BIG-bench Machine Learning
2318 papers with code • 1 benchmarks • 1 datasets
This branch include most common machine learning fundamental algorithms.
Benchmarks
These leaderboards are used to track progress in BIG-bench Machine Learning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find BIG-bench Machine Learning models and implementations
BIG-bench
Most implemented papers
YOLOv4: Optimal Speed and Accuracy of Object Detection
AlexeyAB/darknet
•
•
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
•
•
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
•
•
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.
Density estimation using Real NVP
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.
PennyLane: Automatic differentiation of hybrid quantum-classical computations
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
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
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of 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.
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains.
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Vishwesh4/Face-Feature-Extraction
•
•
It involves predicting the co-ordinates of the FKPs, e. g. nose tip, center of eyes, etc, for a given face.
