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Diversity
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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/qingwang-isu/augure
공식 코드Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
https://github.com/qinjr/SCoRe
공식 코드Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
https://github.com/qinyiwei/dive
공식 코드https://github.com/qizhangli/morebayesian-attack
공식 코드Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
https://github.com/qkaren/converse_reading_cmr
공식 코드Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading
https://github.com/qrqrqrqr/emu
공식 코드Enhanced Memory Network: The novel network structure for Symbolic Music Generation
https://github.com/qtli/eventplanfortextgen
공식 코드https://github.com/quanshr/augcon
공식 코드Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity
https://github.com/quchangle1/DRAFT
공식 코드From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions
https://github.com/qyin863/dca-sbrs
공식 코드A Simple Yet Effective Approach for Diversified Session-Based Recommendation
https://github.com/qyin863/understanding-diversity-in-sbrss
공식 코드https://github.com/qym7/cbdm-pytorch
공식 코드https://github.com/qzc438-research/ontology-nlp
공식 코드How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?
https://github.com/rafaelhferreira/mtad_cta
공식 코드Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants
https://github.com/raghavian/cFlow
공식 코드Uncertainty quantification in medical image segmentation with normalizing flows
https://github.com/rahafaljundi/Gradient-based-Sample-Selection
공식 코드Gradient based sample selection for online continual learning
https://github.com/rainysponge/split-learning-attacks
공식 코드https://github.com/rajiv1990/DEff-GAN-Data-Efficient-GAN
공식 코드DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis
https://github.com/ramprasad-group/bioplastic_design
공식 코드https://github.com/raojiyong/ppap
공식 코드Probabilistic Prompt Distribution Learning for Animal Pose Estimation
https://github.com/raphael-baena/FIERCE-repo
공식 코드Preserving Fine-Grain Feature Information in Classification via Entropic Regularization
https://github.com/raphael-sch/map2seq_vln
공식 코드Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor Areas
https://github.com/rashmimarganiatgithub/ICLR2023_Workshop
공식 코드From Local to Global: Navigating Linguistic Diversity in the African Context
https://github.com/rasmusbergpalm/vnca
공식 코드https://github.com/ratschlab/repulsive_ensembles
공식 코드https://github.com/raydentseng/generated_opinions
공식 코드ChatGPT as a commenter to the news: can LLMs generate human-like opinions?
https://github.com/raymin0223/logo
공식 코드Re-thinking Federated Active Learning based on Inter-class Diversity
https://github.com/raymondzmc/topic-model-diversity-aware-coherence-loss
공식 코드Diversity-Aware Coherence Loss for Improving Neural Topic Models
https://github.com/reallsp/SAF
공식 코드Learning Semantic-Aligned Feature Representation for Text-based Person Search
https://github.com/realpolitix/crystmorph
공식 코드From structure mining to unsupervised exploration of atomic octahedral networks
https://github.com/reasat/mstt
공식 코드MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation
https://github.com/reclist/e-pluribus-unum-evalrs-2022
공식 코드E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
https://github.com/rekriz11/DeDiv
공식 코드Comparison of Diverse Decoding Methods from Conditional Language Models
https://github.com/renato145/DENN
공식 코드Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation
https://github.com/renytek13/soft-prompt-generation-with-cgan
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.