https://github.com/zequnl/CMAML
공식 코드Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Papers with Code 아카이브
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/zetaalphavector/inpars
공식 코드InPars: Data Augmentation for Information Retrieval using Large Language Models
https://github.com/zeyuet/vidmuse
공식 코드VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling
https://github.com/zfan20/spmrec
공식 코드https://github.com/zgahhblhc/Debiased-Chat
공식 코드Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
https://github.com/zhang-pengyu/HMFT
공식 코드Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline
https://github.com/zhang-xiaokun/finerec
공식 코드https://github.com/zhangleye/Kinematic-Analysis
공식 코드Kinematic analysis of structural mechanics based on convolutional neural network
https://github.com/zhangr2021/Multiagent_poetry
공식 코드LLM-based multi-agent poetry generation in non-cooperative environments
https://github.com/zhangrenyuuchicago/balance
공식 코드Scalable Batch-Mode Deep Bayesian Active Learning via Equivalence Class Annealing
https://github.com/zhangyl660/lerm
공식 코드Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding Perspective
https://github.com/zhangzilongc/MMR
공식 코드https://github.com/zhangzizhen/ml-dam
공식 코드Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
https://github.com/zhaochangming/boostforest
공식 코드https://github.com/zhaominyiz/epida
공식 코드EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification
https://github.com/zhaoyi-yan/dkpnet
공식 코드https://github.com/zhaozetong/dualedit
공식 코드Mitigating Safety Fallback in Editing-based Backdoor Injection on LLMs
https://github.com/zheliu92/nlp_followupqg_public
공식 코드https://github.com/zhenxiong-dl/icassp2025-rcac
공식 코드https://github.com/zherenz/imt-mrd
공식 코드https://github.com/zhicaiwww/diff-mix
공식 코드Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
https://github.com/zhixuan-lin/ensemble-rl-continuous
공식 코드https://github.com/zhixuan-lin/ensemble-rl-discrete
공식 코드https://github.com/zhiyintan/topic-model-LLMjudgment
공식 코드Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation
https://github.com/zhiyuns/UNITPathSSL
공식 코드https://github.com/zhoudaquan/Refiner_ViT
공식 코드https://github.com/zhoujiahuan1991/icml2025-tcpa
공식 코드Token Coordinated Prompt Attention is Needed for Visual Prompting
https://github.com/zhouziyuya/consisaug
공식 코드Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis
https://github.com/zhpinkman/llms-effect-on-linguistic-diversity
공식 코드The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
https://github.com/zhudongsheng75/sda
공식 코드SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning
https://github.com/zhunzhong07/CamStyle
공식 코드https://github.com/zhuyun97/rosa
공식 코드RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning
https://github.com/zhuzzq/isfl
공식 코드ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
https://github.com/ziangcao0312/DiffTF
공식 코드DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D Generation
https://github.com/zipjiang/rora
공식 코드https://github.com/zirui-HIT/Fused
공식 코드Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.