TOC {:toc} Deep Learning Environment Currently we find that multiple version of CUDA could be installed on windows. And different frameworks could use different CUDA version nicely together. PyTorch Tensorflow Co-environment Currently, we can have
Dec 19, 2019
Objective Here I want to compare several common deep learning frameworks and make sense of their workflow. Core Logic Tensorflow General Comments: TF is more like a library, in which many low-level operations are defined and programs are long. In contrast, Keras which can use tensorflow as backend has the similar level of abstraction as PyTorch, which is a higher level deep learning package. TFLearn may also be a higher level wrapper.
Dec 18, 2019
Informative Fragment Approach to Object Recognition It’s intuitive that some basic features in the image of objects are informative to the category of the object. Thus, even for occluded images, the revealed fragments can also provide such information, so that we could recognize the object from few patches.
Sep 25, 2019
TOC {:toc} Objective Build the software environment for Scientific Computing Data Analysis and Deep Learning for a GPU enabled Linux work station. This post majorly summarizes the tools and references for building up a Linux Working Environment. I’ll update the errors and trouble shooting notes as I encounter them.
Jul 25, 2019
看得见还是看不见, 这是一个问题! 如果问你, 你现在看到了什么, 你可以不假思索地说, 就是眼前的屏幕嘛. 如果稍微受过一点生理学或者神经科学训练的人也许会说, 我们看到的是这个充满电磁波的世界里, 光学波段辐射被眼球的光学元件折射之后, 落在我们视网膜上的影像. 然而我们不总是能看见落到我们视网膜上的东西. 稍加反思, 我们都会想到自己夏日午后看着窗外出神的时候, 其实并没有看见窗外有什么景色；对着讲台发呆的时候也没有看到黑板上的字是什么；对着击球手飞速击出的棒球同样略过了视网膜, 但是否产生了知觉只有他知道. 简而言之, 就像英语中see（看到）与look（看的动作）不同, 我们看到的东西绝不是落在我们视网膜上的光子所携带的所有信息1——你看不见不可见光也看不到偏振. 那么是什么决定了我们能看到什么以及看到的世界是什么样子呢? 我们可以拍脑门的说, 当我们用心去看就能看到(比如黑板或者窗外), 如果不用心, 那刺激就被我忽视(Neglect)了嘛——的确没错, 这就将我们引导到了视觉注意(Visual Attention)的领地. 不过这篇post中我们先不讨论注意的问题, 而要去关注一些与视觉意识相关的更基本的情况. 什么时候我们看不到 (When we do not see) 首先, 我们将自然环境以及落在视网膜上的光子一概称为物理刺激(Physical stimuli), 我们感觉到的那个世界称为主观感受(Perception)2. 那么刺激与感受不完全匹配的情况其实比比皆是, 下面举几个例子：
Jul 2, 2019
Note on Computation by Biological Plausible Learning from lecture of Cengiz Penleven 2019 Philosophy Neural dynamics can be a substrate of computation. The neural dynamics and plasticity dynamics can both do optimization, and the biological constraint form a source of constraint on variables.
Jun 24, 2019