Papers with Code 아카이브

Prediction

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

https://github.com/alessandroragano/nomad

WARP-Q: Quality Prediction For Generative Neural Speech Codecs

https://github.com/alex-petrenko/curious-rl

Curiosity-driven Exploration by Self-supervised Prediction

https://github.com/alexanderbrenning/spdiag

Spatial machine-learning model diagnostics: a model-agnostic distance-based approach

https://github.com/alexeyab/midas

Vision Transformers for Dense Prediction

https://github.com/alibaba-miil/peta

Recognizing and Curating Photo Albums via Event-Specific Image Importance

https://github.com/alibaba/TorchEasyRec

Deep Interest Network for Click-Through Rate Prediction

https://github.com/alisadeghian/DRUM

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

https://github.com/allenai/kb

TuckER: Tensor Factorization for Knowledge Graph Completion

https://github.com/alzmxx/OD_Prediction

Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter

https://github.com/amaryllis404/horseshoeBNN

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care

https://github.com/amazon-science/tanl

Structured Prediction as Translation between Augmented Natural Languages

https://github.com/anahnas/SIAP

A multinomial probabilistic model for movie genre predictions

https://github.com/andi611/Self-Supervised-Speech-Pretraining-and-Representation-Learning

Utilizing Self-supervised Representations for MOS Prediction

https://github.com/angelosnal/vision-diffmask

How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking

https://github.com/anonymous20201002/chemprop

Analyzing Learned Molecular Representations for Property Prediction

https://github.com/antocad/FocusOnDepth

Vision Transformers for Dense Prediction

https://github.com/antonisa/lang2vec

Learning Language Representations for Typology Prediction

https://github.com/apoorvumang/CronKGQA

Tensor Decompositions for temporal knowledge base completion

https://github.com/apple/ml-multiple-futures-prediction

Multiple Futures Prediction

https://github.com/ariosantoso/Specification-Driven-Predictive-Process-Monitoring

Specification-Driven Multi-Perspective Predictive Business Process Monitoring (Extended Version)

https://github.com/arshadshk/Position-Prediction-Pretraining

Position Prediction as an Effective Pretraining Strategy

https://github.com/arshadshk/SAINT-pytorch

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

https://github.com/as-ideas/ForwardTacotron

FastPitch: Parallel Text-to-speech with Pitch Prediction

https://github.com/atharvacc/SigmaNewsProject

Neural networks for stock price prediction

https://github.com/atilberk/evidential-deep-learning-to-quantify-classification-uncertainty

Evidential Deep Learning to Quantify Classification Uncertainty

https://github.com/aurianworld/matpac

Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning

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

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/avidale/encodechka

Sentence Embeddings for Russian NLU

https://github.com/axeber01/dold

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/baaivision/emu3

Emu3: Next-Token Prediction is All You Need

https://github.com/baidut/PaQ-2-PiQ

From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

https://github.com/bakirillov/capsules

Exploring Deep Anomaly Detection Methods Based on Capsule Net

https://github.com/bbrighttaer/jova_baselines

PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction

https://github.com/beierzhu/xerm

Disentangling Label Distribution for Long-tailed Visual Recognition

https://github.com/benedekrozemberczki/pytorch_geometric_temporal

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

https://github.com/benjilu/forestError

A Unified Framework for Random Forest Prediction Error Estimation

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