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General Classification

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평가표

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보존된 평가 기록

19

코드

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

https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/ewc.py

Overcoming catastrophic forgetting in neural networks

https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/memory.py

Protoformer: Embedding Prototypes for Transformers

https://github.com/codingperson/pearl

X-Class: Text Classification with Extremely Weak Supervision

https://github.com/cognitiveailab/questionclassification

Multi-class Hierarchical Question Classification for Multiple Choice Science Exams

https://github.com/cognitivemodeling/finn

Weight Uncertainty in Neural Networks

https://github.com/cogsci2/Pneumonia-XRay-Differentiation-from-Kaggle-Dataset

A systematic study of the class imbalance problem in convolutional neural networks

https://github.com/cogsys-tuebingen/uninas

Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours

https://github.com/coleridge72/quick_thoughts

An efficient framework for learning sentence representations

https://github.com/colesturza/CSCI4622-Project

Convolutional Recurrent Neural Networks for Music Classification

https://github.com/collab-uniba/Emotion_and_Polarity_SO

EmoTxt: A Toolkit for Emotion Recognition from Text

https://github.com/comicencyclo/TransferLearning_DiscriminativeFineTuning

Universal Language Model Fine-tuning for Text Classification

https://github.com/comp-well-org/Data_Augmentation_for_Biobehavioral_Time_Series_Data

Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

https://github.com/compSciKai/CMPT414-Term-Project-Tensorflow-OCR

EMNIST: an extension of MNIST to handwritten letters

https://github.com/compnet/pang

graph2vec: Learning Distributed Representations of Graphs

https://github.com/conan7882/adversarial-autoencoders

Adversarial Autoencoders

https://github.com/conceptofmind/CrossViT-flax

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

https://github.com/conceptofmind/LeViT-flax

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

https://github.com/congyingxia/IncrementalFSTC

Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System

https://github.com/context-mover/HypEval

Experiments with Three Approaches to Recognizing Lexical Entailment

https://github.com/continental/kiwissen-bayesian-trajectory-prediction

Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

https://github.com/convergence-lab/novograd

Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

https://github.com/cooijmanstim/recurrent-batch-normalization

Recurrent Batch Normalization

https://github.com/cooooorn/pytorch-xnor-net

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

https://github.com/corenel/pytorch-adda

Adversarial Discriminative Domain Adaptation

https://github.com/cornell-zhang/allo-pldi24-artifact

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/cornellius-gp/gpytorch

Doubly Stochastic Variational Inference for Deep Gaussian Processes

https://github.com/cove-adml/adml-anon

Convolutional Neural Networks for Sentence Classification

https://github.com/cpark321/bayesian-neural-networks

Weight Uncertainty in Neural Networks

https://github.com/cpark321/uncertainty-deep-learning

Weight Uncertainty in Neural Networks

https://github.com/cpjku/efficientleaf

LEAF: A Learnable Frontend for Audio Classification

https://github.com/cran/fairmodels

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

https://github.com/cran/missRanger

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/craston/object_detection_cib

Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

https://github.com/createamind/DIM_Commented

Learning deep representations by mutual information estimation and maximization

https://github.com/criteo-research/pytorch-ada

Domain-Adversarial Training of Neural Networks

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