-annotated labels are expensive and limited.

Transfer Learning

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

no code implementations23 Jun 2020 Rameswar Panda, Michele Merler, Mayoore Jaiswal, Hui Wu, Kandan Ramakrishnan, Ulrich Finkler, Chun-Fu Chen, Minsik Cho, David Kung, Rogerio Feris, Bishwaranjan Bhattacharjee

The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.

Neural Architecture Search

P2L: Predicting Transfer Learning for Images and Semantic Relations

no code implementations20 Aug 2019 Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill, Parijat Dube, Siyu Huo, Michael R. Glass, Brian Belgodere, Sharath Pankanti, Noel Codella, Patrick Watson

We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.

Transfer Learning

Automatic Labeling of Data for Transfer Learning

no code implementations24 Mar 2019 Parijat Dube, Bishwaranjan Bhattacharjee, Siyu Huo, Patrick Watson, John Kender, Brian Belgodere

Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset.

Transfer Learning

Improving Transferability of Deep Neural Networks

no code implementations30 Jul 2018 Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, Matthew Hill

This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source dataset.

Small Data Image Classification Transfer Learning

IBM Deep Learning Service

2 code implementations18 Sep 2017 Bishwaranjan Bhattacharjee, Scott Boag, Chandani Doshi, Parijat Dube, Ben Herta, Vatche Ishakian, K. R. Jayaram, Rania Khalaf, Avesh Krishna, Yu Bo Li, Vinod Muthusamy, Ruchir Puri, Yufei Ren, Florian Rosenberg, Seetharami R. Seelam, Yandong Wang, Jian Ming Zhang, Li Zhang

Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.

Distributed, Parallel, and Cluster Computing

Cannot find the paper you are looking for? You can Submit a new open access paper.