Search Results for author: Subrata Mitra

Found 8 papers, 1 papers with code

Few-Shot Class-Incremental Learning for Named Entity Recognition

no code implementations ACL 2022 Rui Wang, Tong Yu, Handong Zhao, Sungchul Kim, Subrata Mitra, Ruiyi Zhang, Ricardo Henao

In this work, we study a more challenging but practical problem, i. e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.

Few-Shot Class-Incremental Learning Incremental Learning +3

Approximate Caching for Efficiently Serving Diffusion Models

no code implementations7 Dec 2023 Shubham Agarwal, Subrata Mitra, Sarthak Chakraborty, Srikrishna Karanam, Koyel Mukherjee, Shiv Saini

Text-to-image generation using diffusion models has seen explosive popularity owing to their ability in producing high quality images adhering to text prompts.

Denoising Management +1

Reinforced Approximate Exploratory Data Analysis

no code implementations12 Dec 2022 Shaddy Garg, Subrata Mitra, Tong Yu, Yash Gadhia, Arjun Kashettiwar

Exploratory data analytics (EDA) is a sequential decision making process where analysts choose subsequent queries that might lead to some interesting insights based on the previous queries and corresponding results.

Decision Making

Analysis of Distributed Deep Learning in the Cloud

no code implementations30 Aug 2022 Aakash Sharma, Vivek M. Bhasi, Sonali Singh, Rishabh Jain, Jashwant Raj Gunasekaran, Subrata Mitra, Mahmut Taylan Kandemir, George Kesidis, Chita R. Das

We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud.

Electra: Conditional Generative Model based Predicate-Aware Query Approximation

no code implementations28 Jan 2022 Nikhil Sheoran, Subrata Mitra, Vibhor Porwal, Siddharth Ghetia, Jatin Varshney, Tung Mai, Anup Rao, Vikas Maddukuri

The goal of Approximate Query Processing (AQP) is to provide very fast but "accurate enough" results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets.

ApproxDet: Content and Contention-Aware Approximate Object Detection for Mobiles

1 code implementation21 Oct 2020 ran Xu, Chen-Lin Zhang, Pengcheng Wang, Jayoung Lee, Subrata Mitra, Somali Chaterji, Yin Li, Saurabh Bagchi

In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios.

Object object-detection +3

ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients

no code implementations28 Aug 2019 Ran Xu, Rakesh Kumar, Pengcheng Wang, Peter Bai, Ganga Meghanath, Somali Chaterji, Subrata Mitra, Saurabh Bagchi

None of the current approximation techniques for object classification DNNs can adapt to changing runtime conditions, e. g., changes in resource availability on the device, the content characteristics, or requirements from the user.

Object Detection

DeepPlace: Learning to Place Applications in Multi-Tenant Clusters

no code implementations30 Jul 2019 Subrata Mitra, Shanka Subhra Mondal, Nikhil Sheoran, Neeraj Dhake, Ravinder Nehra, Ramanuja Simha

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics.

reinforcement-learning Reinforcement Learning (RL) +1

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