Deep Learning, NLP, NMT, AI, ML
Unofficial PyTorch implementation of SimCLR by Google Brain [abs, pdf] Installation You can install the package via pip: pip install simclr_pytorch Usage You can use the SimCLR model like so: from simclr_pytorch import SimCLR train_loader = … bigcnn = ResNet50(…) simclr = SimCLR( image_size=(32, 32), bigcnn=bigcnn ) trained_bigcnn = simclr.fit(train_loader)
Read more
arXiv | BibTeX | Project Page This repo contains code for single GPU training of LayoutTransformer from LayoutTransformer: Layout Generation and Completion with Self-attention. This code was rewritten from scratch using a cleaner GPT codebase. Some of the details such as training hyperparameters might differ from the arxiv version of the paper. How To Use This Code Start a new conda environment conda env create -f environment.yml conda activate layout or update an existing environment conda env
Read more
How to contribute and current situation (9/11/2021~) I have been working as a full-time AI engineer and barely have free time to manage this project any more. I want to make a simple guidance of how to response to contributions: For exercises that have no answer yet, (for example, chapter 12) Prepare your latex code, make sure it works and looks somewhat nice. Send you code to [email protected]. By default, I will put contributer’s name in the pdf file, besides […]
Read more
Aiohttp admin Demo site | Demo source code. The aiohttp admin is a library for build admin interface for applications based on the aiohttp. With this library you can ease to generate CRUD views for your data (for data storages which support by aiohttp admin) and flexibly customize representation and access to these. Installation The first step which you need to do it’s installing library pip install aiohttp_admin2
Read more
jfc is an utility to make reviewing ArXiv papers for your Journal Club easier. Install Consider using pipx. pipx install git+https://github.com/mikeevmm/jfc/ Otherwise, if you are using pip: pip install git+https://github.com/mikeevmm/jfc/ How to Use jfc aggregates unseen articles from the specified timeframe (see the configuration section), and displays them to you in an interactive prompt. From the title, you can choose to read the abstract, and from there you can choose to open the ArXiv PDF. Run jfc to get an […]
Read moreVersion 0.2.0 A markdown lexer and parser which gives the developer atomic control over markdown parsing to html. Atomd first tokenizes the markdown with the lexer. Using the tokenized markdown, parsers for various different markup languages (HTML, XML, etc) are easier to implement, since it more or less becomes a game of find and replace. Install python3 -m pip install atomd Usage Functions in atomd.py called md2 ()combine the lexer and parser to provide an easier calling convention. In the […]
Read more
Abstract Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learning, via simply incorporating relation prediction into the commonly used 1vsAll objective. The new training objective contains not only terms for predicting the subject and object of a given triple, but also a term for predicting the relation type. We analyse how this new objective impacts multi-relational learning in KBC: experiments on […]
Read more
A docker container using restful endpoints exposed on port 5000 “/analyze” to gather sentiment analysis on the wsb subreddit. Getting Started These instructions will cover usage information and for the docker container Prerequisities In order to run this container you’ll need docker installed. Usage Container Startup List the different parameters available to your container docker run -p 5000:5000 -e CLIENT_ID= -e CLIENT_SECRET= -e USERNAME= -e PASSWORD= restfulleo/wallstreetbets-sentiment-analysis Environment Variables CLIENT_ID – A client_id from valid reddit account with a developer […]
Read more
Introduction In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss functionis utilized to learn to produce pixel-wise feature embeddings such that pixels from the same object are close to each other and pixels from different objects are separated in the embedding space. With the learned feature embeddings, a mean shift clustering algorithm can be applied to discover and segment unseen objects. We further improve […]
Read more