Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for the task-specific models, when compared to training the models separately. Early versions of MTL were called "hints".
Business process modeling (BPM) in business process management and systems engineering is the activity of representing processes of an enterprise, so that the current business processes may be analyzed, improved, and automated. BPM is typically performed by business analysts, who provide expertise in the modeling discipline; by subject matter experts, who have specialized knowledge of the processes being modeled; or more commonly by a team comprising both. Alternatively, the process model can be derived directly from events' logs using process mining tools.
In software engineering, profiling is a form of dynamic program analysis that measures, for example, the space (memory) or time complexity of a program, the usage of particular instructions, or the frequency and duration of function calls. Most commonly, profiling information serves to aid program optimization, and more specifically, performance engineering.
In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler written in the source programming language that it intends to compile. An initial core version of the compiler is generated in a different language ; successive expanded versions of the compiler are developed using this minimal subset of the language. The problem of compiling a self-compiling compiler has been called the chicken-or-egg problem in compiler design, and bootstrapping is a solution to this problem.
Process mining is a family of techniques relating the fields of data science and process management to support the analysis of operational processes based on event logs. The goal of process mining is to turn event data into insights and actions. Process mining is an integral part of data science, fueled by the availability of event data and the desire to improve processes. Process mining techniques use event data to show what people, machines, and organizations are really doing. Process mining provides novel insights that can be used to identify the execution paths taken by operational processes and address their performance and compliance problems.
Monica Sin-Ling Lam is an American computer scientist. She is a professor in the Computer Science Department at Stanford University.
A hyper-heuristic is a heuristic search method that seeks to automate, often by the incorporation of machine learning techniques, the process of selecting, combining, generating or adapting several simpler heuristics to efficiently solve computational search problems. One of the motivations for studying hyper-heuristics is to build systems which can handle classes of problems rather than solving just one problem.
In computing, compiler correctness is the branch of computer science that deals with trying to show that a compiler behaves according to its language specification. Techniques include developing the compiler using formal methods and using rigorous testing on an existing compiler.
The Interactive Compilation Interface (ICI) is a plugin system with a high-level compiler-independent and low-level compiler-dependent API to transform production compilers into interactive research toolsets. It was developed by Grigori Fursin during the MILEPOST project. The ICI framework acts as a "middleware" interface between the compiler and the user-definable plugins. It opens up and reuses the production-quality compiler infrastructure to enable program analysis and instrumentation, fine-grain program optimizations, simple prototyping of new development and research ideas while avoiding building new compilation tools from scratch. For example, it is used in MILEPOST GCC to automate compiler and architecture design and program optimizations based on statistical analysis and machine learning, and predict profitable optimization to improve program execution time, code size and compilation time.
MILEPOST GCC is a free, community-driven, open-source, adaptive, self-tuning compiler that combines stable production-quality GCC, Interactive Compilation Interface and machine learning plugins to adapt to any given architecture and program automatically and predict profitable optimizations to improve program execution time, code size and compilation time. It is currently used and supported by academia and industry and is intended to open up research opportunities to automate compiler and architecture design and optimization.
The Collective Tuning Initiative is a community-driven initiative started by Grigori Fursin to develop free and open-source research tools with a unified API for collaborative characterization, optimization and co-design of computer systems. They enable sharing of benchmarks, data sets and optimization cases from the community in the Collective Optimization Database through unified web services to predict better optimizations or architecture designs. Using common research-and-development tools should help to improve the quality and reproducibility of computer systems' research and development and accelerate innovation in this area. This approach helped establish Reproducibility Initiatives and Artifact Evaluation at several ACM-sponsored conferences to encourage sharing of artifacts and validation of experimental results from accepted papers.
A scientific workflow system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or workflow, in a scientific application.
Artifact-centric business process model represents an operational model of business processes in which the changes and evolution of business data, or business entities, are considered as the main driver of the processes. The artifact-centric approach, a kind of data-centric business process modeling, focuses on describing how business data is changed/updated, by a particular action or task, throughout the process.
Kathleen Shanahan Fisher is an American computer scientist who specializes in programming languages and their implementation.
The Collective Knowledge (CK) project is an open-source framework and repository to enable collaborative, reproducible and sustainable research and development of complex computational systems. CK is a small, portable, customizable and decentralized infrastructure helping researchers and practitioners:
Grigori Fursin is a British computer scientist, president of the non-profit CTuning foundation, founding member of MLCommons, co-chair of the MLCommons Task Force on Automation and Reproducibility and founder of cKnowledge. His research group created open-source machine learning based self-optimizing compiler, MILEPOST GCC, considered to be the first in the world. At the end of the MILEPOST project he established cTuning foundation to crowdsource program optimisation and machine learning across diverse devices provided by volunteers. His foundation also developed Collective Knowledge Framework to support open research. Since 2015 Fursin leads Artifact Evaluation at several ACM and IEEE computer systems conferences. He is also a founding member of the ACM taskforce on Data, Software, and Reproducibility in Publication.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems.
ModelOps, as defined by Gartner, "is focused primarily on the governance and lifecycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models". "ModelOps lies at the heart of any enterprise AI strategy". It orchestrates the model lifecycles of all models in production across the entire enterprise, from putting a model into production, then evaluating and updating the resulting application according to a set of governance rules, including both technical and business KPI's. It grants business domain experts the capability to evaluate AI models in production, independent of data scientists.
Collective Mind (CM) is collection of portable, extensible and ready-to-use automation recipes with a human-friendly interface to make it easier to compose, benchmark and optimize complex AI, ML and other applications and systems across diverse and continuously changing models, data sets, software and hardware.
