Papers with Code 아카이브

General Classification

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하위 태스크

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평가표

11

보존된 평가 기록

19

코드

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

https://github.com/gpleiss/equalized_odds_and_calibration

공식 코드

On Fairness and Calibration

https://github.com/gpleiss/temperature_scaling

공식 코드

On Calibration of Modern Neural Networks

https://github.com/gregorykucherov/spaced-seeds-for-metagenomics

공식 코드

Spaced seeds improve k-mer-based metagenomic classification

https://github.com/griegler/octnet

공식 코드

OctNet: Learning Deep 3D Representations at High Resolutions

https://github.com/grigorisg9gr/polynomials-for-augmenting-nns

공식 코드

Augmenting Deep Classifiers with Polynomial Neural Networks

https://github.com/groupoasys/Medical_data

공식 코드

A novel embedded min-max approach for feature selection in nonlinear support vector machine classification

https://github.com/gtolias/mom

공식 코드

Mining on Manifolds: Metric Learning without Labels

https://github.com/gtownrocks/grafuple

공식 코드

Applications of Graph Integration to Function Comparison and Malware Classification

https://github.com/gtshs2/BetaLSTM

공식 코드

Bivariate Beta-LSTM

https://github.com/guangyizhangbci/eeg_riemannian

공식 코드

Spatio-Temporal EEG Representation Learning on Riemannian Manifold and Euclidean Space

https://github.com/gudovskiy/fmap_compression

공식 코드

DNN Feature Map Compression using Learned Representation over GF(2)

https://github.com/guelfoweb/peframe

공식 코드

Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network

https://github.com/gugarosa/opfython

공식 코드

OPFython: A Python-Inspired Optimum-Path Forest Classifier

https://github.com/gugarosa/synthetic_rbms

공식 코드

Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann Machines

https://github.com/guilherme-pombo/3DPixelCNN

공식 코드

Bayesian Volumetric Autoregressive generative models for better semisupervised learning

https://github.com/guotong1988/Automatic-Label-Error-Correction

공식 코드

Learning From Human Correction

https://github.com/guoxih/Busy-Quiet-Video-Disentangling-for-Video-Classification

공식 코드

Busy-Quiet Video Disentangling for Video Classification

https://github.com/guoxih/region-based-non-local-network

공식 코드

Region-based Non-local Operation for Video Classification

https://github.com/guoyinwang/LEAM

공식 코드

Joint Embedding of Words and Labels for Text Classification

https://github.com/guoyuntu/BEAC_network

공식 코드

A Multi-task Neural Approach for Emotion Attribution, Classification and Summarization

https://github.com/guoyurong0104/crl-code

공식 코드

Competing Ratio Loss for Discriminative Multi-class Image Classification

https://github.com/gurkirt/actNet-inAct

공식 코드

Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge

https://github.com/gy20073/compact_bilinear_pooling

공식 코드

Compact Bilinear Pooling

https://github.com/gyglim/dvn

공식 코드

Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs

https://github.com/gyhandy/humanoid-vision-engine

공식 코드

Contributions of Shape, Texture, and Color in Visual Recognition

https://github.com/gzerveas/mvts_transformer

공식 코드

A Transformer-based Framework for Multivariate Time Series Representation Learning

https://github.com/h-aldarmaki/sentence_eval

공식 코드

Evaluation of Unsupervised Compositional Representations

https://github.com/haarburger/multi-scale-curriculum

공식 코드

Multi Scale Curriculum CNN for Context-Aware Breast MRI Malignancy Classification

https://github.com/hackerekcah/distinct-events-asc

공식 코드

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

https://github.com/haczqyf/gcn-data-alignment

공식 코드

Quantifying the Alignment of Graph and Features in Deep Learning

https://github.com/haczqyf/ggc

공식 코드

Geometric graphs from data to aid classification tasks with graph convolutional networks

https://github.com/haebeom-lee/dropmax

공식 코드

DropMax: Adaptive Variational Softmax

https://github.com/hancyran/SepNet

공식 코드

Deeper or Wider Networks of Point Clouds with Self-attention?

https://github.com/hanmenghan/TMC

공식 코드

Trusted Multi-View Classification

https://github.com/hanselowski/athene_system

공식 코드

A Retrospective Analysis of the Fake News Challenge Stance Detection Task

https://github.com/hantek/BinaryConnect

공식 코드

Neural Networks with Few Multiplications

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