https://github.com/rongyizhu/l2t
공식 코드Learning to Transform Dynamically for Better Adversarial Transferability
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/ronyabecidan/holisticsteganalysiswithsetcovering
공식 코드Using Set Covering to Generate Databases for Holistic Steganalysis
https://github.com/ropgen/ropgen
공식 코드RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
https://github.com/rossetl/fef
공식 코드Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics
https://github.com/rowerliu/adbd
공식 코드Generating Progressive Images from Pathological Transitions via Diffusion Model
https://github.com/rowerliu/add
공식 코드Generating Progressive Images from Pathological Transitions via Diffusion Model
https://github.com/rpatrik96/lti-ica
공식 코드https://github.com/rsilva8/MISA
공식 코드Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
https://github.com/rubinxin/CoCaBO_code
공식 코드Bayesian Optimisation over Multiple Continuous and Categorical Inputs
https://github.com/rucaibox/simpledeepsearcher
공식 코드SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis
https://github.com/rucaibox/vda
공식 코드Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
https://github.com/rudymorel/shadowing
공식 코드https://github.com/ruiliu-ai/DivCo
공식 코드DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network
https://github.com/ruiyinglu/ofanet-for-dual-view-sci
공식 코드Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network
https://github.com/ruizhaogit/maximum_entropy_population_based_training
공식 코드Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
https://github.com/runedk93/pantypes
공식 코드Pantypes: Diverse Representatives for Self-Explainable Models
https://github.com/ruotianluo/coco-caption
공식 코드https://github.com/russiannlp/rublimp
공식 코드https://github.com/ruyue0001/decompose_icl_improvement
공식 코드https://github.com/rvl-lab-utoronto/dibs
공식 코드Diversity inducing Information Bottleneck in Model Ensembles
https://github.com/rvp-group/vbr-devkit
공식 코드https://github.com/rwickman/neat_rl
공식 코드Efficient Quality-Diversity Optimization through Diverse Quality Species
https://github.com/ryanzhumich/editsql
공식 코드https://github.com/ryc-98/mumodig
공식 코드https://github.com/ryeoat3/gomin
공식 코드https://github.com/rzhwang/infodiffusion
공식 코드InfoDiffusion: Information Entropy Aware Diffusion Process for Non-Autoregressive Text Generation
https://github.com/s3prl/s3prl
공식 코드https://github.com/sachinpc1993/fair-arguments
공식 코드https://github.com/sadil13/vln-ram
공식 코드https://github.com/sadjadeb/exHarmony
공식 코드exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem
https://github.com/saeedhaghighi/vanet
공식 코드Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs
https://github.com/safakkbilici/conditional-variational-transformer
공식 코드Transformers as Neural Augmentors: Class Conditional Sentence Generation via Variational Bayes
https://github.com/safreita1/malnet-graph
공식 코드https://github.com/sagarverma/FashionRecommendationST-LSTM
공식 코드https://github.com/sagebei/local_diversity_of_condorcet_domains
공식 코드https://github.com/sai990323/decodingmatters
공식 코드Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.