Papers with Code 아카이브

BIG-bench Machine Learning

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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/master/tensorflow-manopt

공식 코드

TensorFlow RiemOpt: a library for optimization on Riemannian manifolds

https://github.com/mathisgerdes/continuous-flow-lft

공식 코드

Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

https://github.com/mathoup31/simhawnet

공식 코드

SimHawNet: A Modified Hawkes Process for Temporal Network Simulation

https://github.com/matonima/lightweight-classifier/blob/main/test_model_2.py

공식 코드

Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs

https://github.com/matthewvowels1/ml_structural_interactions

공식 코드

Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables

https://github.com/mattswatson/attack-agnostic-adversarial-attack-detection

공식 코드

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

https://github.com/mattwear21/learningfromthepros

공식 코드

Learning from the Pros: Extracting Professional Goalkeeper Technique from Broadcast Footage

https://github.com/maximilianpi/pichler-and-hartig-2022

공식 코드

Machine Learning and Deep Learning -- A review for Ecologists

https://github.com/maziarraissi/HPM

공식 코드

Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

https://github.com/mblondel/lightning

공식 코드

API design for machine learning software: experiences from the scikit-learn project

https://github.com/mc-robinson/validating_validation_supp_info

공식 코드

Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction

https://github.com/mdabashar/Propensity-to-Pay

공식 코드

Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty

https://github.com/mdppml/RKN-as-a-Service

공식 코드

CECILIA: Comprehensive Secure Machine Learning Framework

https://github.com/mebrunet/understanding-bias

공식 코드

Understanding the Origins of Bias in Word Embeddings

https://github.com/medspacy/medspacy

공식 코드

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python

https://github.com/meelgroup/manthan

공식 코드

Manthan: A Data Driven Approach for Boolean Function Synthesis

https://github.com/meelgroup/mlic

공식 코드

MLIC: A MaxSAT-Based framework for learning interpretable classification rules

https://github.com/megvii-research/neurips2021-ml4co-kida

공식 코드

ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation

https://github.com/melloddy/sparsechem

공식 코드

SparseChem: Fast and accurate machine learning model for small molecules

https://github.com/menggedu/autoke

공식 코드

AutoKE: An automatic knowledge embedding framework for scientific machine learning

https://github.com/mesosbrodleto/playerank

공식 코드

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

https://github.com/mhdslh/Federated-Learning-in-Unreliable-and-Resource-Constrained-Cellular-Wireless-Networks

공식 코드

Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks

https://github.com/micahjsmith/ml-bazaar-2019

공식 코드

The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development

https://github.com/michael-goebel/ei_red

공식 코드

Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions

https://github.com/michaelgutmann/ml-pen-and-paper-exercises

공식 코드

Pen and Paper Exercises in Machine Learning

https://github.com/michaeltrs/DeepSatData

공식 코드

DeepSatData: Building large scale datasets of satellite images for training machine learning models

https://github.com/microsoft/CodeXGLUE

공식 코드

CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

https://github.com/microsoft/graspologic

공식 코드

GraSPy: Graph Statistics in Python

https://github.com/microsoft/hummingbird

공식 코드

A Tensor Compiler for Unified Machine Learning Prediction Serving

https://github.com/microsoft/interpret

공식 코드

InterpretML: A Unified Framework for Machine Learning Interpretability

https://github.com/miffyli/gan-aimbots

공식 코드

GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters

https://github.com/miguel-rg/dynamical-loss-functions

공식 코드

Tilting the playing field: Dynamical loss functions for machine learning

https://github.com/mihaioltean/traceless-genetic-programming

공식 코드

New Evolutionary Computation Models and their Applications to Machine Learning

https://github.com/mikahama/natas

공식 코드

From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-Correction

https://github.com/mil-tokyo/hyperbolic_nn_plusplus

공식 코드

Hyperbolic Neural Networks++

https://github.com/mims-harvard/TDC

공식 코드

Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.