============================================================================================================ `MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ>`_. The PyMC developers are continuing Theano development in a `fork <https://github.com/pymc-devs/theano-pymc>`_. ============================================================================================================ To install the package, see this page: http://deeplearning.net/software/theano/install.html For the documentation, see the project website: http://deeplearning.net/software/theano/ Related Projects: https://github.com/Theano/Theano/wiki/Related-projects It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package. In order to build the documentation yourself, you will need sphinx. Issue the following command: :: python ./doc/scripts/docgen.py Documentation is built into ``html/`` The PDF of the documentation can be found at ``html/theano.pdf`` ================ DIRECTORY LAYOUT ================ ``Theano`` (current directory) is the distribution directory. * ``Theano/theano`` contains the package * ``Theano/theano`` has several submodules: * ``gof`` + ``compile`` are the core * ``scalar`` depends upon core * ``tensor`` depends upon ``scalar`` * ``sparse`` depends upon ``tensor`` * ``sandbox`` can depend on everything else * ``Theano/examples`` are copies of the example found on the wiki * ``Theano/benchmark`` and ``Theano/examples`` are in the distribution, but not in the Python package * ``Theano/bin`` contains executable scripts that are copied to the bin folder when the Python package is installed * Tests are distributed and are part of the package, i.e. fall in the appropriate submodules * ``Theano/doc`` contains files and scripts used to generate the documentation * ``Theano/html`` is where the documentation will be generated
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
Overview
Editing a classifier by rewriting its prediction rules
This repository contains the code and data for our paper: Editing a classifier by rewriting its prediction rules Shibani Santurkar*, Dimitris Tsipras*
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification
GalaXC GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification @InProceedings{Saini21, author = {Saini, D. and Jain,
AIR^2 for Interaction Prediction
This is the repository for AIR^2 for Interaction Prediction. Explanation of the solution: Video: link License AIR is released under the Apache 2.0 lic
AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning
AutoPentest-DRL: Automated Penetration Testing Using Deep Reinforcement Learning AutoPentest-DRL is an automated penetration testing framework based o
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo
QilingLab challenge writeup
qiling lab writeup shielder 在 2021/7/21 發布了 QilingLab 來幫助學習 qiling framwork 的用法,剛好最近有用到,順手解了一下並寫了一下 writeup。 前情提要 Qiling 是一款功能強大的模擬框架,和 qemu user mode
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks
TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks [Paper] [Project Website] This repository holds the source code, pretra
Official repository for ABC-GAN
ABC-GAN The work represented in this repository is the result of a 14 week semesterthesis on photo-realistic image generation using generative adversa
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"
Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment
Fewshot-face-translation-GAN - Generative adversarial networks integrating modules from FUNIT and SPADE for face-swapping.
Few-shot face translation A GAN based approach for one model to swap them all. The table below shows our priliminary face-swapping results requiring o
Train DeepLab for Semantic Image Segmentation
Train DeepLab for Semantic Image Segmentation Martin Kersner, [email protected]
An AFL implementation with UnTracer (our coverage-guided tracer)
UnTracer-AFL This repository contains an implementation of our prototype coverage-guided tracing framework UnTracer in the popular coverage-guided fuz
Objax Apache-2Objax (🥉19 · ⭐ 580) - Objax is a machine learning framework that provides an Object.. Apache-2 jax
Objax Tutorials | Install | Documentation | Philosophy This is not an officially supported Google product. Objax is an open source machine learning fr
(CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"
(CVPR 2022) TokenCut Pytorch implementation of Tokencut: Self-supervised Transformers for Unsupervised Object Discovery using Normalized Cut Yangtao W
Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs
Context-Aware-Healthcare Codes for AAAI 2022 paper: Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs Download
Object DGCNN and DETR3D, Our implementations are built on top of MMdetection3D.
Object DGCNN & DETR3D This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110
The official implementation of NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021]. https://arxiv.org/pdf/2101.12378.pdf
NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021] Release Notes The offical PyTorch implementation of NeMo, p
[ICCV'21] Neural Radiance Flow for 4D View Synthesis and Video Processing
NeRFlow [ICCV'21] Neural Radiance Flow for 4D View Synthesis and Video Processing Datasets The pouring dataset used for experiments can be download he
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'
Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs
1st place solution in CCF BDCI 2021 ULSEG challenge
1st place solution in CCF BDCI 2021 ULSEG challenge This is the source code of the 1st place solution for ultrasound image angioma segmentation task (