Milad Vazan
This book is intended for beginners who have no familiarity with deep learning. Our only expectation from readers is that they already have the basic programming skills in Python.
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We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.
In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.
DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions. The availability of a representative selection of DPMs in a single library makes it possible to combine them in a straightforward manner, a common practice in deep learning research nowadays. In addition, it includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs. The development of DeeProb-kit will help t…
Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation such as PyTorch or TensorFlow. However, when models are used for inference they are typically extracted from Python as TensorFlow graphs or TorchScript programs in order to meet performance and packaging constraints. The extraction process can be time consuming, impeding fast prototyping. We show how it is possible to meet these performance and packaging constraints while performing inference in Python. In particular…
Modern deep learning frameworks provide imperative, eager execution programming interfaces embedded in Python to provide a productive development experience. However, deep learning practitioners sometimes need to capture and transform program structure for performance optimization, visualization, analysis, and hardware integration. We study the different designs for program capture and transformation used in deep learning. By designing for typical deep learning use cases rather than long tail ones, it is possible to create a simpler framework for program capture and transformation. We apply th…
bde: A Python Package for Bayesian Deep Ensembles via MILE
bde是一个用于贝叶斯深度集成的Python包，专注于表格数据，基于JAX实现的MILE方法，提供兼容scikit-learn的估计器，实现快速训练、高效MCMC采样和不确定性量化。
Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
针对机器学习研究应用中原型构建、特征选择等步骤耗时的问题，推出兼容主流框架的DFMLU Python库，提供模型搜索、特征选择等工具，附示例以简化ML原型开发。
Pixyz: a Python library for developing deep generative models
本文提出Python库Pixyz，通过三个API和记忆化技术简洁高效地实现深度生成模型，实验表明其训练速度优于现有概率编程语言。
Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed \emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural networks in Python. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and seamlessly integrates with them. Its BSD license makes it suitable for both acade…
dlordinal: a Python package for deep ordinal classification
本文介绍 Python 库 dlordinal，它基于 PyTorch 统一实现多种深度序数分类方法、损失、输出层及序数评价指标。
A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP
本研究提出一种基于回归、仅需参考事件标注、采用堆叠集成元模型的多变量时间序列事件检测深度学习方法，并配套开发了可通过PyPI安装的Python包eventdetector-ts，经多领域实际用例验证具备通用性与有效性。
Detecting microbial biomarkers used to predict disease phenotypes and clinical outcomes is crucial for disease early-stage screening and diagnosis. Most methods for biomarker identification are linear-based, which is very limited as biological processes are rarely fully linear. The introduction of machine learning to this field tends to bring a promising solution. However, identifying microbial biomarkers in an interpretable, data-driven and robust manner remains challenging. We present MIIDL, a Python package for the identification of microbial biomarkers based on interpretable deep learning.…
Due to the rise of machine learning, Python is an increasingly popular programming language. Python, however, is dynamically typed. Dynamic typing has shown to have drawbacks when a project grows, while at the same time it improves developer productivity. To have the benefits of static typing, combined with high developer productivity, types need to be inferred. In this paper, we present DLTPy: a deep learning type inference solution for the prediction of types in function signatures based on the natural language context (identifier names, comments and return expressions) of a function. We fou…
si4onnx: A Python package for Selective Inference in Deep Learning Models
本文提出 si4onnx，一个兼容 PyTorch 和 TensorFlow 的 Python 包，通过控制 I 类错误率的选择性推断评估深度学习模型中输入图像显著区域的统计显著性，以增强 AI 系统的可靠性。
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision Prototyping
本文提出Python库Deep Fast Vision，通过嵌套字典定义简化深度学习视觉流程，降低使用门槛，促进迁移学习在图像分类等任务中的普及。
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
DetoxAI是一个开源Python工具包，通过事后去偏、内部表示干预、公平性指标与可视化工具，解决深度学习视觉分类中的公平性问题。
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
针对现有本体工具缺乏深度学习适配能力、跨语言生态不兼容的问题，本文提出Python工具包DeepOnto，基于OWL API封装扩展本体处理能力，集成预训练语言模型等深度学习方法支持本体工程任务，并通过两个实际用例验证其实用性。
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
本文提出schlably框架，为基于深度强化学习的生产调度研究提供标准化Python工具集，减少重复开发工作并提升实验的可比性与可复用性。
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
针对大地测量时间序列非线性、非平稳且不完整的预测难题，提出开源Python包GTS_Forecaster，集成KAN、GNNGRU、TimeGNN等深度学习模型与KTIF间隙填充算法，支持GNSS等数据集的预测与评估，促进了深度学习在大地测量任务中的应用。
TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
TorchDA软件包将深度神经网络与卡尔曼滤波、EnKF、3DVar和4DVar等数据同化算法无缝结合，用于状态转移和观测函数建模，并在Lorenz 63和二维浅水系统上验证了其显著优于无同化模型预测的性能。
Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1
本研究借助OASIS耦合器的Python接口，将神经网络深对流参数化方案集成进全球大气模型ARP-GEM1，5年模拟验证其效果与原物理方案吻合良好，且通过GPU部署提升了推理速度。
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
本书通过Python语言，系统讲解机器学习和深度学习的基础理论与实践，涵盖数学基础、算法实现及大数据应用，帮助读者掌握AI开发的核心技能。
Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility and productivity. Lack of static typing can cause run-time exceptions and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing codebases is error-prone and laborious, machine learning (ML)-based approaches have been proposed to enable automatic type inference based on existing, partially annotated codebases. However, previous ML-based approaches are trained and evaluated on human-provided type an…
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
SPD Learn是一种基于对称正定矩阵的几何深度学习Python库，通过平凡化参数化实现流形约束，提供统一的神经网络层和SPD运算符，用于神经解码和脑机接口应用。
Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation. Latte is compatible with both PyTorch and TensorFlow/Keras, and provides both functional and modular APIs that can be easily extended to support other deep learning frameworks. Using NumPy-based and framework-agnostic implementation, Latte ensures reproducible, consistent, and deterministic metric calculations regardless of the deep learning framework of choice.
bacpipe: a Python package to make bioacoustic deep learning models accessible
bacpipe提供了一套生物声学深度学习模型和评估流程，通过图形和编程接口让生态学家和计算机科学家更方便地使用最新模型，提升分析效率。
pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons
本文提出RTKLIB的Python绑定pyrtklib及基于其的深度学习子系统，解决传统GNSS算法与深度学习工具的技术鸿沟，通过预测定位过程中的权重和偏差，显著提升城市峡谷中的定位精度。
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the input, and generates relevant interpretations as the output with the post-hoc manner. From the functionality perspective, XDeep integrates a wide range of interpretation algorithms from the state-of-the-arts, covering different types of methodologies, and is capable of providing both local explanation and global explanation for DNN when interpreting model behaviours. With the well-documented API designed in XDeep, end-…
Context: Identifying potential vulnerable code is important to improve the security of our software systems. However, the manual detection of software vulnerabilities requires expert knowledge and is time-consuming, and must be supported by automated techniques. Objective: Such automated vulnerability detection techniques should achieve a high accuracy, point developers directly to the vulnerable code fragments, scale to real-world software, generalize across the boundaries of a specific software project, and require no or only moderate setup or configuration effort. Method: In this article, w…
Processing of medical images such as MRI or CT presents unique challenges compared to RGB images typically used in computer vision. These include a lack of labels for large datasets, high computational costs, and metadata to describe the physical properties of voxels. Data augmentation is used to artificially increase the size of the training datasets. Training with image patches decreases the need for computational power. Spatial metadata needs to be carefully taken into account in order to ensure a correct alignment of volumes. We present TorchIO, an open-source Python library to enable effi…
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastable and coherent sets, slow relaxation processes, collective variables dominant transition pathways or manifolds and channels of probability flow can be of great importance for understanding and characterizing the kinetic, thermodynamic and mechanistic properties of the system. Deeptime is a general purpose Python library offering various tools to estimate dynamical models based on time-series data including conventio…
Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, several systems have been proposed to combine the usability of imperative programming with the optimized performance of symbolic graph execution. Such systems convert imperative Python DL programs to optimized symbolic graphs and execute them. However, they cannot fully support the usability of imperative programming. For example, if an imperative DL program contains a Python feature with no corresponding symbolic represen…
Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable progress has been made of using various deep learning techniques to solve the KT problem. However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge. First, data preprocessing procedures in existing works are often private and custom, which l…
Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
本文介绍了Continual Inference，一个在PyTorch中实现CINs的Python库，专为在线和批处理场景的高效推理设计，并提供了全面的实践指南、最佳实践和代码示例。
Background: Breast cancer is a multifactorial disease, genetic and environmental factors will affect its incidence probability. Breast cancer metastasis is one of the main cause of breast cancer related deaths reported by the American Cancer Society (ACS). Method: the iMedBot is a web application that we developed using the python Flask web framework and deployed on Amazon Web Services. It contains a frontend and a backend. The backend is supported by a python program we developed using the python Keras and scikit-learn packages, which can be used to learn deep feedforward neural network (DFNN…
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab是一个模块化Python包，通过解耦设计与可组合正则化损失项，支持深度神经网络在分布外数据上的领域泛化训练与基准测试。
Deep autoencoders are often extended with a supervised or adversarial loss to learn latent representations with desirable properties, such as greater predictivity of labels and outcomes or fairness with respects to a sensitive variable. Despite the ubiquity of supervised and adversarial deep latent factor models, these methods should demonstrate improvement over simpler linear approaches to be preferred in practice. This necessitates a reproducible linear analog that still adheres to an augmenting supervised or adversarial objective. We address this methodological gap by presenting methods tha…
Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive i…
Bayesian neural networks via MCMC: a Python-based tutorial
本文提供了一份基于Python的MCMC方法教程，涵盖贝叶斯线性、逻辑回归及贝叶斯神经网络，旨在通过代码实现弥合理论与应用鸿沟，并展示了MCMC在基准问题上的优劣及多模态后验采样的挑战。
Recently, a lot of papers proposed to use neural networks to approximately solve partial differential equations (PDEs). Yet, there has been a lack of flexible framework for convenient experimentation. In an attempt to fill the gap, we introduce a PyDEns-module open-sourced on GitHub. Coupled with capabilities of BatchFlow, open-source framework for convenient and reproducible deep learning, PyDEns-module allows to 1) solve partial differential equations from a large family, including heat equation and wave equation 2) easily search for the best neural-network architecture among the zoo, that i…
MALPOLON: A Framework for Deep Species Distribution Modeling
本文介绍了基于PyTorch的深度物种分布建模框架MALPOLON，通过模块化设计、多GPU支持和详尽文档，降低了生态学家使用深度学习构建SDM的门槛并提升了性能可扩展性。
The rapid evolution of deep neural networks is demanding deep learning (DL) frameworks not only to satisfy the requirement of quickly executing large computations, but also to support straightforward programming models for quickly implementing and experimenting with complex network structures. However, existing frameworks fail to excel in both departments simultaneously, leading to diverged efforts for optimizing performance and improving usability. This paper presents JANUS, a system that combines the advantages from both sides by transparently converting an imperative DL program written in P…
Recent work has shown that Field-Programmable Gate Arrays (FPGAs) play an important role in the acceleration of Machine Learning applications. Initial specification of machine learning applications are often done using a high-level Python-oriented framework such as Tensorflow, followed by a manual translation to either C or RTL for synthesis using vendor tools. This manual translation step is time-consuming and requires expertise that limit the applicability of FPGAs in this important domain. In this paper, we present an open-source tool-flow that maps numerical computation models written in T…
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstra…
TorchCP: A Python Library for Conformal Prediction
TorchCP 是一个基于 PyTorch 的库，旨在将先进的置信预测算法集成到深度学习技术中，提升大规模数据中的不确定性量化能力。
HiQ -- A Declarative, Non-intrusive, Dynamic and Transparent Observability and Optimization System
本文提出非侵入式、声明式的动态透明系统 HiQ，用于追踪 Python 及分布式程序运行时信息并定位性能瓶颈，且已在深度学习生命周期管理中落地并开源。
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