Papers with Code 아카이브

Prediction

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평가표

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보존된 평가 기록

6

코드

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

https://github.com/BorealisAI/towards-better-sel-cls

SelectiveNet: A Deep Neural Network with an Integrated Reject Option

https://github.com/Bradley-Butcher/Conformers

Conformal Language Modeling

https://github.com/BugOMan/summary_generator

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/Bunseki2/DeepL

Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models

https://github.com/CAVED123/DeepPurpose

DeepDTA: Deep Drug-Target Binding Affinity Prediction

https://github.com/CV51GO/PAA_Megengine

Probabilistic Anchor Assignment with IoU Prediction for Object Detection

https://github.com/Carlisle-Liu/OCENet

Confidence-Aware Learning for Camouflaged Object Detection

https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

https://github.com/ChenDelong1999/HHForecasting

Significant Wave Height Prediction based on Wavelet Graph Neural Network

https://github.com/Cherrydomini/Effects-of-feature-dropping-on-COMPAS-with-the-influence-of-LIME-

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

https://github.com/Chung-I/Variational-Recurrent-Autoencoder-Tensorflow

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/CogComp/iclr_database

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

https://github.com/Cogito2012/DEAR

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/CoinCheung/denseCL

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/CompRhys/roost

Materials Representation and Transfer Learning for Multi-Property Prediction

https://github.com/CompVis/image2video-synthesis-using-cINNs

Self-Supervised Visual Planning with Temporal Skip Connections

https://github.com/DEck13/conformal.glm

Distribution-Free Predictive Inference For Regression

https://github.com/DEck13/paraconformal

Distribution-Free Predictive Inference For Regression

https://github.com/DaehanKim/vgae_pytorch

Variational Graph Auto-Encoders

https://github.com/DarshanDeshpande/jax-models

MPViT: Multi-Path Vision Transformer for Dense Prediction

https://github.com/DataCanvasIO/DeepTables

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

https://github.com/DavorPenzar/tlp

Temporal Link Prediction using Matrix and Tensor Factorizations

https://github.com/Developer-Y/cs-video-courses

Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images

https://github.com/Di-Chai/GraphCNN-Bike

Bike Flow Prediction with Multi-Graph Convolutional Networks

https://github.com/Divyansh898/Compiler-Lab-

Heart Disease Prediction System using Associative Classification and Genetic Algorithm

https://github.com/Djmcflush/Funky-Movement

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/Dohppak/Music-Emotion-Recognition-Classification

Music Mood Detection Based On Audio And Lyrics With Deep Neural Net

https://github.com/EBjerrum/DeepIEP

DeepIEP: a Peptide Sequence Model of Isoelectric Point (IEP/pI) using Recurrent Neural Networks (RNNs)

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/EPFL-VILAB/3DCommonCorruptions

Vision Transformers for Dense Prediction

https://github.com/EbadSyed/spadRGBD

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/EgoRedMC/pytorch_OBS

Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

https://github.com/EkdeepSLubana/OrthoReg

Faster gaze prediction with dense networks and Fisher pruning

https://github.com/ElementAI/HypE

Knowledge Hypergraphs: Prediction Beyond Binary Relations

https://github.com/Entodi/meshnet-pytorch

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/Expedit-LargeScale-Vision-Transformer/Expedit-SWAG

Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning

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