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Time-series forecasting models are invariably used in a variety of domains for crucial decision-making. Traditionally these models are constructed by experts with considerable manual effort. Unfortunately, this approach has poor scalability while ...

This study explores the development of classifiers for microbial images, specifically focusing on streptococci captured via microscopy of live samples. Our approach uses AutoML-based techniques and automates the creation and analysis of feature ...

The automated assessment of symbolic knowledge, derived, for instance, from extraction procedures, facilitates the autotuning of machine learning algorithms, obviating inherent biases in subjective human evaluations. Despite advancements, ...

We assess the feasibility of a reusable neural architecture search agent aimed at amortizing the initial time-investment in building a good search strategy. We do this through the use of Reinforcement Learning, where an agent learns to iteratively ...

Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task independent constructs: their general composition remains the ...

In recent years, researchers have developed several methods to automate discovering datasets and augmenting features for training Machine Learning (ML) models. Together with feature selection, these efforts have paved the way towards what is termed the ...

Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in image processing and computer vision. Neural architecture search (NAS) ...

Data augmentation has become an integral part of deep learning, as it is known to improve the generalization capabilities of neural networks. Since the most effective set of image transformations differs between tasks and domains, automatic data ...

Distillation-aware Architecture Search (DAS) seeks to discover the ideal student architecture that delivers superior performance by distilling knowledge from a given teacher model. Previous DAS methods involve time-consuming training-based search ...

In this paper, we introduce Auto-GAS, the first training-free Generative Architecture Search (GAS) framework enabled by an auto-discovered proxy. Generative models like Generative Adversarial Networks (GANs) are now widely used in many real-time ...$$

In this paper, we present AttnZero, the first framework for automatically discovering efficient attention modules tailored for Vision Transformers (ViTs). While traditional self-attention in ViTs suffers from quadratic computation complexity, ...

18F-FDG PET/CT images have been proven promising for the prognosis of Diffuse Large B-cell Lymphoma (DLBCL) patients. However, the implicit drawbacks of images constrain their wide applications. In this paper, we propose a fusion solution which ...

In contemporary urban environments, the proliferation of smart city initiatives has generated vast volumes of open data, offering unparalleled opportunities for enhancing municipal services and resource allocation. Machine learning offers a wide range of ...

Association Rule Mining is an important subfield of data mining, which consists of extracting interesting associations between items that coexist in transactions on databases. The transactions dataset may be of different types, like (a) a market basket ...

In this paper, we present a novel Bayesian optimization method named Conditional Local Bayesian Optimization (CLBO) designed specifically to address challenges in optimizing Automated Machine Learning (AutoML) tasks. Inspired by a controller-responder ...

Automated machine learning (AutoML) allows for selecting, parametrizing, and composing learning algorithms for a given data set. While resources play a pivotal role in neural architecture search, it is less pronounced by classical AutoML ...

A configurable system is characterized by the configuration options present or absent in its variants. Selecting and deselecting those configuration options directly influences the functional properties of the system. Apart from functional properties, ...

The exponential growth of data from digitization requires efficient utilization and storage of large amounts of data. Data lakes can store heterogeneous datasets and prepare them for machine learning (ML). However, current data lakes lack mature ...

Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-independent and identically distributed (Non-IID) private data and ...

Accurately predicting parallel application performance across diverse architectures is crucial for cost-effective platform selection and optimization. The existing analytic predictive approaches pose challenges in building accurate, scalable, and ...