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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/oxai/gpt3-jobadvert-bias

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Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements

https://github.com/pabloruizponce/in2IN

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in2IN: Leveraging individual Information to Generate Human INteractions

https://github.com/pacerv/implicitmotion

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Implicit Neural Representations for Variable Length Human Motion Generation

https://github.com/pair-code/interpretability

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Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language Models

https://github.com/panasonicconnect/invreg

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Invariant Feature Regularization for Fair Face Recognition

https://github.com/panchengzhao/lake-red

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LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion

https://github.com/pangzss/pytorch-ctvsum

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Contrastive Losses Are Natural Criteria for Unsupervised Video Summarization

https://github.com/paper-submission-rafa/diversification-code

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Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees

https://github.com/paramita08/wikidata_bias_websci_2023

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Diversity matters: Robustness of bias measurements in Wikidata

https://github.com/parinzee/seed-free-synthetic-instruct

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Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai

https://github.com/park-ing-lot/dive

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DIVE: Towards Descriptive and Diverse Visual Commonsense Generation

https://github.com/parkjinhyeock/evalrs-submission

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Item-based Variational Auto-encoder for Fair Music Recommendation

https://github.com/pascalson/causaldialogue

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CausalDialogue: Modeling Utterance-level Causality in Conversations

https://github.com/passing2961/dialogcc

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DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset

https://github.com/paulviallard/ecml21-pb-cbound

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Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound

https://github.com/pcdgan/PcDGAN

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PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design

https://github.com/pclucas14/GansFallingShort

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Language GANs Falling Short

https://github.com/pedroramaciotti/HINPy

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Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity

https://github.com/penfever/sos-bench

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Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking

https://github.com/pengyu-team/s3e

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S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM

https://github.com/penway/lfm

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Latent Space is Feature Space: Regularization Term for GANs Training on Limited Dataset

https://github.com/performative/performative-recommendation

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Performative Recommendation: Diversifying Content via Strategic Incentives

https://github.com/perladoubinsky/SemAug

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Semantic Generative Augmentations for Few-Shot Counting

https://github.com/peterfengyx/dunst

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DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation

https://github.com/peterfengyx/kest

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KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation

https://github.com/peterliwenhao/care

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Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention

https://github.com/pfriedri/wdm-3d

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WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis

https://github.com/pgiouroukis/dual

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DUAL: Diversity and Uncertainty Active Learning for Text Summarization

https://github.com/pherrusa7/Traffic4cast_NeurIPS_2019

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Recurrent Autoencoder with Skip Connections and Exogenous Variables for Traffic Forecasting

https://github.com/philippaltmann/crop

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CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing

https://github.com/phlippe/P2_Net

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Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing

https://github.com/photon-git/AlloyGAN

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Inverse Materials Design by Large Language Model-Assisted Generative Framework

https://github.com/picsart-ai-research/ipl-zero-shot-generative-model-adaptation

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Zero-shot Generative Model Adaptation via Image-specific Prompt Learning

https://github.com/pierrepeterlongo/commet

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Commet: comparing and combining multiple metagenomic datasets

https://github.com/pillowsofwind/debateqa

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DebateQA: Evaluating Question Answering on Debatable Knowledge

https://github.com/pittisl/mpnp-llm

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Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.