Search Results for author: ASHISH SHARMA

Found 11 papers, 5 papers with code

Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support

no code implementations28 Mar 2022 ASHISH SHARMA, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff

Advances in artificial intelligence (AI) are enabling systems that augment and collaborate with humans to perform simple, mechanistic tasks like scheduling meetings and grammar-checking text.

Applications and Techniques for Fast Machine Learning in Science

no code implementations25 Oct 2021 Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini, Thea Aarrestad, Steffen Bahr, Jurgen Becker, Anne-Sophie Berthold, Richard J. Bonventre, Tomas E. Muller Bravo, Markus Diefenthaler, Zhen Dong, Nick Fritzsche, Amir Gholami, Ekaterina Govorkova, Kyle J Hazelwood, Christian Herwig, Babar Khan, Sehoon Kim, Thomas Klijnsma, Yaling Liu, Kin Ho Lo, Tri Nguyen, Gianantonio Pezzullo, Seyedramin Rasoulinezhad, Ryan A. Rivera, Kate Scholberg, Justin Selig, Sougata Sen, Dmitri Strukov, William Tang, Savannah Thais, Kai Lukas Unger, Ricardo Vilalta, Belinavon Krosigk, Thomas K. Warburton, Maria Acosta Flechas, Anthony Aportela, Thomas Calvet, Leonardo Cristella, Daniel Diaz, Caterina Doglioni, Maria Domenica Galati, Elham E Khoda, Farah Fahim, Davide Giri, Benjamin Hawks, Duc Hoang, Burt Holzman, Shih-Chieh Hsu, Sergo Jindariani, Iris Johnson, Raghav Kansal, Ryan Kastner, Erik Katsavounidis, Jeffrey Krupa, Pan Li, Sandeep Madireddy, Ethan Marx, Patrick McCormack, Andres Meza, Jovan Mitrevski, Mohammed Attia Mohammed, Farouk Mokhtar, Eric Moreno, Srishti Nagu, Rohin Narayan, Noah Palladino, Zhiqiang Que, Sang Eon Park, Subramanian Ramamoorthy, Dylan Rankin, Simon Rothman, ASHISH SHARMA, Sioni Summers, Pietro Vischia, Jean-Roch Vlimant, Olivia Weng

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.

Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach

1 code implementation19 Jan 2021 ASHISH SHARMA, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff

Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context.

Dialogue Generation Language Modelling +2

A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support

2 code implementations EMNLP 2020 Ashish Sharma, Adam S. Miner, David C. Atkins, Tim Althoff

We develop a novel unifying theoretically-grounded framework for characterizing the communication of empathy in text-based conversations.

A DICOM Framework for Machine Learning Pipelines against Real-Time Radiology Images

1 code implementation16 Apr 2020 Pradeeban Kathiravelu, Puneet Sharma, ASHISH SHARMA, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, Judy Wawira Gichoya

Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.

DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis

no code implementations10 Aug 2019 Supreeth P. Shashikumar, Christopher Josef, ASHISH SHARMA, Shamim Nemati

Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU).

Time Series

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images

no code implementations journal 2019 D. P. YADAV, ASHISH SHARMA, MADHUSUDAN SINGH, AND AYUSH GOYAL

The 74 images of test data set are tested with the proposed SVM based method and according to the ground truth, the accuracy of 82. 43% was achieved for the SVM based model, which was higher than the 79. 73% achieved in past work using the multidimensional scaling analysis (MDS) approach

Improving Latent User Models in Online Social Media

no code implementations30 Nov 2017 Adit Krishnan, ASHISH SHARMA, Hari Sundaram

Modern social platforms are characterized by the presence of rich user-behavior data associated with the publication, sharing and consumption of textual content.

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