108,135 research outputs found
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
MoBYv2AL: Self-supervised Active Learning for Image Classification
Active learning(AL) has recently gained popularity for deep learning(DL) models. This is due to efficient and informative sampling, especially when the learner requires large-scale labelled datasets. Commonly, the sampling and training happen in stages while more batches are added. One main bottleneck in this strategy is the narrow representation learned by the model that affects the overall AL selection. We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY - one of the most successful self-supervised learning algorithms to the AL pipeline. Thus, we add the downstream task-aware objective function and optimize it jointly with contrastive loss. Further, we derive a data-distribution selection function from labelling the new examples. Finally, we test and study our pipeline robustness and performance for image classification tasks. We successfully achieved state-of-the-art results when compared to recent AL methods
Towards Free Data Selection with General-Purpose Models
A desirable data selection algorithm can efficiently choose the most
informative samples to maximize the utility of limited annotation budgets.
However, current approaches, represented by active learning methods, typically
follow a cumbersome pipeline that iterates the time-consuming model training
and batch data selection repeatedly. In this paper, we challenge this status
quo by designing a distinct data selection pipeline that utilizes existing
general-purpose models to select data from various datasets with a single-pass
inference without the need for additional training or supervision. A novel free
data selection (FreeSel) method is proposed following this new pipeline.
Specifically, we define semantic patterns extracted from inter-mediate features
of the general-purpose model to capture subtle local information in each image.
We then enable the selection of all data samples in a single pass through
distance-based sampling at the fine-grained semantic pattern level. FreeSel
bypasses the heavy batch selection process, achieving a significant improvement
in efficiency and being 530x faster than existing active learning methods.
Extensive experiments verify the effectiveness of FreeSel on various computer
vision tasks. Our code is available at https://github.com/yichen928/FreeSel.Comment: accepted by NeurIPS 202
Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathology) for creating and deploying deep learning-based segmentation models for computational pathology. We demonstrate the pipeline on a use case of separating epithelium from stroma in colonic mucosa. A dataset of 251 annotated WSIs, comprising 140 hematoxylin-eosin (HE)-stained and 111 CD3 immunostained colon biopsy WSIs, were developed through active learning using the pipeline. On a hold-out test set of 36 HE and 21 CD3-stained WSIs a mean intersection over union score of 95.5 and 95.3% was achieved on epithelium segmentation. We demonstrate pathologist-level segmentation accuracy and clinical acceptable runtime performance and show that pathologists without programming experience can create near state-of-the-art segmentation solutions for histopathological WSIs using only free-to-use software. The study further demonstrates the strength of open-source solutions in its ability to create generalizable, open pipelines, of which trained models and predictions can seamlessly be exported in open formats and thereby used in external solutions. All scripts, trained models, a video tutorial, and the full dataset of 251 WSIs with ~31 k epithelium annotations are made openly available at to accelerate research in the field.Peer reviewe
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology
available to every developer. In the near future, every application on every
platform will incorporate trained models to encode data-based decisions that
would be impossible for developers to author. This presents a significant
engineering challenge, since currently data science and modeling are largely
decoupled from standard software development processes. This separation makes
incorporating machine learning capabilities inside applications unnecessarily
costly and difficult, and furthermore discourage developers from embracing ML
in first place. In this paper we present ML .NET, a framework developed at
Microsoft over the last decade in response to the challenge of making it easy
to ship machine learning models in large software applications. We present its
architecture, and illuminate the application demands that shaped it.
Specifically, we introduce DataView, the core data abstraction of ML .NET which
allows it to capture full predictive pipelines efficiently and consistently
across training and inference lifecycles. We close the paper with a
surprisingly favorable performance study of ML .NET compared to more recent
entrants, and a discussion of some lessons learned
The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice
Companies struggle to continuously develop and deploy AI models to complex
production systems due to AI characteristics while assuring quality. To ease
the development process, continuous pipelines for AI have become an active
research area where consolidated and in-depth analysis regarding the
terminology, triggers, tasks, and challenges is required. This paper includes a
Multivocal Literature Review where we consolidated 151 relevant formal and
informal sources. In addition, nine-semi structured interviews with
participants from academia and industry verified and extended the obtained
information. Based on these sources, this paper provides and compares
terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle
management, and CD4ML. Furthermore, the paper provides an aggregated list of
potential triggers for reiterating the pipeline, such as alert systems or
schedules. In addition, this work uses a taxonomy creation strategy to present
a consolidated pipeline comprising tasks regarding the continuous development
of AI. This pipeline consists of four stages: Data Handling, Model Learning,
Software Development and System Operations. Moreover, we map challenges
regarding pipeline implementation, adaption, and usage for the continuous
development of AI to these four stages.Comment: accepted in the Journal Systems and Softwar
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