Month: June 2021

Analysis

Hyperbolic Dimensionality Reduction via Horospherical Projections

HoroPCA This code is the official PyTorch implementation of the ICML 2021 paper: HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsInes Chami*, Albert Gu*, Dat Nguyen*, Christopher RéStanford UniversityPaper: https://arxiv.org/abs/2106.03306 Abstract. This paper studies Principal Component Analysis (PCA) for data lying in hyperbolic spaces. Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained […]

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Graph

Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the journal Bioinformatics. DeepCDR is a hybrid graph convolutional network for cancer drug response prediction. It takes both multi-omics data of cancer cell lines and drug structure as inputs and predicts the drug sensitivity (binary or contineous IC50 value). Keras==2.1.4 TensorFlow==1.13.1 hickle >= 2.1.0 DeepCDR can be downloaded by git clone https://github.com/kimmo1019/DeepCDR Installation has been tested in […]

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Machine Learning

Flexible data structures for complex machine learning datasets

meerkat Meerkat provides fast and flexible data structures for working with complex machine learning datasets. Getting started pip install meerkat-ml Note: some parts of Meerkat rely on optional dependencies. If you know which optional dependencies you’d like to install, you can do so using something like pip install meerkat-ml[dev,text] instead. See setup.py for a full list of optional dependencies. Load your dataset into a DataPanel and get going! import meerkat as mk dp = mk.DataPanel.from_csv(“…”) What is Meerkat? Meerkat makes […]

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Machine Learning

3D Animal Reconstruction with Expectation Maximization in the Loop

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop. Install Clone the repository with submodules: git clone –recurse-submoduleshttps://github.com/benjiebob/WLDO For segmentation decoding, install pycocotools python -m pip install “git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI” Datasets To use the StanfordExtra dataset, you will need to download the .json file via the repository. Please ensure you have StanfordExtra_v12 installed, which we released 1 Feb 2021. You may also wish […]

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Video

Implementation of FitVid video prediction model in JAX/Flax

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper: @article{babaeizadeh2021fitvid, title={FitVid: Overfitting in Pixel-Level Video Prediction}, author= {Babaeizadeh, Mohammad and Saffar, Mohammad Taghi and Nair, Suraj and Levine, Sergey and Finn, Chelsea and Erhan, Dumitru}, journal={arXiv preprint arXiv:2106.13195}, year={2020} } Method FitVid is a new architecture for conditional variational video prediction. It has ~300 million parameters and can be trained with minimal training tricks. Sample […]

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Machine Learning

Complete U-net Implementation with keras in python

U Net Lowered with Keras Complete U-net Implementation with keras The model is implemented using the original paper. But I have changed the number of filters of the layers. The implemented number of layers are reduced to 25% of the original paper. Original Model Architecture : Dataset : The dataset has been taken from kaggle . It had a specific directory tree, but it was tough to execute dataset building from it, so I prepared an usable dat directory. Link […]

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Dataset

A dataset for online Arabic calligraphy

Calliar is a dataset for Arabic calligraphy. The dataset consists of 2500 json files that contain strokes manually annotated for Arabic calligraphy. This repository contains the dataset for the following paper : Calliar: An Online Handwritten Dataset for Arabic Calligraphy Zaid Alyafeai, Maged S. Al-shaibani, Mustafa Ghaleb, Yousif Ahmed Al-Wajih https://arxiv.org/abs/2106.10745 Abstract: Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy […]

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tensorflow

Attention mechanism with MNIST dataset

MNIST_AttentionMap [TensorFlow] Attention mechanism with MNIST dataset Usage $ python run.py Result Training Loss graph. Test Each figure shows input digit, attention map, and overlapped image sequentially. Further usage The further usages. Detecting the location of digits can be conducted using an attention map. Requirements TensorFlow 2.3.0 Numpy 1.18.5 GitHub https://github.com/YeongHyeon/MNIST_AttentionMap    

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Analysis

A Scanpy extension for analyzing single-cell immune-cell receptor sequencing data

Scirpy Scirpy is a scalable python-toolkit to analyse T cell receptor (TCR) or B cell receptor (BCR) repertoires from single-cell RNA sequencing (scRNA-seq) data. It seamlessly integrates with the popular scanpy library and provides various modules for data import, analysis and visualization. Getting started Please refer to the documentation. In particular, the In the documentation, you can also learn more about our immune-cell receptor model. Case-study The case study from our preprint is available here. Installation You need to have […]

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Dataset

A Naturally-Occurring Dataset Based on Stack Exchange Data

SEDE SEDE (Stack Exchange Data Explorer) is new dataset for Text-to-SQL tasks with more than 12,000 SQL queries and their natural language description. It’s based on a real usage of users from the Stack Exchange Data Explorer platform, which brings complexities and challenges never seen before in any other semantic parsing dataset like including complex nesting, dates manipulation, numeric and text manipulation, parameters, and most importantly: under-specification and hidden-assumptions. Paper (NLP4Prog workshop at ACL2021): Text-to-SQL in the Wild: A Naturally-Occurring […]

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