Deep Learning, NLP, NMT, AI, ML
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
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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 […]
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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 […]
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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 […]
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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 […]
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All experiments are done with python3.6, torch==1.5.0; torchvision==0.6.0 Data Preparation Prepare the ImageNet data in ${root_of_your_clone}/data/imagenet_train, ${root_of_your_clone}/data/imagenet_val. Since we have an internal platform(storage) to read imagenet, I have not tried the local mode. You may need to do some modification in momentum_teacher/data/dataset.py to support the local mode. Training Before training, ensure the path (namely ${root_of_clone}) is added in your PYTHONPATH, e.g. export PYTHONPATH=$PYTHONPATH:${root_of_clone} To do unsupervised pre-training of a ResNet-50 model on ImageNet in an 8-gpu machine, run: using -d […]
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An AWS Pentesting tool that lets you use one-liner commands to backdoor an AWS account’s resources with a rogue AWS account – or share the resources with the entire internet? TL;DR: endgame smash –service all to create backdoors across your entire AWS account – by sharing resources either with a rogue IAM user/role or with the entire Internet. Endgame abuses AWS’s resource permission model to grant rogue users (or the Internet) access to an AWS account’s resources with a single […]
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This repository contains the official chumpy implementation of mocap body solver used for AMASS: AMASS: Archive of Motion Capture as Surface ShapesNaureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. BlackFull paper |Video |Project website |Poster Description This repository holds the code for MoSh++, introduced in AMASS, ICCV’19.MoSh++ is the upgraded version of MoSh, Sig.Asia’2014.Given a labeled marker-based motion capture (mocap) c3d file and the correspondencesof the marker labels to the locations on the body, MoSh canreturn model […]
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This repository includes the dataset, experiments results, and code for the paper: Few-Shot Bot: Prompt-Based Learning for Dialogue Systems PDF. Authors: Andrea Madotto, Zhaojiang Lin, Genta Indra Winata, Pascale Fung Abstract Learning to converse using only a few examples is a grand challenge in Conversational AI. The current best conversational models, which are either good chit-chatters (e.g., BlenderBot) or goal-oriented systems (e.g., MinTL), are language models (LMs) fine-tuned on large conversational datasets. Training these models is expensive, both in terms […]
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