1882 papers with code • 0 benchmarks • 9 datasets

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Use these libraries to find regression models and implementations

Most implemented papers

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

cbfinn/maml 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

tensorflow/models 20 May 2015

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

yaringal/DropoutUncertaintyExps 6 Jun 2015

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

google/dopamine ICML 2018

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 19 Nov 2019

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

worksheets/0xd53d03a4 EMNLP 2016

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

DLR-RM/stable-baselines3 27 Oct 2017

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

yaringal/multi-task-learning-example CVPR 2018

Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives.

Bayesian regression and Bitcoin

panditanvita/BTCpredictor 6 Oct 2014

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 22 Aug 2023

Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data.