regression
1882 papers with code • 0 benchmarks • 9 datasets
Benchmarks
These leaderboards are used to track progress in regression
Libraries
Use these libraries to find regression models and implementationsDatasets
FLIP
SciRepEval
TTE-A&O
xView3-SAR
MineralImage5k
Meta Omnium
Satlas
Brightfield vs Fluorescent Staining Dataset
Most implemented papers
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
cbfinn/maml
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ICML 2017
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
Implicit Quantile Networks for Distributional Reinforcement Learning
In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Zzh-tju/DIoU
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By incorporating DIoU and CIoU losses into state-of-the-art object detection algorithms, e. g., YOLO v3, SSD and Faster RCNN, we achieve notable performance gains in terms of not only IoU metric but also GIoU metric.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100, 000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage.
Distributional Reinforcement Learning with Quantile Regression
In this paper, we build on recent work advocating a distributional approach to reinforcement learning in which the distribution over returns is modeled explicitly instead of only estimating the mean.
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.
Bayesian regression and Bitcoin
In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency.
Tensor Regression
tensorly/torch
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Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data.
