engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this princip...
A deep learning framework for neuroscience 郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 系统神经科学寻求有关大脑如何执行各种感知,认知和运动任务的解释。相反,AI试图根据必须解决的任务来设计计算系统。在ANN中,设计指定的三个组成部分是目标函数,学习规则和结构。随着利用脑启发性架构的深度学习取得越...
The main novel contribution of this paper is the construction and formulation of a deep learning framework that allows a holistic approach to continual learning. Key to the viability of progressive learning is the following: Progressive learning is, to the best of our knowledge, the first to ...
A deep-learning framework for multi-level peptide–protein interaction prediction多层次多肽-蛋白相互作用预测深度学习框架 本文的生物医学背景: Nat Commun | 曾坚阳团队开发蛋白-多肽相互作用预测的深度学习模型 (qq.com) 多肽涉及和参与生物体内各种细胞过程,比如信号传导、基因表达调控、细胞增殖和凋亡,在生物体...
models preprocess README.md data_helpers.py helper.py main.py requirements.txt train_models.py tree_model_interpreter.py DeepGBM Implementation for the paper "DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks", which has been accepted by KDD'2019 as an Oral Paper...
A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nat Mach Intell 3, 68–75 (2021). https://doi.org/10.1038/s42256-020-00276-w Download citation Received27 February 2020 Accepted16 November 2020 Published04 January 2021 Issue DateJanuary ...
In contrast, this paper proposes a joint framework that simultaneously performs both tasks within a shared deep neural network architecture. This joint framework inte- grates the restoration and recognition tasks by incorporating: (i) common layers, (ii) restoration layers and (iii) classification ...
我们知道 GBDT 擅长处理的是稠密的数值型变量,而对稀疏的分类变量效果较差;相反,Deep Learning 擅长处理的是稀疏的分类变量,而对稠密的数值型变量效果较差。那能不能将两者的长处相结合呢?我们看看 DeepGBM 是怎么做的。 1. 模型架构 DeepGBM 模型包含两部分,GatNN 处理的是稀疏的分类变量,GBDT2NN处理的是稠密的...
This model motivates why the restoration of the video frames is structured into separate processing steps, which are implemented as deep learning models. For multi-class endoscopic artifact restoration, it is required to perform 1) frame deblurring when h(.) is unknown, i.e. a blind deblurring...
In this paper, we propose a new learning framework, DeepGBM, which integrates the advantages of the both NN and GBDT by using two corresponding NN components: (1) CatNN, focusing on handling sparse categorical features. (2) GBDT2NN, focusing on dense numerical features w...