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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/harry67hu/madps

공식 코드

Measuring Policy Distance for Multi-Agent Reinforcement Learning

https://github.com/harsha-simhadri/big-ann-benchmarks

공식 코드

Results of the Big ANN: NeurIPS'23 competition

https://github.com/haruohosoya/cigmo

공식 코드

CIGMO: Categorical invariant representations in a deep generative framework

https://github.com/harvard-evolutionary-dynamics/diversity-time

공식 코드

Maintaining diversity in structured populations

https://github.com/hate-alert/crowdcounter

공식 코드

CrowdCounter: A benchmark type-specific multi-target counterspeech dataset

https://github.com/hav-cam-mit/CTO

공식 코드

Optimally Combining Classifiers for Semi-Supervised Learning

https://github.com/havenpersona/title_generation

공식 코드

Music Playlist Title Generation Using Artist Information

https://github.com/haydengunraj/COVIDNet-CT

공식 코드

COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning

https://github.com/hbchen121/dgreid

공식 코드

Style Variable and Irrelevant Learning for Generalizable Person Re-identification

https://github.com/hdoi5324/benthic-uda

공식 코드

Improved Benthic Classification using Resolution Scaling and SymmNet Unsupervised Domain Adaptation

https://github.com/he-y/nlp-dataset-pruning

공식 코드

Swift Cross-Dataset Pruning: Enhancing Fine-Tuning Efficiency in Natural Language Understanding

https://github.com/he-y/soft-label-pruning-for-dataset-distillation

공식 코드

Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?

https://github.com/health-hci-group/largest_rppg_dataset_evaluation

공식 코드

Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones

https://github.com/heartnetkung/xt-neighbor

공식 코드

Lightning-fast adaptive immune receptor similarity search by symmetric deletion lookup

https://github.com/hed-ucas/alphadecay

공식 코드

AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs

https://github.com/hehesjtu/metropolis-theorem

공식 코드

Metropolis Theorem and Its Applications in Single Image Detail Enhancement

https://github.com/heimingx/semi_seg_proto

공식 코드

Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization

https://github.com/helloworld1973/DNA-DA

공식 코드

Adversarial Domain Adaptation for Cross-user Activity Recognition Using Diffusion-based Noise-centred Learning

https://github.com/hengruilou/deepfacegen

공식 코드

A Large-scale Universal Evaluation Benchmark For Face Forgery Detection

https://github.com/hengyicai/ContrastiveLearning4Dialogue

공식 코드

Group-wise Contrastive Learning for Neural Dialogue Generation

https://github.com/henzler/neuraltexture

공식 코드

Learning a Neural 3D Texture Space from 2D Exemplars

https://github.com/hgilles06/infashai

공식 코드

Neural Fashion Image Captioning : Accounting for Data Diversity

https://github.com/hgroener/diversity_in_poetry_generation

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Evaluating Diversity in Automatic Poetry Generation

https://github.com/hiaac-nlp/capivara

공식 코드

CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages

https://github.com/hiba-meiruan/urdu-vqa-dataset-

공식 코드

Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering

https://github.com/hijkzzz/pymarl2

공식 코드

Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration

https://github.com/hikmatkhan/adrm

공식 코드

Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual Learning

https://github.com/hilab-open-source/wheelpose

공식 코드

WheelPose: Data Synthesis Techniques to Improve Pose Estimation Performance on Wheelchair Users

https://github.com/histai/histai

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HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology

https://github.com/hitachi-nlp/ensemble-metrics

공식 코드

Rethinking Fano's Inequality in Ensemble Learning

https://github.com/hitarth64/therml

공식 코드

Closed-loop Error Correction Learning Accelerates Experimental Discovery of Thermoelectric Materials

https://github.com/hits-mli/gafl

공식 코드

Generating Highly Designable Proteins with Geometric Algebra Flow Matching

https://github.com/hitsz-tmg/explaincpe

공식 코드

ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination

https://github.com/hitsz-tmg/multi-agent-peer-review

공식 코드

Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration

https://github.com/hitsz-tmg/multimodal-in-context-tuning

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A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation

https://github.com/hiyouga/llama-factory

공식 코드

CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning

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