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Found 14 papers, 1 papers with code
IBM Deep Learning Service
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
Neurology-as-a-Service for the Developing World
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.
FfDL : A Flexible Multi-tenant Deep Learning Platform
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
AI Trust in business processes: The need for process-aware explanations
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.
PipeSim: Trace-driven Simulation of Large-Scale AI Operations Platforms
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.
From Robotic Process Automation to Intelligent Process Automation: Emerging Trends
In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes.
A Conversational Digital Assistant for Intelligent Process Automation
Robotic process automation (RPA) has emerged as the leading approach to automate tasks in business processes.
Do's and Don'ts for Human and Digital Worker Integration
Robotic process automation (RPA) and its next evolutionary stage, intelligent process automation, promise to drive improvements in efficiencies and process outcomes.
Extending LIME for Business Process Automation
AI business process applications automate high-stakes business decisions where there is an increasing demand to justify or explain the rationale behind algorithmic decisions.
Natural Language Sentence Generation from API Specifications
APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks.
A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language
We propose a paradigm for the construction of business automations using natural language.
A Case for Business Process-Specific Foundation Models
The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks.
FedGen: Generalizable Federated Learning for Sequential Data
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.
API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
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.
