no code implementations • EMNLP (sustainlp) 2020 • Parul Awasthy, Bishwaranjan Bhattacharjee, John Kender, Radu Florian
Transfer learning is a popular technique to learn a task using less training data and fewer compute resources.
no code implementations • 9 Mar 2024 • Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspooon, Marcel Zalmanovici
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
no code implementations • 8 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.
no code implementations • 18 Dec 2023 • Christoph Tillmann, Aashka Trivedi, Sara Rosenthal, Santosh Borse, Rong Zhang, Avirup Sil, Bishwaranjan Bhattacharjee
Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web.
no code implementations • 13 Nov 2023 • Fatema Hasan, Yulong Li, James Foulds, SHimei Pan, Bishwaranjan Bhattacharjee
This leads to an improved language model for analyzing spoken transcripts while avoiding an audio processing overhead at test time.
no code implementations • 13 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.
no code implementations • 16 Mar 2023 • Aashka Trivedi, Takuma Udagawa, Michele Merler, Rameswar Panda, Yousef El-Kurdi, Bishwaranjan Bhattacharjee
In each episode of the search process, a NAS controller predicts a reward based on the distillation loss and latency of inference.
no code implementations • 7 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.
no code implementations • 20 Nov 2020 • Ulrich Finkler, Michele Merler, Rameswar Panda, Mayoore S. Jaiswal, Hui Wu, Kandan Ramakrishnan, Chun-Fu Chen, Minsik Cho, David Kung, Rogerio Feris, Bishwaranjan Bhattacharjee
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Masayasu Muraoka, Tetsuya Nasukawa, Bishwaranjan Bhattacharjee
We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task.
no code implementations • 23 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.
no code implementations • 20 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.
no code implementations • 24 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.
no code implementations • 30 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.
2 code implementations • 18 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