QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Overview

QuickAI logo

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Announcement video https://www.youtube.com/watch?v=kK46sJphjIs

Motivation

When I started to get into more advanced Machine Learning, I started to see how these famous neural network architectures(such as EfficientNet), were doing amazing things. However, when I tried to implement these architectures to problems that I wanted to solve, I realized that it was not super easy to implement and quickly experiment with these architectures. That is where QuickAI came in. It allows for easy experimentation of many model architectures quickly.

Dependencies:

Tensorflow, PyTorch, Sklearn, Matplotlib, Numpy, and Hugging Face Transformers. You should install TensorFlow and PyTorch following the instructions from their respective websites.

Why you should use QuickAI

QuickAI can reduce what would take tens of lines of code into 1-2 lines. This makes fast experimentation very easy and clean. For example, if you wanted to train EfficientNet on your own dataset, you would have to manually write the data loading, preprocessing, model definition and training code, which would be many lines of code. Whereas, with QuickAI, all of these steps happens automatically with just 1-2 lines of code.

The following models are currently supported:

  1. Image Classification

    • EfficientNet B0-B7
    • VGG16
    • VGG19
    • DenseNet121
    • DenseNet169
    • DenseNet201
    • Inception ResNet V2
    • Inception V3
    • MobileNet
    • MobileNet V2
    • MobileNet V3 Small & Large
    • ResNet 101
    • ResNet 101 V2
    • ResNet 152
    • ResNet 152 V2
    • ResNet 50
    • ResNet 50 V2
    • Xception
  2. Natural Language Processing

    • GPT-NEO 125M(Generation, Inference)
    • GPT-NEO 350M(Generation, Inference)
    • GPT-NEO 1.3B(Generation, Inference)
    • GPT-NEO 2.7B(Generation, Inference)
    • Distill BERT Cased(Q&A, Inference and Fine Tuning)
    • Distill BERT Uncased(Named Entity Recognition, Inference)
    • Distil BART (Summarization, Inference)
    • Distill BERT Uncased(Sentiment Analysis & Text/Token Classification, Inference and Fine Tuning)

Installation

pip install quickAI

How to use

Please see the examples folder for details.

Issues/Questions

If you encounter any bugs, please open a new issue so they can be corrected. If you have general questions, please use the discussion section.

Comments
  • Memory error

    Memory error

    Is it possible to host the gpt neo models on a website and make some kind of API, the models are to large to run on my computer. Also It would be nice if to have a stop function so the model knows at what token to stop and be able to add examples of the query needed.

    enhancement 
    opened by TheProtaganist 5
  • Add link to a demo

    Add link to a demo

    Hi, I tried using the notebook in the example folder but it wasn't working (I think the files were not imported into Colab), so I created a demo which should work.

    opened by equiet 1
  • Better code for image_classification.py

    Better code for image_classification.py

    Main change: Used a dict instead of excessive elifs. Other smaller changes.

    Important: I do not have the resources to test the code, but technically, it's just a rewrite of the original, so it should work.

    opened by pinjuf 1
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires numpy, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade ubuntu from 21.10 to jammy

    [Snyk] Security upgrade ubuntu from 21.10 to jammy

    This PR was automatically created by Snyk using the credentials of a real user.


    Keeping your Docker base image up-to-date means youโ€™ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:jammy, as this image has only 10 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 514 | Out-of-bounds Read
    SNYK-UBUNTU2110-E2FSPROGS-2770726 | No Known Exploit | | medium severity | 300 | NULL Pointer Dereference
    SNYK-UBUNTU2110-KRB5-1735754 | No Known Exploit | | medium severity | 300 | OS Command Injection
    SNYK-UBUNTU2110-OPENSSL-2933132 | No Known Exploit | | medium severity | 300 | Inadequate Encryption Strength
    SNYK-UBUNTU2110-OPENSSL-2941384 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by geekjr 0
  • [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    [Snyk] Security upgrade wheel from 0.30.0 to 0.38.0

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-WHEEL-3092128 | wheel:
    0.30.0 -> 0.38.0
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from rolling to 21.10

    [Snyk] Security upgrade ubuntu from rolling to 21.10

    Keeping your Docker base image up-to-date means youโ€™ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:21.10, as this image has only 12 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Issue | Exploit Maturity | | :------: | :---- | :--------------- | | medium severity | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU2110-PERL-1930909 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU2110-SHADOW-1758374 | No Known Exploit | | low severity | NULL Pointer Dereference
    SNYK-UBUNTU2110-TAR-1744334 | No Known Exploit | | medium severity | CVE-2018-25032
    SNYK-UBUNTU2110-ZLIB-2433596 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade ubuntu from 18.04 to rolling

    [Snyk] Security upgrade ubuntu from 18.04 to rolling

    Keeping your Docker base image up-to-date means youโ€™ll benefit from security fixes in the latest version of your chosen image.

    Changes included in this PR

    • Dockerfile

    We recommend upgrading to ubuntu:rolling, as this image has only 13 known vulnerabilities. To do this, merge this pull request, then verify your application still works as expected.

    Some of the most important vulnerabilities in your base image include:

    | Severity | Priority Score / 1000 | Issue | Exploit Maturity | | :------: | :-------------------- | :---- | :--------------- | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Information Exposure
    SNYK-UBUNTU1804-GCC8-572149 | No Known Exploit | | medium severity | 300 | Improper Verification of Cryptographic Signature
    SNYK-UBUNTU1804-PERL-1930908 | No Known Exploit | | low severity | 150 | Time-of-check Time-of-use (TOCTOU)
    SNYK-UBUNTU1804-SHADOW-306209 | No Known Exploit |


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 471/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321966 | numpy:
    1.19.5 -> 1.22.0
    | No | No Known Exploit low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    [Snyk] Security upgrade numpy from 1.19.5 to 1.22.0rc1

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Buffer Overflow
    SNYK-PYTHON-NUMPY-2321969 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept low severity | 578/1000
    Why? Proof of Concept exploit, Recently disclosed, Has a fix available, CVSS 3.7 | Denial of Service (DoS)
    SNYK-PYTHON-NUMPY-2321970 | numpy:
    1.19.5 -> 1.22.0rc1
    | No | Proof of Concept

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the effected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic

    opened by snyk-bot 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.1 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 551/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.3 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3180412 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    [Snyk] Security upgrade setuptools from 39.0.1 to 65.5.1

    This PR was automatically created by Snyk using the credentials of a real user.


    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires numpy, which is not installed.
    torchvision 0.5.0 requires pillow, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- low severity | 441/1000
    Why? Recently disclosed, Has a fix available, CVSS 3.1 | Regular Expression Denial of Service (ReDoS)
    SNYK-PYTHON-SETUPTOOLS-3113904 | setuptools:
    39.0.1 -> 65.5.1
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Regular Expression Denial of Service (ReDoS)

    opened by geekjr 0
  • [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    [Snyk] Security upgrade protobuf from 3.20.1 to 3.20.2

    Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

    Changes included in this PR

    • Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
      • requirements.txt
    โš ๏ธ Warning
    torchvision 0.5.0 requires pillow, which is not installed.
    sympy 1.5.1 requires mpmath, which is not installed.
    coremltools 6.0 requires protobuf, which is not installed.
    
    

    Vulnerabilities that will be fixed

    By pinning:

    Severity | Priority Score (*) | Issue | Upgrade | Breaking Change | Exploit Maturity :-------------------------:|-------------------------|:-------------------------|:-------------------------|:-------------------------|:------------------------- medium severity | 571/1000
    Why? Recently disclosed, Has a fix available, CVSS 5.7 | Denial of Service (DoS)
    SNYK-PYTHON-PROTOBUF-3031740 | protobuf:
    3.20.1 -> 3.20.2
    | No | No Known Exploit

    (*) Note that the real score may have changed since the PR was raised.

    Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

    Check the changes in this PR to ensure they won't cause issues with your project.


    Note: You are seeing this because you or someone else with access to this repository has authorized Snyk to open fix PRs.

    For more information: ๐Ÿง View latest project report

    ๐Ÿ›  Adjust project settings

    ๐Ÿ“š Read more about Snyk's upgrade and patch logic


    Learn how to fix vulnerabilities with free interactive lessons:

    ๐Ÿฆ‰ Learn about vulnerability in an interactive lesson of Snyk Learn.

    opened by snyk-bot 0
Releases(2.0.0)
3D-Transformer: Molecular Representation with Transformer in 3D Space

3D-Transformer: Molecular Representation with Transformer in 3D Space

55 Dec 19, 2022
Bringing sanity to world of messed-up data

Sanitize sanitize is a Python module for making sure various things (e.g. HTML) are safe to use. It was originally written by Mark Pilgrim and is dist

Alireza Savand 63 Oct 26, 2021
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at .

PixelNet: Representation of the pixels, by the pixels, and for the pixels. We explore design principles for general pixel-level prediction problems, f

Aayush Bansal 196 Aug 10, 2022
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Adversarial Texture Optimization from RGB-D Scans (CVPR 2020).

AdversarialTexture Adversarial Texture Optimization from RGB-D Scans (CVPR 2020). Scanning Data Download Please refer to data directory for details. B

Jingwei Huang 153 Nov 28, 2022
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 03, 2023
"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021

undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f

Yichen Jiang 0 Mar 25, 2022
TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.

TalkNet 2 [WIP] TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Predictio

Rishikesh (เค‹เคทเคฟเค•เฅ‡เคถ) 69 Dec 17, 2022
Official implementation of "A Unified Objective for Novel Class Discovery", ICCV2021 (Oral)

A Unified Objective for Novel Class Discovery This is the official repository for the paper: A Unified Objective for Novel Class Discovery Enrico Fini

Enrico Fini 118 Dec 26, 2022
Coursera - Quiz & Assignment of Coursera

Coursera Assignments This repository is aimed to help Coursera learners who have difficulties in their learning process. The quiz and programming home

ๆต…ๆขฆ 828 Jan 04, 2023
FLVIS: Feedback Loop Based Visual Initial SLAM

FLVIS Feedback Loop Based Visual Inertial SLAM 1-Video EuRoC DataSet MH_05 Handheld Test in Lab FlVIS on UAV Platform 2-Relevent Publication: Under Re

UAV Lab - HKPolyU 182 Dec 04, 2022
K-Nearest Neighbor in Pytorch

Pytorch KNN CUDA 2019/11/02 This repository will no longer be maintained as pytorch supports sort() and kthvalue on tensors. git clone https://github.

Chris Choy 65 Dec 01, 2022
Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2

CoaDTI Multi-modal co-attention for drug-target interaction annotation and Its Application to SARS-CoV-2 Abstract Environment The test was conducted i

Layne_Huang 7 Nov 14, 2022
DGN pymarl - Implementation of DGN on Pymarl, which could be trained by VDN or QMIX

This is the implementation of DGN on Pymarl, which could be trained by VDN or QM

4 Nov 23, 2022
Code for EMNLP2021 paper "Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training"

VoCapXLM Code for EMNLP2021 paper Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training Environment DockerFile: dancingso

Bo Zheng 15 Jul 28, 2022
โš“ Eurybia monitor model drift over time and securize model deployment with data validation

View Demo ยท Documentation ยท Medium article ๐Ÿ” Overview Eurybia is a Python library which aims to help in : Detecting data drift and model drift Valida

MAIF 172 Dec 27, 2022
Learning Off-Policy with Online Planning, CoRL 2021

LOOP: Learning Off-Policy with Online Planning Accepted in Conference of Robot Learning (CoRL) 2021. Harshit Sikchi, Wenxuan Zhou, David Held Paper In

Harshit Sikchi 24 Nov 22, 2022
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Website | Documentation | Tutorials | Installation | Release Notes CatBoost is a machine learning method based on gradient boosting over decision tree

CatBoost 6.9k Jan 04, 2023
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
The official implementation of the IEEE S&P`22 paper "SoK: How Robust is Deep Neural Network Image Classification Watermarking".

Watermark-Robustness-Toolbox - Official PyTorch Implementation This repository contains the official PyTorch implementation of the following paper to

49 Dec 19, 2022