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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/hyren/acl

공식 코드

Adversarial Constraint Learning for Structured Prediction

https://github.com/hyunjinhwn/sigir22-ahp

공식 코드

AHP: Learning to Negative Sample for Hyperedge Prediction

https://github.com/hyunp2/alphafold

공식 코드

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

https://github.com/iambic-therapeutics/np-bench

공식 코드

NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models

https://github.com/iankrupkin/Prediction-Error-Estimation-in-Random-Forests

공식 코드

Prediction Error Estimation in Random Forests

https://github.com/iarai/neurips2022-traffic4cast

공식 코드

Hierarchical Graph Structures for Congestion and ETA Prediction

https://github.com/ibalazevic/HypER

공식 코드

Hypernetwork Knowledge Graph Embeddings

https://github.com/ibalazevic/TuckER

공식 코드

TuckER: Tensor Factorization for Knowledge Graph Completion

https://github.com/ibm/molformer

공식 코드

Improving Molecular Properties Prediction Through Latent Space Fusion

https://github.com/ibm/otter-knowledge

공식 코드

Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery

https://github.com/ic-crc/uncertainty-estimation

공식 코드

Target Strangeness: A Novel Conformal Prediction Difficulty Estimator

https://github.com/iccv2595/svdp

공식 코드

Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

https://github.com/icitylab/traffnet

공식 코드

TraffNet: Learning Causality of Traffic Generation for What-if Prediction

https://github.com/ictnlp/DiverseNMT

공식 코드

Modeling Fluency and Faithfulness for Diverse Neural Machine Translation

https://github.com/idirlab/kgcompletion

공식 코드

Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

https://github.com/idrori/cu-tsp

공식 코드

Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

https://github.com/idt-iti/xai-deepfakes

공식 코드

Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection

https://github.com/ifm-mag/HyDRO_PLUS

공식 코드

Efficient and Privacy-Preserved Link Prediction via Condensed Graphs

https://github.com/illdepence/contextgraph

공식 코드

CoCon: A Data Set on Combined Contextualized Research Artifact Use

https://github.com/image-science-lab-cmu/skycam

공식 코드

Computational Imaging for Long-Term Prediction of Solar Irradiance

https://github.com/imanlab/action_conditioned_tactile_prediction

공식 코드

Action Conditioned Tactile Prediction: case study on slip prediction

https://github.com/imatge-upc/SalEMA

공식 코드

Simple vs complex temporal recurrences for video saliency prediction

https://github.com/imatge-upc/affective-2017-musa2

공식 코드

More cat than cute? Interpretable Prediction of Adjective-Noun Pairs

https://github.com/imatge-upc/pathgan

공식 코드

PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks

https://github.com/imatge-upc/salgan

공식 코드

SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

https://github.com/imatge-upc/saliency-2016-cvpr

공식 코드

Shallow and Deep Convolutional Networks for Saliency Prediction

https://github.com/imatge-upc/saliency-2016-lsun

공식 코드

End-to-end Convolutional Network for Saliency Prediction

https://github.com/iminthemiddle/emloco

공식 코드

Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment

https://github.com/impression2805/fmfp

공식 코드

Rethinking Confidence Calibration for Failure Prediction

https://github.com/imsb-uke/dcsurv

공식 코드

Deep Learning-Based Discrete Calibrated Survival Prediction

https://github.com/imsb-uke/ecarenet

공식 코드

Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network

https://github.com/ingenii-solutions/ingenii-quantum-hybrid-networks

공식 코드

A hybrid quantum-classical fusion neural network to improve protein-ligand binding affinity predictions for drug discovery

https://github.com/inhwanbae/gpgraph

공식 코드

Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction

https://github.com/inhwanbae/npsn

공식 코드

Non-Probability Sampling Network for Stochastic Human Trajectory Prediction

https://github.com/innovator-zero/MTDP_Lib

공식 코드

Prompt Guided Transformer for Multi-Task Dense Prediction

https://github.com/innvariant/sparsity-experiments-2021

공식 코드

Experiments on Properties of Hidden Structures of Sparse Neural Networks

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