https://github.com/lbasora/bayesrul
A Benchmark on Uncertainty Quantification for Deep Learning Prognostics
Papers with Code 아카이브
Active Learning
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Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/learningmatter-mit/NeuralForceField
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
https://github.com/lightning-uq-box/lightning-uq-box
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
https://github.com/louis2889184/gnn_few_shot_cifar100
https://github.com/lucaerculiani/towards-visual-semantics
https://github.com/ludles/credanno
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis
https://github.com/lumiqai/UOI-1806.01264
https://github.com/marc-gorriz/CEAL-Medical-Image-Segmentation
https://github.com/markWJJ/Multitask-Learning
https://github.com/maziarraissi/TutorialGP
Inferring solutions of differential equations using noisy multi-fidelity data
https://github.com/meghshukla/MPII-Human-Pose-Visualization
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?
https://github.com/melkherj/puddle
https://github.com/micka-charpak/ProjetAnalyse
https://github.com/microsoft/Icebreaker
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
https://github.com/microsoft/cameratraps
A deep active learning system for species identification and counting in camera trap images
https://github.com/microsoft/intrepid
https://github.com/minfanzhang/noisy-K-FAC_added_flipout
https://github.com/minnesotanlp/dynamic-multi-reward-weighting
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information
https://github.com/mmcdermott/chexpertplusplus
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic Output
https://github.com/morl12345/dg-morl
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
https://github.com/mrgiovanni/dissertation
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
https://github.com/munanning/madav2
Multi-Anchor Active Domain Adaptation for Semantic Segmentation
https://github.com/neuropoly/axondeepseg
Deep Active Learning for Axon-Myelin Segmentation on Histology Data
https://github.com/ofnt/real_anot
https://github.com/open-starlab/trackid3x3
Enhanced Multi-Object Tracking Using Pose-based Virtual Markers in 3x3 Basketball
https://github.com/openeventdata/PLOVER
Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks
https://github.com/pdebench/pdebench
https://github.com/peymanbateni/simple-cnaps
https://github.com/pjlab-adg/3dtrans
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection
https://github.com/ptrckhmmr/DEAL
DEAL: Deep Evidential Active Learning for Image Classification
https://github.com/qarchli/pytorch-gan-for-outlier-detection
Generative Adversarial Active Learning for Unsupervised Outlier Detection
https://github.com/qijindou/CRAB
Multi-Label Bayesian Active Learning with Inter-Label Relationships
https://github.com/rapfff/jajapy
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)
https://github.com/rgreenblatt/ai_alignment_readings
https://github.com/rle-foundation/rlexplore
https://github.com/saleml/gfn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.