1 code implementation • 23 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.
no code implementations • 3 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.
no code implementations • 26 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.
no code implementations • 15 Jul 2022 • Michael Desmond, Evelyn Duesterwald, Vatche Isahagian, Vinod Muthusamy
We propose a paradigm for the construction of business automations using natural language.
no code implementations • 1 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.
no code implementations • 9 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.
no code implementations • 15 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.
no code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 22 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.
no code implementations • 21 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.
no code implementations • 14 Sep 2019 • K. R. Jayaram, Vinod Muthusamy, Parijat Dube, Vatche Ishakian, Chen Wang, Benjamin Herta, Scott Boag, Diana Arroyo, Asser Tantawi, Archit Verma, Falk Pollok, Rania Khalaf
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
no code implementations • 16 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.
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