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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/kasys-lab/anserini-kasys

Document Expansion by Query Prediction

https://github.com/katerinagiannoutsou/dso-coding-project

Smart "Predict, then Optimize"

https://github.com/katieluo88/280finalproj_nyudepth

Deeper Depth Prediction with Fully Convolutional Residual Networks

https://github.com/kbrodt/waymo-motion-prediction-2021

MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving

https://github.com/kclip/r_autoeval_plus

Prediction-Powered Inference

https://github.com/keerthi165/depthEstimation

Deep attention-based classification network for robust depth prediction

https://github.com/keillernogueira/FDSI

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/keonlee9420/FastPitchFormant

FastPitch: Parallel Text-to-speech with Pitch Prediction

https://github.com/keonlee9420/WaveGrad2

End-to-End Adversarial Text-to-Speech

https://github.com/kethmih/airphynet

AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction

https://github.com/kjasinska/ltls

Log-time and Log-space Extreme Classification

https://github.com/kkhoot/PAA_Faster-RCNN

Probabilistic Anchor Assignment with IoU Prediction for Object Detection

https://github.com/kparaschakis/bbc_algorithm

Post-Selection Confidence Bounds for Prediction Performance

https://github.com/kritiksoman/GIMP-ML

Vision Transformers for Dense Prediction

https://github.com/kupuSs/DIEN-pipline

Deep Interest Evolution Network for Click-Through Rate Prediction

https://github.com/labmlai/annotated_deep_learning_paper_implementations

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/lanyexiaosa/brltm

Bidirectional Representation Learning from Transformers using Multimodal Electronic Health Record Data to Predict Depression

https://github.com/lars-research/red-gnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/layumi/Seg-Uncertainty

Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation

https://github.com/ldmt-muri/alignment-with-openfst

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

https://github.com/leffff/diffusion-forcing

Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion

https://github.com/leffff/vgae-pytorch

Variational Graph Auto-Encoders

https://github.com/leoandeol/cods

Towards PAC Multi-Object Detection and Tracking

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit

EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/lesterpjy/numeric-t5

WT5?! Training Text-to-Text Models to Explain their Predictions

https://github.com/lfhase/ciga

Variational Graph Auto-Encoders

https://github.com/lgerrets/rl18-curiosity

Curiosity-driven Exploration by Self-supervised Prediction

https://github.com/liangcaisu/stem

BARS-CTR: Open Benchmarking for Click-Through Rate Prediction

https://github.com/licheng0794/IMV-LSTM-Classfication

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

https://github.com/lifu-tu/BenchmarkingApproximateInference

Benchmarking Approximate Inference Methods for Neural Structured Prediction

https://github.com/lifu-tu/ENGINE

Learning Approximate Inference Networks for Structured Prediction

https://github.com/lifu-tu/INFNET

Learning Approximate Inference Networks for Structured Prediction

https://github.com/lightly-ai/lightly

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

https://github.com/lightning-uq-box/lightning-uq-box

Conformalized Quantile Regression

https://github.com/linz94/mfin-cycle

Temporal Interpolation via Motion Field Prediction

https://github.com/liuem607/DYAN

Deep multi-scale video prediction beyond mean square error

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