Search Results for author: Vinod Muthusamy

Found 14 papers, 1 papers with code

API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs

no code implementations23 Feb 2024 Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, Luis A. Lastras

There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks.

Benchmarking slot-filling +2

FedGen: Generalizable Federated Learning for Sequential Data

no code implementations3 Nov 2022 Praveen Venkateswaran, Vatche Isahagian, Vinod Muthusamy, Nalini Venkatasubramanian

Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data.

Federated Learning

A Case for Business Process-Specific Foundation Models

no code implementations26 Oct 2022 Yara Rizk, Praveen Venkateswaran, Vatche Isahagian, Vinod Muthusamy

The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks.

Decision Making

Natural Language Sentence Generation from API Specifications

no code implementations1 Jun 2022 Siyu Huo, Kushal Mukherjee, Jayachandu Bandlamudi, Vatche Isahagian, Vinod Muthusamy, Yara Rizk

APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks.

Chatbot Intent Recognition +1

Extending LIME for Business Process Automation

no code implementations9 Aug 2021 Sohini Upadhyay, Vatche Isahagian, Vinod Muthusamy, Yara Rizk

AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions.

Do's and Don'ts for Human and Digital Worker Integration

no code implementations15 Oct 2020 Vinod Muthusamy, Merve Unuvar, Hagen Völzer, Justin D. Weisz

Robotic process automation (RPA) and its next evolutionary stage, intelligent process automation, promise to drive improvements in efficiencies and process outcomes.


A Conversational Digital Assistant for Intelligent Process Automation

no code implementations27 Jul 2020 Yara Rizk, Vatche Isahagian, Scott Boag, Yasaman Khazaeni, Merve Unuvar, Vinod Muthusamy, Rania Khalaf

Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes.

From Robotic Process Automation to Intelligent Process Automation: Emerging Trends

no code implementations27 Jul 2020 Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf, Yasaman Khazaeni, Vinod Muthusamy, Yara Rizk, Merve Unuvar

In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.

PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms

no code implementations22 Jun 2020 Thomas Rausch, Waldemar Hummer, Vinod Muthusamy

To optimize operations of production-grade AI workflow platforms we can leverage existing scheduling approaches, yet it is challenging to fine-tune operational strategies that achieve application-specific cost-benefit tradeoffs while catering to the specific domain characteristics of machine learning (ML) models, such as accuracy, robustness, or fairness.

Fairness Scheduling

AI Trust in business processes: The need for process-aware explanations

no code implementations21 Jan 2020 Steve T. K. Jan, Vatche Ishakian, Vinod Muthusamy

There is a large opportunity for infusing AI to reduce cost or provide better customer experience, and the business process management (BPM) literature is rich in machine learning solutions including unsupervised learning to gain insights on clusters of process traces, classification models to predict the outcomes, duration, or paths of partial process traces, extracting business process from documents, and models to recommend how to optimize a business process or navigate decision points.

Management Navigate

Neurology-as-a-Service for the Developing World

no code implementations16 Nov 2017 Tejas Dharamsi, Payel Das, Tejaswini Pedapati, Gregory Bramble, Vinod Muthusamy, Horst Samulowitz, Kush R. Varshney, Yuvaraj Rajamanickam, John Thomas, Justin Dauwels

In this work, we present a cloud-based deep neural network approach to provide decision support for non-specialist physicians in EEG analysis and interpretation.

EEG Electroencephalogram (EEG) +1

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

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