BIG-bench Machine Learning

2323 papers with code • 1 benchmarks • 1 datasets

This branch include most common machine learning fundamental algorithms.


Use these libraries to find BIG-bench Machine Learning models and implementations


Most implemented papers

YOLOv4: Optimal Speed and Accuracy of Object Detection

AlexeyAB/darknet 23 Apr 2020

There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.

Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

zalandoresearch/fashion-mnist 25 Aug 2017

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

lmcinnes/umap 9 Feb 2018

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.

Density estimation using Real NVP

tensorflow/models 27 May 2016

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLaneAI/pennylane 12 Nov 2018

PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.

XGBoost: A Scalable Tree Boosting System

dmlc/xgboost 9 Mar 2016

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

Deep Learning with Differential Privacy

tensorflow/models 1 Jul 2016

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.

Datasheets for Datasets

williamgilpin/dysts 23 Mar 2018

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains.

Noise2Noise: Learning Image Restoration without Clean Data

NVlabs/noise2noise ICML 2018

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

Vishwesh4/Face-Feature-Extraction 3 Oct 2017

It involves predicting the co-ordinates of the FKPs, e. g. nose tip, center of eyes, etc, for a given face.