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BIG-bench Machine Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 0
평가표: 2
보존된 평가 기록: 2

This branch include most common machine learning fundamental algorithms.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/clvcooke/virtual-fluorescence

공식 코드

Physics-enhanced machine learning for virtual fluorescence microscopy

https://github.com/cmhcbb/attackbox

공식 코드

Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach

https://github.com/cmpute/dgal

공식 코드

Differentiable Computational Geometry for 2D and 3D machine learning

https://github.com/cmu-mars/model-learner

공식 코드

Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

https://github.com/cmudig/emblaze

공식 코드

Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces

https://github.com/cnewbie007/DADG

공식 코드

Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

https://github.com/cnguyen10/few_shot_meta_learning

공식 코드

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

https://github.com/coarse-graining/cgnet

공식 코드

Coarse Graining Molecular Dynamics with Graph Neural Networks

https://github.com/cobermai/rfstudies

공식 코드

Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities

https://github.com/cog-imperial/omlt

공식 코드

OMLT: Optimization & Machine Learning Toolkit

https://github.com/coleygroup/polymer-chemprop-data

공식 코드

A graph representation of molecular ensembles for polymer property prediction

https://github.com/complex-systems-lab/Project_Chimera_ML

공식 코드

Identification of Chimera using Machine Learning

https://github.com/compstat-lmu/code_pitfalls_iml

공식 코드

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

https://github.com/compstat-lmu/paper_2019_iml_measures

공식 코드

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

https://github.com/compstat-lmu/paper_2019_multiobjective_rfms

공식 코드

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

https://github.com/compstat-lmu/paper_2021_categorical_feature_encodings

공식 코드

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

https://github.com/computationalprivacy/ml-correlation-inference

공식 코드

Correlation inference attacks against machine learning models

https://github.com/comsys/ml-iids-generalizability

공식 코드

A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection

https://github.com/congzlwag/UnsupGenModbyMPS

공식 코드

Unsupervised Generative Modeling Using Matrix Product States

https://github.com/conlain-k/RLN_elasticity

공식 코드

Recurrent Localization Networks applied to the Lippmann-Schwinger Equation

https://github.com/cooliotonyio/predicting-media-memorability

공식 코드

Multi-modal Ensemble Models for Predicting Video Memorability

https://github.com/corail-research/SeaPearl.jl

공식 코드

SeaPearl: A Constraint Programming Solver guided by Reinforcement Learning

https://github.com/cory-b-scott/diff_ifs

공식 코드

Differentiable Iterated Function Systems

https://github.com/csieber/hasbrain

공식 코드

Towards Machine Learning-Based Optimal HAS

https://github.com/csinva/imodels

공식 코드

Interpretable machine learning: definitions, methods, and applications

https://github.com/csong27/membership-inference

공식 코드

Membership Inference Attacks against Machine Learning Models

https://github.com/csong27/ml-model-remember

공식 코드

Machine Learning Models that Remember Too Much

https://github.com/csyanbin/LSMI-Sinkhorn

공식 코드

LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport

https://github.com/cuihenggang/geeps

공식 코드

MLtuner: System Support for Automatic Machine Learning Tuning

https://github.com/cvillacampa/GPInputNoise

공식 코드

Multi-class Gaussian Process Classification with Noisy Inputs

https://github.com/cvvletter/palsy

공식 코드

Towards an Automatic Diagnosis of Peripheral and Central Palsy Using Machine Learning on Facial Features

https://github.com/czarmanu/deeplab-lakeice-webcams

공식 코드

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

https://github.com/czarmanu/photi-lakeice-dataset

공식 코드

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning

https://github.com/czs108/microsoft-malware-classification

공식 코드

Malware Classification Using Static Disassembly and Machine Learning

https://github.com/d909b/cxplain

공식 코드

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

https://github.com/dachosen1/User-activity-anomaly-detection-by-mouse-movements-in-web-surveys

공식 코드

Validating psychometric survey responses

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