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
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Libraries
Use these libraries to find BIG-bench Machine Learning models and implementations
BIG-bench
Most implemented papers
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.
Practical Black-Box Attacks against Machine Learning
Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN.
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
Self-Normalizing Neural Networks
bioinf-jku/SNNs
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NeurIPS 2017
We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations.
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
ptschandl/HAM10000_dataset
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Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images.
Membership Inference Attacks against Machine Learning Models
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained.
Model Cards for Model Reporting
Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.
Challenges in Representation Learning: A report on three machine learning contests
phamquiluan/ResidualMaskingNetwork
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The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Multitask learning and benchmarking with clinical time series data
yerevann/mimic3-benchmarks
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Health care is one of the most exciting frontiers in data mining and machine learning.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
