https://github.com/antonsteenvoorden/ADSR
공식 코드Attribute-aware Diversification for Sequential Recommendations
Papers with Code 아카이브
Diversity
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anubhabghosh/genhmm
공식 코드Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
https://github.com/anubhav1997/youneednodataset
공식 코드Zero-shot racially balanced dataset generation using an existing biased StyleGAN2
https://github.com/anugyas/nluproject
공식 코드No Offense Taken: Eliciting Offensiveness from Language Models
https://github.com/anything-of-anything/anything-3d
공식 코드Anything-3D: Towards Single-view Anything Reconstruction in the Wild
https://github.com/apaleyes/hippo
공식 코드https://github.com/apexrl/MapGo
공식 코드MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
https://github.com/apple/ml-aspanformer
공식 코드ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer
https://github.com/apple/ml-gsn
공식 코드Unconstrained Scene Generation with Locally Conditioned Radiance Fields
https://github.com/apple/ml-interactive-data-augmentation
공식 코드https://github.com/appliedgeometry/eulearn_db
공식 코드https://github.com/aprilbian/st_jscc
공식 코드Space-time design for deep joint source channel coding of images Over MIMO channels
https://github.com/apuaaChen/GNEDNN_release
공식 코드https://github.com/aqlaboratory/genie2
공식 코드https://github.com/araffin/robotics-rl-srl
공식 코드S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
https://github.com/aramahmed/DCSO-Algorithm
공식 코드Dynamic Cat Swarm Optimization Algorithm for Backboard Wiring Problem
https://github.com/arashrabbani/augmented-pattern
공식 코드https://github.com/arayabrain/space-engineers-ai-spaceship-generator
공식 코드Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms
https://github.com/ari-dasci/s-planningproblemgeneration
공식 코드NeSIG: A Neuro-Symbolic Method for Learning to Generate Planning Problems
https://github.com/ari-holtzman/degen
공식 코드https://github.com/arikreuter/tntm
공식 코드Probabilistic Topic Modelling with Transformer Representations
https://github.com/arlundborg/perturbationcoda
공식 코드https://github.com/armandnm/diverse-vit
공식 코드Learning Diverse Features in Vision Transformers for Improved Generalization
https://github.com/armandstrickernlp/task-chitchat-entropy
공식 코드https://github.com/armelrandy/icl-mt
공식 코드In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation
https://github.com/arnaoutlab/diversity
공식 코드$\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets
https://github.com/arron2003/rgbd2full3d
공식 코드https://github.com/arunbalajeev/query-video-summary
공식 코드Query-adaptive Video Summarization via Quality-aware Relevance Estimation
https://github.com/asaran/vessal
공식 코드https://github.com/asash/gptrec_rl
공식 코드Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
https://github.com/asdvfghg/quadratic_efficiency
공식 코드One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
https://github.com/ashwinkalyan/dbs
공식 코드Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/asimokby/formality-bias-analysis
공식 코드https://github.com/asohmo/asohmo-dataset
공식 코드Which Argumentative Aspects of Hate Speech in Social Media can be reliably identified?
https://github.com/asopi/rental-project
공식 코드Implementing Smart Contracts: The case of NFT-rental with pay-per-like
https://github.com/asoulet/superficiality
공식 코드The Structure and Dynamics of Knowledge Graphs, with Superficiality
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.