Papers with Code 아카이브

Inductive Bias

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

https://github.com/machine-reasoning-ufrgs/typed-graph-network

Relational inductive biases, deep learning, and graph networks

https://github.com/madrylab/pretraining-distribution-shift-robustness

How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers

https://github.com/makora9143/pytorch-convcnp

Convolutional Conditional Neural Processes

https://github.com/mariehane/prototypical-networks

Prototypical Networks for Few-shot Learning

https://github.com/materialsvirtuallab/matgl

Relational inductive biases, deep learning, and graph networks

https://github.com/materialsvirtuallab/megnet

Relational inductive biases, deep learning, and graph networks

https://github.com/matlabmtl/Reco-KGNN-LS

Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems

https://github.com/maxiao0234/ttga

Medical Image Segmentation Using Squeeze-and-Expansion Transformers

https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/mayurak47/Reproducibility_Challenge

Neural Networks Fail to Learn Periodic Functions and How to Fix It

https://github.com/meiyor/SincNet-for-Autism-EEG-based-Emotion-Recognition

Speech and Speaker Recognition from Raw Waveform with SincNet

https://github.com/mic-dkfz/lesionlocator

Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

https://github.com/migalkin/NodePiece

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/migalkin/NodePiece/tree/main/ogb

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/mindspore-ai/models/tree/master/research/cv/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CoAtNet.py

CoAtNet: Marrying Convolution and Attention for All Data Sizes

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/S2Attention.py

S$^2$-MLPv2: Improved Spatial-Shift MLP Architecture for Vision

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/backbone/ConViT.py

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/convit.py

ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases

https://github.com/minseop-aitrics/FewshotLearning

Prototypical Networks for Few-shot Learning

https://github.com/mli0603/blenderneuralangelo

Volume Rendering of Neural Implicit Surfaces

https://github.com/moon23k/Transformer_Archs

Universal Transformers

https://github.com/msfuxian/DualAttentionNet

Prototypical Networks for Few-shot Learning

https://github.com/mxbi/backdoor

Architectural Backdoors in Neural Networks

https://github.com/mylonasc/tf-gnns

Relational inductive biases, deep learning, and graph networks

https://github.com/niranjankrishna-acad/Training-Vision-Transformers-with-Only-2040-Images

Training Vision Transformers with Only 2040 Images

https://github.com/nlokeshiisc/pairnet_release

On Inductive Biases for Heterogeneous Treatment Effect Estimation

https://github.com/nqt228/CoAtNet-tensorflow

CoAtNet: Marrying Convolution and Attention for All Data Sizes

https://github.com/nrel/m2p

Relational inductive biases, deep learning, and graph networks

https://github.com/nrimsky/devinterp

The Local Learning Coefficient: A Singularity-Aware Complexity Measure

https://github.com/ntunlp/ptrnet-depparser

Hierarchical Pointer Net Parsing

https://github.com/octanove/shiba

CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation

https://github.com/oliviawl/image_classification_utkface

A Study on Overfitting in Deep Reinforcement Learning

https://github.com/open-mmlab/mmaction2

Video Swin Transformer

https://github.com/open-mmlab/mmclassification

Global Context Vision Transformers

https://github.com/open-mmlab/mmpretrain

RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer

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