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Prediction

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코드

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

https://github.com/So-Cool/bLIMEy

공식 코드

bLIMEy: Surrogate Prediction Explanations Beyond LIME

https://github.com/SongTang-x/SwinLSTM

공식 코드

SwinLSTM:Improving Spatiotemporal Prediction Accuracy using Swin Transformer and LSTM

https://github.com/Soumya-Mukherjee-Statistics/UKP-Arxiv

공식 코드

Uniform Kernel Prober

https://github.com/Srizzle/Deep-Time-Series

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Sentiment Predictability for Stocks

https://github.com/StephAO/olfmlm

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On Losses for Modern Language Models

https://github.com/StonyBrookNLP/PerSenT

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Author's Sentiment Prediction

https://github.com/StonyBrookNLP/irene

공식 코드

IrEne: Interpretable Energy Prediction for Transformers

https://github.com/TRI-ML/RAP

공식 코드

RAP: Risk-Aware Prediction for Robust Planning

https://github.com/TRI-ML/packnet-sfm

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Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion

https://github.com/TZWwww/PROPHET

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PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation

https://github.com/TalSchuster/CATs

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Consistent Accelerated Inference via Confident Adaptive Transformers

https://github.com/TeCSAR-UNCC/CARPe_Posterum

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CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

https://github.com/TeCSAR-UNCC/Pishgu

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Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems

https://github.com/Telecommunication-Telemedia-Assessment/ai_upscaling

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Appeal prediction for AI up-scaled Images

https://github.com/Tobi-r9/RaMViD

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Diffusion Models for Video Prediction and Infilling

https://github.com/TomWaka/CovariateDependentStacking

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Ensemble Prediction via Covariate-dependent Stacking

https://github.com/Traffic-X/ViT-CoMer

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ViT-CoMer: Vision Transformer with Convolutional Multi-scale Feature Interaction for Dense Predictions

https://github.com/Tsinghua-MARS-Lab/CVT-Occ

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CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction

https://github.com/Tsinghua-MARS-Lab/DenseTNT

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DenseTNT: Waymo Open Dataset Motion Prediction Challenge 1st Place Solution

https://github.com/Tsinghua-MARS-Lab/ViP3D

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ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

https://github.com/UCLA-StarAI/NaCL

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What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

https://github.com/UM-Data-Intelligence-Lab/REPLAY

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REPLAY: Modeling Time-Varying Temporal Regularities of Human Mobility for Location Prediction over Sparse Trajectories

https://github.com/USC-Melady/Benchmarking_DL_MIMICIII

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Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets

https://github.com/USTCLH/OccMamba

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OccMamba: Semantic Occupancy Prediction with State Space Models

https://github.com/Uehwan/VisionForPedestrian

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A Real-Time Predictive Pedestrian Collision Warning Service for Cooperative Intelligent Transportation Systems Using 3D Pose Estimation

https://github.com/Unco3892/semf-paper

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SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals

https://github.com/UniHD-CEG/gpu-mangrove

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A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

https://github.com/VRCMF/MARN

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Multitask Balanced and Recalibrated Network for Medical Code Prediction

https://github.com/VRCMF/MT-RAM

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Multitask Recalibrated Aggregation Network for Medical Code Prediction

https://github.com/VachelHU/ESGRN

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Time-Series Event Prediction with Evolutionary State Graph

https://github.com/VachelHU/EvoNet

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Time-Series Event Prediction with Evolutionary State Graph

https://github.com/Vaibhavs10/anli-performance-prediction

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Embarrassingly Simple Performance Prediction for Abductive Natural Language Inference

https://github.com/VeritasYin/DEGNN_node_classification

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Revisiting graph neural networks and distance encoding from a practical view

https://github.com/VityaVitalich/MultiModalCoreset

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Challenges of Multi-Modal Coreset Selection for Depth Prediction

https://github.com/WHUIR/CARL

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A Context-Aware User-Item Representation Learning for Item Recommendation

https://github.com/WHUIR/CARP

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A Capsule Network for Recommendation and Explaining What You Like and Dislike

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