https://github.com/4paradigm/featinsight
공식 코드FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform
Papers with Code 아카이브
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BinhangYuan/IST_Release
공식 코드Distributed Learning of Deep Neural Networks using Independent Subnet Training
https://github.com/CIOL-SUST/SCG
공식 코드https://github.com/CausalML/Weak-Instruments-Obs-Data-CATE
공식 코드Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
https://github.com/Extreme-classification/DECAF
공식 코드https://github.com/Extreme-classification/ECLARE
공식 코드ECLARE: Extreme Classification with Label Graph Correlations
https://github.com/OctoberChang/X-Transformer
공식 코드Taming Pretrained Transformers for Extreme Multi-label Text Classification
https://github.com/SubsetSelection/EquiVSet
공식 코드Learning Neural Set Functions Under the Optimal Subset Oracle
https://github.com/Tangyiming205069/controllable-seo
공식 코드StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization
https://github.com/abdulazizasz/fairness_sentiment
공식 코드Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
https://github.com/allenjack/GATE
공식 코드Gated Attentive-Autoencoder for Content-Aware Recommendation
https://github.com/amazon-science/synthesizrr
공식 코드SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
https://github.com/buds-lab/recommender-sys-for-buildings-textmining-review
공식 코드https://github.com/cenyk1230/GATNE
공식 코드Representation Learning for Attributed Multiplex Heterogeneous Network
https://github.com/chang-li/MergeDTS
공식 코드MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
https://github.com/claclark/preferences
공식 코드https://github.com/criteo-research/reco-gym
공식 코드https://github.com/diptamath/context-aware-retail-product-recommendation
공식 코드Context-aware Retail Product Recommendation with Regularized Gradient Boosting
https://github.com/ehsankazemi/adaptiveSubseq
공식 코드https://github.com/extreme-classification/mufin
공식 코드https://github.com/gofynd/mildnet
공식 코드MILDNet: A Lightweight Single Scaled Deep Ranking Architecture
https://github.com/haitaomao/amazon-m2
공식 코드https://github.com/iosband/ts_tutorial
공식 코드https://github.com/jumxglhf/g2a2c
공식 코드https://github.com/kang205/STL-Dataset
공식 코드Complete the Look: Scene-based Complementary Product Recommendation
https://github.com/kramerlab/4StepFocus
공식 코드https://github.com/mickeystroller/TaxoExpan
공식 코드TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
https://github.com/msn199959/Logo-2k-plus-Dataset
공식 코드Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification
https://github.com/otto-de/tron
공식 코드https://github.com/retailmarketingai/retailsynth
공식 코드RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation
https://github.com/snap-stanford/bc-emb
공식 코드https://github.com/stayones/usgae-centric-intent-understanding
공식 코드https://github.com/tonyrivermsfly/TSPRA
공식 코드Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews
https://github.com/utahIRlab/AMTurk-Product-Search-Explanation-Evaluation
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/utahIRlab/drem-attention
공식 코드Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search
https://github.com/vinid/prodb
공식 코드BERT Goes Shopping: Comparing Distributional Models for Product Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.