BIG-bench Machine Learning
2205 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in BIG-bench Machine Learning
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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
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There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
lmcinnes/umap
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UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
zalandoresearch/fashion-mnist
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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.
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
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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
joehoeller/Computer-Vision-Facial-Key-Point-Detection
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It involves predicting the co-ordinates of the FKPs, e. g. nose tip, center of eyes, etc, for a given face.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
ndrplz/ConvLSTM_pytorch
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NeurIPS 2015
The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time.
