https://github.com/tamlhp/kbqa
공식 코드A Comparative Study of Question Answering over Knowledge Bases
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/tanay2001/core
공식 코드CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
https://github.com/tangjialong/event-schema-harvester
공식 코드https://github.com/taolusi/secure
공식 코드https://github.com/tareknaous/readme
공식 코드ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
https://github.com/tartunlp/smugri-llm
공식 코드https://github.com/tasos-a/sonopt-1.0
공식 코드SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
https://github.com/tayebiarasteh/fldomain
공식 코드Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
https://github.com/tcl9876/visual-vae
공식 코드https://github.com/tcoroller/pGAN
공식 코드A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANs
https://github.com/tdopierre/ProtAugment
공식 코드ProtAugment: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning
https://github.com/tehqin/EvoSabber
공식 코드Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
https://github.com/tehqin/EvoStone
공식 코드Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
https://github.com/tehqin/QualDivBenchmark
공식 코드Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
https://github.com/tejas-gokhale/alt
공식 코드Improving Diversity with Adversarially Learned Transformations for Domain Generalization
https://github.com/telekom/indiceval-xl
공식 코드IndicEval-XL: Bridging Linguistic Diversity in Code Generation Across Indic Languages
https://github.com/tencent-alchemy/Alchemy
공식 코드Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
https://github.com/tengyu-liu/GenDexGrasp
공식 코드https://github.com/tensorflow/graphics/tree/master/tensorflow_graphics/projects/local_implicit_grid
공식 코드https://github.com/terarachang/dataicl
공식 코드https://github.com/testzer0/ambiqt
공식 코드Benchmarking and Improving Text-to-SQL Generation under Ambiguity
https://github.com/tgangwani/QDAgents
공식 코드Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity
https://github.com/thaihungle/SAM
공식 코드https://github.com/thalesbertaglia/instasynth
공식 코드https://github.com/thanhluantrinh/lddgan
공식 코드Latent Denoising Diffusion GAN: Faster sampling, Higher image quality
https://github.com/thanos-db/pixelsmith
공식 코드Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation Models
https://github.com/theia-4869/cdpruner
공식 코드Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs
https://github.com/themaxiao/ditmos
공식 코드DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers
https://github.com/themoro/diraugmentation
공식 코드Device-Robust Acoustic Scene Classification via Impulse Response Augmentation
https://github.com/thetempaccount/co-speech-motion-generation
공식 코드https://github.com/thinkwee/DPP_CNN_Summarization
공식 코드https://github.com/thinkwee/UniKeyphrase
공식 코드UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction
https://github.com/thkreutz/crowdorchestrationsim
공식 코드Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
https://github.com/thomasnorr/q-senn
공식 코드Take 5: Interpretable Image Classification with a Handful of Features
https://github.com/thomasp05/improved-robustness-with-ecoc
공식 코드Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes
https://github.com/thomassutter/mmvampvae
공식 코드Unity by Diversity: Improved Representation Learning in Multimodal VAEs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.