390,055 research outputs found
Sharing Open Deep Learning Models
We examine how and why trained deep learning (DL) models are shared, and by whom, and why some developers share their models while others do not. Prior research has examined sharing of data and software code, but DL models are a hybrid of the two. The results from a Qualtrics survey administered to GitHub users and academics who publish on DL show that a diverse population shares DL models, from students to computer/data scientists. We find that motivations for sharing include: increasing citation rates; contributing to the collaboration of developing new DL models; encouraging to reuse; establishing a good reputation; receiving feedback to improve the model; and personal enjoyment. Reasons for not sharing include: lack of time; thinking that their models would not be interesting for others; and not having permission for sharing. The study contributes to our understanding of motivations for participating in a novel form of peer-production
Open and Reusable Deep Learning for Pathology with WSInfer and QuPath
Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology
Open and reusable deep learning for pathology with WSInfer and QuPath
The field of digital pathology has seen a proliferation of deep learning
models in recent years. Despite substantial progress, it remains rare for other
researchers and pathologists to be able to access models published in the
literature and apply them to their own images. This is due to difficulties in
both sharing and running models. To address these concerns, we introduce
WSInfer: a new, open-source software ecosystem designed to make deep learning
for pathology more streamlined and accessible. WSInfer comprises three main
elements: 1) a Python package and command line tool to efficiently apply
patch-based deep learning inference to whole slide images; 2) a QuPath
extension that provides an alternative inference engine through user-friendly
and interactive software, and 3) a model zoo, which enables pathology models
and metadata to be easily shared in a standardized form. Together, these
contributions aim to encourage wider reuse, exploration, and interrogation of
deep learning models for research purposes, by putting them into the hands of
pathologists and eliminating a need for coding experience when accessed through
QuPath. The WSInfer source code is hosted on GitHub and documentation is
available at https://wsinfer.readthedocs.io
Deep Equilibrium Models Meet Federated Learning
In this study the problem of Federated Learning (FL) is explored under a new
perspective by utilizing the Deep Equilibrium (DEQ) models instead of
conventional deep learning networks. We claim that incorporating DEQ models
into the federated learning framework naturally addresses several open problems
in FL, such as the communication overhead due to the sharing large models and
the ability to incorporate heterogeneous edge devices with significantly
different computation capabilities. Additionally, a weighted average fusion
rule is proposed at the server-side of the FL framework to account for the
different qualities of models from heterogeneous edge devices. To the best of
our knowledge, this study is the first to establish a connection between DEQ
models and federated learning, contributing to the development of an efficient
and effective FL framework. Finally, promising initial experimental results are
presented, demonstrating the potential of this approach in addressing
challenges of FL.Comment: The paper has been accepted for publication in European Signal
Processing Conference, Eusipco 202
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
We present the Open MatSci ML Toolkit: a flexible, self-contained, and
scalable Python-based framework to apply deep learning models and methods on
scientific data with a specific focus on materials science and the OpenCatalyst
Dataset. Our toolkit provides: 1. A scalable machine learning workflow for
materials science leveraging PyTorch Lightning, which enables seamless scaling
across different computation capabilities (laptop, server, cluster) and
hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for
rapid graph neural network prototyping and development. By publishing and
sharing this toolkit with the research community via open-source release, we
hope to: 1. Lower the entry barrier for new machine learning researchers and
practitioners that want to get started with the OpenCatalyst dataset, which
presently comprises the largest computational materials science dataset. 2.
Enable the scientific community to apply advanced machine learning tools to
high-impact scientific challenges, such as modeling of materials behavior for
clean energy applications. We demonstrate the capabilities of our framework by
enabling three new equivariant neural network models for multiple OpenCatalyst
tasks and arrive at promising results for compute scaling and model
performance.Comment: Paper accompanying Open-Source Software from
https://github.com/IntelLabs/matscim
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
Deep Audio Analyzer: a Framework to Industrialize the Research on Audio Forensics
Deep Audio Analyzer is an open source speech framework that aims to simplify
the research and the development process of neural speech processing pipelines,
allowing users to conceive, compare and share results in a fast and
reproducible way. This paper describes the core architecture designed to
support several tasks of common interest in the audio forensics field, showing
possibility of creating new tasks thus customizing the framework. By means of
Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies)
and researchers will be able to visualize audio features, easily evaluate
performances on pretrained models, to create, export and share new audio
analysis workflows by combining deep neural network models with few clicks. One
of the advantages of this tool is to speed up research and practical
experimentation, in the field of audio forensics analysis thus also improving
experimental reproducibility by exporting and sharing pipelines. All features
are developed in modules accessible by the user through a Graphic User
Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning
Audio Pipeline creation, Audio Forensics
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