PyTorch

Language Emergence in Multi Agent Dialog

Language Emergence in Multi Agent Dialog Code for the Paper Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra EMNLP 2017 (Best Short Paper) If you find this code useful, please consider citing the original work by authors: @inproceedings{visdial, title = {{N}atural {L}anguage {D}oes {N}ot {E}merge ‘{N}aturally’ in {M}ulti-{A}gent {D}ialog}, author = {Satwik Kottur and Jos’e M.F. Moura and Stefan Lee and Dhruv Batra}, journal = {CoRR}, volume = {abs/1706.08502}, […]

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Deep Learning

Rainbow: Combining Improvements in Deep Reinforcement Learning

Rainbow Rainbow: Combining Improvements in Deep Reinforcement Learning [1]. Results and pretrained models can be found in the releases. [x] DQN [2] [x] Double DQN [3] [x] Prioritised Experience Replay [4] [x] Dueling Network Architecture [5] [x] Multi-step Returns [6] [x] Distributional RL [7] [x] Noisy Nets [8] Run the original Rainbow with the default arguments: python main.py Data-efficient Rainbow [9] can be run using the following options (note that the “unbounded” memory is implemented here in practice by manually […]

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Tool

Python tool to Check running WebClient services on multiple targets

WebClient Service Scanner Python tool to Check running WebClient services on multiple targets based on @tifkin_ idea. This tool uses impacket project. Usage webclientservicescanner hackn.lab/user:[email protected]/24 Provided credentials will be tested against a domain controller before scanning so that a typo in the domain/username/password won’t lock out the account. If you want to bypass this check, just use -no-validation flag. Exploitation Green entries mean that WebDav client is active on remote host. Using PetitPotam or PrinterBug, an HTTP authentication can be […]

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Deep Learning

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

SPPR Self-Promoted Prototype Refinement for Few-Shot Class-Incremental LearningThis is the implementation of the paper “Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning” (accepted to CVPR2021). Requirements Python 3.8 PyTorch 1.8.1 (>1.1.0) cuda 11.2 Preparing Few-Shot Class-Incremental Learning Datasets Download following datasets: 1. CIFAR-100 Automatically downloaded on torchvision. 2. MiniImageNet (1) Download MiniImageNet train/test images[github],and prepare related datasets according to [TOPIC]. (2) or Download processed data from our Google Drive: [mini-imagenet.zip],(and locate the entire folder under datasets/ directory). 3. CUB200 (1) Download […]

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Transformer

A library for finding knowledge neurons in pretrained transformer models

knowledge-neurons An open source repository replicating the 2021 paper Knowledge Neurons in Pretrained Transformers by Dai et al., and extending the technique to autoregressive models, as well as MLMs. The Huggingface Transformers library is used as the backend, so any model you want to probe must be implemented there. Currently integrated models: BERT_MODELS = [“bert-base-uncased”, “bert-base-multilingual-uncased”] GPT2_MODELS = [“gpt2”] GPT_NEO_MODELS = [ “EleutherAI/gpt-neo-125M”, “EleutherAI/gpt-neo-1.3B”, “EleutherAI/gpt-neo-2.7B”, ] The technique from Dai et al. has been used to locate knowledge neurons in […]

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RESTful API

Boilerplate code for quick docker implementation of REST API

Boilerplate code for quick docker implementation of REST API with JWT Authentication using FastAPI, PostgreSQL and PgAdmin. Getting Started Features Auth Description Login Access Token OAuth2 compatible token login, get an access token for future requests Check Session Test if a user is logged in by checking if a valid access token is in the header Recover Password Password Recovery Reset Password Reset your password User Description Create New User Create a new user Get Current User By Id Get […]

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Database

A Relational Database Management System for a miniature version of Twitter written in python

Mini-Twitter-Database This was done as a database design course project at Amirkabir university of technology. This is a relational database management system that is built using MySQL and works with CLI in python. This project contains the following files: tables.sql: A DDL script to generate relational tables for our mini-twitter schema including relevant constraints. procedures.sql: Several procedures to perform complex queries. triggers.sql: Some triggers that are sensitive to a particular event and perform specific tasks as they occur. Relational_schema.png: Relational […]

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Dataset

A work-in-progress vector version of the MNIST dataset

bezier-mnist This is a work-in-progress vector version of the MNIST dataset. Here are some samples from the training set. Note that, while these are rasterized, the underlying images can be rendered at any resolution because they are smooth vector graphics. ![A grid of sixteen digit images](https://github.com/unixpickle/bezier-mnist/raw/main/samples.png =300×300) I have already converted all of MNIST to Bezier curves. This dataset can be downloaded at this page. There are two files: train.zip and test.zip, each containing a separate json file for each […]

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Django

Pydantic models for Django

Djantic Documentation: https://jordaneremieff.github.io/djantic/ Requirements: Python 3.7+, Django 3.0+ Pydantic models for Django. This project should be considered a work-in-progress. It should be okay to use, but no specific version support has been determined (#16) and the default model generation behaviour may change across releases. Please use the issues tracker to report any bugs, or if something seems incorrect. Quickstart Install using pip: pip install djantic Generating schemas from models Configure a custom ModelSchema class for a Django model to generate […]

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Asyncio

An Asynchronous Python object-document mapper for MongoDB

beanie Beanie – is an Asynchronous Python object-document mapper (ODM) for MongoDB, based on Motor and Pydantic. When using Beanie each database collection has a corresponding Document that is used to interact with that collection. In addition to retrieving data, Beanie allows you to add, update, or delete documents from the collection as well. Beanie saves you time by removing boiler-plate code and it helps you focus on the parts of your app that actually matter. Data and schema migrations […]

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