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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/gcorso/torsional-diffusion

공식 코드

Torsional Diffusion for Molecular Conformer Generation

https://github.com/gdkent/bsg_methods_con_unc

공식 코드

Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems

https://github.com/gemeinl/auto-eeg-diagnosis-comparison

공식 코드

Machine-Learning-Based Diagnostics of EEG Pathology

https://github.com/geomdata/gda-public

공식 코드

Supervised Learning of Labeled Pointcloud Differences via Cover-Tree Entropy Reduction

https://github.com/geomstats/geomstats

공식 코드

geomstats: a Python Package for Riemannian Geometry in Machine Learning

https://github.com/georgestein/ml-in-cosmology

공식 코드

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

https://github.com/gianlucatruda/quantified-sleep

공식 코드

Quantified Sleep: Machine learning techniques for observational n-of-1 studies

https://github.com/gianluigilopardo/smace

공식 코드

SMACE: A New Method for the Interpretability of Composite Decision Systems

https://github.com/giasemidis/basketball-data-analysis

공식 코드

Descriptive and Predictive Analysis of Euroleague Basketball Games and the Wisdom of Basketball Crowds

https://github.com/gieseke/speedynn

공식 코드

Sacrificing information for the greater good: how to select photometric bands for optimal accuracy

https://github.com/gifford-lab/diminishingreturns

공식 코드

Constrained Submodular Optimization for Vaccine Design

https://github.com/gingsmith/fmtl

공식 코드

Federated Multi-Task Learning

https://github.com/giorgiovisani/lime_stability

공식 코드

Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models

https://github.com/giotto-ai/giotto-deep

공식 코드

Persformer: A Transformer Architecture for Topological Machine Learning

https://github.com/giotto-ai/giotto-tda

공식 코드

giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

https://github.com/giovannicina/selecting_ood_detector

공식 코드

Out-of-Distribution Detection for Medical Applications: Guidelines for Practical Evaluation

https://github.com/globus-labs/covid-analyses

공식 코드

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

https://github.com/globus-labs/g4mp2-atomization-energy

공식 코드

Machine Learning Prediction of Accurate Atomization Energies of Organic Molecules from Low-Fidelity Quantum Chemical Calculations

https://github.com/gmatilde/vdrnn

공식 코드

Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings

https://github.com/gnina/libmolgrid

공식 코드

libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications

https://github.com/gomes-lab/H-CLMP

공식 코드

Materials Representation and Transfer Learning for Multi-Property Prediction

https://github.com/goncalorafaria/learning-inner-consensus

공식 코드

Learning to compute inner consensus: A novel approach to modeling agreement between Capsules

https://github.com/gondimribeiro/adv-attacks-vae

공식 코드

Adversarial Attacks on Variational Autoencoders

https://github.com/goodfeli/forgetting

공식 코드

An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks

https://github.com/google-research-datasets/wit

공식 코드

WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning

https://github.com/google-research/google-research

공식 코드

Practical and Consistent Estimation of f-Divergences

https://github.com/google-research/google-research/tree/master/automl_zero

공식 코드

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

https://github.com/google-research/google-research/tree/master/cfq

공식 코드

Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

https://github.com/google-research/google-research/tree/master/jax_dft

공식 코드

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

https://github.com/google-research/google-research/tree/master/ml_debiaser

공식 코드

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models

https://github.com/google-research/runtime-error-prediction

공식 코드

Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions

https://github.com/google-research/si-score

공식 코드

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size

https://github.com/google/TensorNetwork

공식 코드

TensorNetwork: A Library for Physics and Machine Learning

https://github.com/google/gcnn-survey-paper

공식 코드

Machine Learning on Graphs: A Model and Comprehensive Taxonomy

https://github.com/google/ml-compiler-opt

공식 코드

MLGO: a Machine Learning Guided Compiler Optimizations Framework

https://github.com/google/unrestricted-adversarial-examples

공식 코드

Unrestricted Adversarial Examples

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