Zobrazeno 1 - 10
of 31
pro vyhledávání: '"PALEYES, ANDREI"'
Software systems impact society at different levels as they pervasively solve real-world problems. Modern software systems are often so sophisticated that their complexity exceeds the limits of human comprehension. These systems must respond to chang
Externí odkaz:
http://arxiv.org/abs/2401.11370
Autor:
Paleyes, Andrei, Lawrence, Neil D.
Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of d
Externí odkaz:
http://arxiv.org/abs/2304.11987
Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software de
Externí odkaz:
http://arxiv.org/abs/2303.09552
Autor:
Picheny, Victor, Berkeley, Joel, Moss, Henry B., Stojic, Hrvoje, Granta, Uri, Ober, Sebastian W., Artemev, Artem, Ghani, Khurram, Goodall, Alexander, Paleyes, Andrei, Vakili, Sattar, Pascual-Diaz, Sergio, Markou, Stratis, Qing, Jixiang, Loka, Nasrulloh R. B. S, Couckuyt, Ivo
We present Trieste, an open-source Python package for Bayesian optimization and active learning benefiting from the scalability and efficiency of TensorFlow. Our library enables the plug-and-play of popular TensorFlow-based models within sequential d
Externí odkaz:
http://arxiv.org/abs/2302.08436
Machine Learning models are being deployed as parts of real-world systems with the upsurge of interest in artificial intelligence. The design, implementation, and maintenance of such systems are challenged by real-world environments that produce larg
Externí odkaz:
http://arxiv.org/abs/2302.04810
Inference is a significant part of ML software infrastructure. Despite the variety of inference frameworks available, the field as a whole can be considered in its early days. This position paper puts forth a range of important qualities that next ge
Externí odkaz:
http://arxiv.org/abs/2210.14665
We present HIghly Parallelisable Pareto Optimisation (HIPPO) -- a batch acquisition function that enables multi-objective Bayesian optimisation methods to efficiently exploit parallel processing resources. Multi-Objective Bayesian Optimisation (MOBO)
Externí odkaz:
http://arxiv.org/abs/2206.13326
As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ML within large software systems brings
Externí odkaz:
http://arxiv.org/abs/2204.12781
Autor:
Paleyes, Andrei, Pullin, Mark, Mahsereci, Maren, McCollum, Cliff, Lawrence, Neil D., Gonzalez, Javier
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems. Tools to deal with this challenge include simulations to gather information and statistical emulation to quantify uncertainty. The machine learning community has
Externí odkaz:
http://arxiv.org/abs/2110.13293
Despite huge successes reported by the field of machine learning, such as voice assistants or self-driving cars, businesses still observe very high failure rate when it comes to deployment of ML in production. We argue that part of the reason is infr
Externí odkaz:
http://arxiv.org/abs/2108.04105