33 posts in this category
Motivation Sometimes we want to examine the Hessian or Jacobian of a function w.r.t some variables. For that purpose, autogradient algorithm can help us. Autograd mechanism In Essence, Autograd requires a computational graph. (Directed Acyclic Graph) For each computational node (e.g. $z=f(x,y)$), we define a forward computation $(x,y)\mapsto z,\ z=f(x,y)$ mapping bottom to top, and a backward computation mapping the partial derivative to top to the partial derivative to bottom. $\partial_z\mapsto (\partial_x,\partial_y); (gx,gy)=g(gz;x,y)$ .
Dec 9, 2019
Installation Official note on installation https://caffe.berkeleyvision.org/installation.html Installing CPU version on CHPC Install Miniconda Install caffe using condaconda install -c intel caffe lsb_release -d Description: CentOS release 6.10 (Final) Building GPU version on CHPC (Not succeeded yet…. aborted)
Oct 27, 2019
Note on CNN Interpretability 2 major way of interpreting CNN Feature visualization: See what a hidden neuron is interested in Attribution: See what part of image activate a filter or detector Activation Atlas These works try to find a tool kits for visualizing DeepNN and building up a human-computer interface of DeepNN.
Oct 4, 2019
Note on Feature Visualization Motivation We want to understand what the hidden units “represent” What are they tuned to? What’s the favorite stimuli? Why should we find the most excitable stimuli? Resources DeepDream.ipynb Tensorflow
Sep 17, 2019
Deep Unsupervised Learning Lecture notes from Berkeley-cs294 https://sites.google.com/view/berkeley-cs294-158-sp19/home Lec 1 Category Generative Model Non-generative representation learning Motivation for Unsupervised Learning Application Generate/predict fancy samples Detect Anomaly / deviation from distribution Which human can do quite well without training Data Compression (because of predictability) Use the inner representation to do other tasks! Pre-training Type of Question Core Question: Modeling a Distribution
Sep 13, 2019
Using Google Cloud Service for Large Scale Image Labelling Installing Google SDK https://cloud.google.com/sdk/docs/quickstart-windows New a Google Cloud Platform Project Download Google Cloud SDK After installation run gcloud init and log in to your account there! Select the GCP Project and the computing zone Finish the SDK configuration! Installing Google API for different programs (like Vision we use) https://cloud.google.com/python/
Sep 11, 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
DeepLabCut Trouble Shooting @(Ponce Lab) TOC {:toc} Install DLC Windows machine, follow the steps in install tutorial to establish the whole conda environment in the machine. Fail at first step Many of us just fail at first step, some error message like
Jul 2, 2019
Motivation Although there are a millennium of methods for neural and behavioral signal recording, the questions asked about the neural data is ususally less diverse. Ultimately, everything is number and we process numbers with algorithm.
Jul 1, 2019
Note on Automatic 3D Instance Segmentation Pipeline In this note I try to summarize several recent works on Automatic 3D Instance Segmentation, with most direct application to saturated reconstruction of neural morphology in an imaging volume (mostly scanning Electral Microscopy, but seems it can be generalized into other imaging modality), which is one of the most important method of high-throughput connectomics1.
Dec 9, 2018