no code implementations • SemEval (NAACL) 2022 • Abhishek Singh
In this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection.
no code implementations • FNP (COLING) 2020 • Abhishek Singh
Companies provide annual reports to their shareholders at the end of the financial year that de-scribes their operations and financial conditions.
1 code implementation • 19 Mar 2024 • Sai Ashish Somayajula, Youwei Liang, Abhishek Singh, Li Zhang, Pengtao Xie
Pretrained Language Models (PLMs) have advanced Natural Language Processing (NLP) tasks significantly, but finetuning PLMs on low-resource datasets poses significant challenges such as instability and overfitting.
no code implementations • 19 Mar 2024 • Zaid Tasneem, Akshat Dave, Abhishek Singh, Kushagra Tiwary, Praneeth Vepakomma, Ashok Veeraraghavan, Ramesh Raskar
It learns photorealistic scene representations by decomposing users' 3D views into personal and global NeRFs and a novel optimally weighted aggregation of only the latter.
no code implementations • 25 Feb 2024 • Abhishek Singh, Gauri Gupta, Ritvik Kapila, Yichuan Shi, Alex Dang, Sheshank Shankar, Mohammed Ehab, Ramesh Raskar
Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters.
1 code implementation • 23 Nov 2023 • Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan
We empirically show that our EIGEN framework can significantly improve the performance of state-of-the-art deep models with the availability of very few labeled data instances.
no code implementations • 20 Nov 2022 • Frédéric Berdoz, Abhishek Singh, Martin Jaggi, Ramesh Raskar
To do so, each client releases averaged last hidden layer activations of similar labels to a central server that only acts as a relay (i. e., is not involved in the training or aggregation of the models).
no code implementations • 23 Mar 2022 • Ayush Chopra, Abhinav Java, Abhishek Singh, Vivek Sharma, Ramesh Raskar
The goal of this work is to protect sensitive information when learning from point clouds; by censoring the sensitive information before the point cloud is released for downstream tasks.
no code implementations • 17 Mar 2022 • Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
While releasing datasets continues to make a big impact in various applications of computer vision, its impact is mostly realized when data sharing is not inhibited by privacy concerns.
no code implementations • 16 Feb 2022 • Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha, Bhargav Gulavani, Rimma Nehme, Amey Agrawal, Chen Chen, Nipun Kwatra, Ramachandran Ramjee, Pankaj Sharma, Atul Katiyar, Vipul Modi, Vaibhav Sharma, Abhishek Singh, Shreshth Singhal, Kaustubh Welankar, Lu Xun, Ravi Anupindi, Karthik Elangovan, Hasibur Rahman, Zhou Lin, Rahul Seetharaman, Cheng Xu, Eddie Ailijiang, Suresh Krishnappa, Mark Russinovich
At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e. g., GPUs, FPGAs).
no code implementations • 2 Dec 2021 • Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
In this work, we introduce AdaSplit which enables efficiently scaling SL to low resource scenarios by reducing bandwidth consumption and improving performance across heterogeneous clients.
no code implementations • 29 Sep 2021 • Abhishek Singh, Ethan Garza, Ayush Chopra, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar
This is done in a two-step process: first, we develop a method that encodes unstructured image-like modality into a structured representation bifurcated by sensitive and non-sensitive representation.
1 code implementation • 29 Sep 2021 • Alexandros Karargyris, Renato Umeton, Micah J. Sheller, Alejandro Aristizabal, Johnu George, Srini Bala, Daniel J. Beutel, Victor Bittorf, Akshay Chaudhari, Alexander Chowdhury, Cody Coleman, Bala Desinghu, Gregory Diamos, Debo Dutta, Diane Feddema, Grigori Fursin, Junyi Guo, Xinyuan Huang, David Kanter, Satyananda Kashyap, Nicholas Lane, Indranil Mallick, Pietro Mascagni, Virendra Mehta, Vivek Natarajan, Nikola Nikolov, Nicolas Padoy, Gennady Pekhimenko, Vijay Janapa Reddi, G Anthony Reina, Pablo Ribalta, Jacob Rosenthal, Abhishek Singh, Jayaraman J. Thiagarajan, Anna Wuest, Maria Xenochristou, Daguang Xu, Poonam Yadav, Michael Rosenthal, Massimo Loda, Jason M. Johnson, Peter Mattson
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
no code implementations • 21 Jul 2021 • Chirag Samal, Kasia Jakimowicz, Krishnendu Dasgupta, Aniket Vashishtha, Francisco O., Arunakiry Natarajan, Haris Nazir, Alluri Siddhartha Varma, Tejal Dahake, Amitesh Anand Pandey, Ishaan Singh, John Sangyeob Kim, Mehrab Singh Gill, Saurish Srivastava, Orna Mukhopadhyay, Parth Patwa, Qamil Mirza, Sualeha Irshad, Sheshank Shankar, Rohan Iyer, Rohan Sukumaran, Ashley Mehra, Anshuman Sharma, Abhishek Singh, Maurizio Arseni, Sethuraman T V, Saras Agrawal, Vivek Sharma, Ramesh Raskar
It is a bane of the \"uber connected world that we live in that this virus has affected almost all countries and caused mortality and economic upheaval at a scale whose effects are going to be felt for generations to come.
no code implementations • 24 May 2021 • Mohammed Elbtity, Abhishek Singh, Brendan Reidy, Xiaochen Guo, Ramtin Zand
In this paper, we develop an in-memory analog computing (IMAC) architecture realizing both synaptic behavior and activation functions within non-volatile memory arrays.
no code implementations • 21 May 2021 • Subhash Chandra Sadhu, Abhishek Singh, Tomohiro Maeda, Tristan Swedish, Ryan Kim, Lagnojita Sinha, Ramesh Raskar
Time of flight based Non-line-of-sight (NLOS) imaging approaches require precise calibration of illumination and detector positions on the visible scene to produce reasonable results.
1 code implementation • 18 May 2021 • Parth Patwa, Viswanatha Reddy, Rohan Sukumaran, Sethuraman TV, Eptehal Nashnoush, Sheshank Shankar, Rishemjit Kaur, Abhishek Singh, Ramesh Raskar
The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states.
no code implementations • 4 Mar 2021 • Krishnasuri Narayanam, Seep Goel, Abhishek Singh, Yedendra Shrinivasan, Parameswaram Selvam
Goods trade is a supply chain transaction that involves shippers buying goods from suppliers and carriers providing goods transportation.
Distributed, Parallel, and Cluster Computing
no code implementations • 20 Jan 2021 • Joseph Bae, Rohan Sukumaran, Sheshank Shankar, Saurish Srivastava, Rohan Iyer, Aryan Mahindra, Qamil Mirza, Maurizio Arseni, Anshuman Sharma, Saras Agrawal, Orna Mukhopadhyay, Colin Kang, Priyanshi Katiyar, Apurv Shekhar, Sifat Hasan, Krishnendu Dasgupta, Darshan Gandhi, Sethuramen TV, Parth Patwa, Ishaan Singh, Abhishek Singh, Ramesh Raskar
In this early draft, we describe a user-centric, card-based system for vaccine distribution.
Computers and Society Cryptography and Security
no code implementations • 5 Jan 2021 • Manuel Morales, Rachel Barbar, Darshan Gandhi, Sanskruti Landuge, Joseph Bae, Arpita Vats, Jil Kothari, Sheshank Shankar, Rohan Sukumaran, Himi Mathur, Krutika Misra, Aishwarya Saxena, Parth Patwa, Sethuraman T. V., Maurizio Arseni, Shailesh Advani, Kasia Jakimowicz, Sunaina Anand, Priyanshi Katiyar, Ashley Mehra, Rohan Iyer, Srinidhi Murali, Aryan Mahindra, Mikhail Dmitrienko, Saurish Srivastava, Ananya Gangavarapu, Steve Penrod, Vivek Sharma, Abhishek Singh, Ramesh Raskar
In this work, we discuss challenges complicating the existing covid-19 testing ecosystem and highlight the need to improve the testing experience for the user and reduce privacy invasions.
Computers and Society
no code implementations • 21 Dec 2020 • Rohan Sukumaran, Parth Patwa, T V Sethuraman, Sheshank Shankar, Rishank Kanaparti, Joseph Bae, Yash Mathur, Abhishek Singh, Ayush Chopra, Myungsun Kang, Priya Ramaswamy, Ramesh Raskar
In this study, we understand trends in the spread of COVID-19 by utilizing the results of self-reported COVID-19 symptoms surveys as an alternative to COVID-19 testing reports.
no code implementations • CVPR 2021 • Abhishek Singh, Ayush Chopra, Vivek Sharma, Ethan Garza, Emily Zhang, Praneeth Vepakomma, Ramesh Raskar
Recent deep learning models have shown remarkable performance in image classification.
no code implementations • 3 Dec 2020 • Darshan Gandhi, Rohan Sukumaran, Priyanshi Katiyar, Alex Radunsky, Sunaina Anand, Shailesh Advani, Jil Kothari, Kasia Jakimowicz, Sheshank Shankar, Sethuraman T. V., Krutika Misra, Aishwarya Saxena, Sanskruti Landage, Richa Sonker, Parth Patwa, Aryan Mahindra, Mikhail Dmitrienko, Kanishka Vaish, Ashley Mehra, Srinidhi Murali, Rohan Iyer, Joseph Bae, Vivek Sharma, Abhishek Singh, Rachel Barbar, Ramesh Raskar
We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues.
Computers and Society
no code implementations • SEMEVAL 2020 • Abhishek Singh, Surya Pratap Singh Parmar
The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation.
no code implementations • 9 Nov 2020 • Darshan Gandhi, Sanskruti Landage, Joseph Bae, Sheshank Shankar, Rohan Sukumaran, Parth Patwa, Sethuraman T V, Priyanshi Katiyar, Shailesh Advani, Rohan Iyer, Sunaina Anand, Aryan Mahindra, Rachel Barbar, Abhishek Singh, Ramesh Raskar
The coronavirus disease 2019 (COVID-19) pandemic has spread rapidly across the world, leading to enormous amounts of human death and economic loss.
no code implementations • 8 Oct 2020 • Abhishek Singh
In this paper, we propose our methodology PoinT-5 (the combination of Pointer Network and T-5 (Test-to-text transfer Transformer) algorithms) that we used in the Financial Narrative Summarisation (FNS) 2020 task.
no code implementations • 26 Sep 2020 • Mikhail Dmitrienko, Abhishek Singh, Patrick Erichsen, Ramesh Raskar
In this work we propose a WiFi colocation methodology for digital contact tracing.
Computers and Society Signal Processing
no code implementations • 4 Sep 2020 • Sheshank Shankar, Rishank Kanaparti, Ayush Chopra, Rohan Sukumaran, Parth Patwa, Myungsun Kang, Abhishek Singh, Kevin P. McPherson, Ramesh Raskar
As we await a vaccine, social-distancing via efficient contact tracing has emerged as the primary health strategy to dampen the spread of COVID-19.
1 code implementation • 20 Aug 2020 • Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, Ramesh Raskar
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy.
no code implementations • 7 Aug 2020 • Iker Ceballos, Vivek Sharma, Eduardo Mugica, Abhishek Singh, Alberto Roman, Praneeth Vepakomma, Ramesh Raskar
In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distributed features.
5 code implementations • 27 Jul 2020 • Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr
Federated learning (FL) is a rapidly growing research field in machine learning.
2 code implementations • 20 Jul 2020 • Abhishek Singh, Surya Pratap Singh Parmar
The use of pre-trained embeddings usually helps in multiple tasks such as sentence classification, and machine translation.
no code implementations • 25 Apr 2020 • Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.
no code implementations • 10 Apr 2020 • Stacy Colaco, Karisma Chhabria, Domdatt Singh, Anshul Bhide, Neha Singh, Abhishek Singh, Atahar Husein, Anuradha Mishra, Richa Sharma, Nancy Ashary, Deepak Modi
To predict if developing human embryos are permissive to coronaviruses, we analyzed publicly available single cell RNA-seq datasets of zygotes, 4-cell, 8-cell, morula, inner cell mass, epiblast, primitive endoderm and trophectoderm for the coronavirus receptors (ACE2, BSG, DPP4 and ANPEP), the Spike protein cleavage enzymes (TMPRSS2, CTSL).
1 code implementation • 19 Mar 2020 • Ramesh Raskar, Isabel Schunemann, Rachel Barbar, Kristen Vilcans, Jim Gray, Praneeth Vepakomma, Suraj Kapa, Andrea Nuzzo, Rajiv Gupta, Alex Berke, Dazza Greenwood, Christian Keegan, Shriank Kanaparti, Robson Beaudry, David Stansbury, Beatriz Botero Arcila, Rishank Kanaparti, Francesco M Benedetti, Alina Clough, Riddhiman Das, Kaushal Jain, Khahlil Louisy, Greg Nadeau, Vitor Pamplona, Steve Penrod, Yasaman Rajaee, Abhishek Singh, Greg Storm, John Werner
Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited.
Cryptography and Security Computers and Society Distributed, Parallel, and Cluster Computing
3 code implementations • EMNLP (ClinicalNLP) 2020 • Kexin Huang, Abhishek Singh, Sitong Chen, Edward T. Moseley, Chih-ying Deng, Naomi George, Charlotta Lindvall
Clinical notes contain rich data, which is unexploited in predictive modeling compared to structured data.
no code implementations • 19 Oct 2019 • Abhishek Singh, Anubhav Garg, Jinan Zhou, Shiv Ram Dubey, Debo Dutta
Neural Architecture Search (NAS) represents a class of methods to generate the optimal neural network architecture and typically iterate over candidate architectures till convergence over some particular metric like validation loss.
no code implementations • 27 Sep 2019 • Indu Ilanchezian, Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta, G. N. Srinivasa Prasanna, Ramesh Raskar
In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection.
no code implementations • 18 Sep 2019 • Abhishek Singh, Praneeth Vepakomma, Otkrist Gupta, Ramesh Raskar
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning.
no code implementations • NAACL 2019 • Abhishek Singh, Eduardo Blanco, Wei Jin
Tweets are short messages that often include specialized language such as hashtags and emojis.
no code implementations • 16 Dec 2018 • Bhaskar Gautam, Annappa Basava, Abhishek Singh, Amit Agrawal
The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized.
no code implementations • 18 Mar 2018 • Purushotham Kamath, Abhishek Singh, Debo Dutta
Fast Neural Architecture Construction (NAC) is a method to construct deep network architectures by pruning and expansion of a base network.
no code implementations • ICML 2018 AutoML Workshop 2018 • Purushotham Kamath, Abhishek Singh, Debo Dutta
Its key architectural features are the decoupling of the network generation from the network evaluation, support for network instrumentation, open model specification and a microservices based architecture for deployment at scale.
no code implementations • CVPR 2015 • Jia-Bin Huang, Abhishek Singh, Narendra Ahuja
However, the internal dictionary obtained from the given image may not always be sufficiently expressive to cover the textural appearance variations in the scene.
no code implementations • CVPR 2014 • Abhishek Singh, Fatih Porikli, Narendra Ahuja
We then show that by taking a convex combination of orientation and frequency selective bands of the noisy and the denoised HR images, we can obtain a desired HR image where (i) some of the textural signal lost in the denoising step is effectively recovered in the HR domain, and (ii) additional textures can be easily synthesized by appropriately constraining the parameters of the convex combination.