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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/keillernogueira/FDSI

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Exploiting ConvNet Diversity for Flooding Identification

https://github.com/keiserlab/exceiver

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A single-cell gene expression language model

https://github.com/keke921/h2t

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Feature Fusion from Head to Tail for Long-Tailed Visual Recognition

https://github.com/keleog/PidginUNMT

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PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English

https://github.com/keniu042/diffusion-reid

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Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-Training

https://github.com/kennyderek/adap

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Adaptable Agent Populations via a Generative Model of Policies

https://github.com/kepingbi/ARedSumSentRank

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AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization

https://github.com/kerrydrx/escort

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Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

https://github.com/keunwoochoi/LSTMetallica

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Text-based LSTM networks for Automatic Music Composition

https://github.com/keunwoochoi/lstm_real_book

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Text-based LSTM networks for Automatic Music Composition

https://github.com/kevinma1515/Concept_Generation

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Exploring the Capabilities of Large Language Models for Generating Diverse Design Solutions

https://github.com/kevinscaria/targen

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TarGEN: Targeted Data Generation with Large Language Models

https://github.com/kevivk/mwp-augmentation

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Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem Solvers

https://github.com/keyonvafa/human-generalization-llms

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Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function

https://github.com/kfu02/jaxmarl

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Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination

https://github.com/khorrams/utlo

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Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions

https://github.com/kidleyh/smw-gs

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Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction

https://github.com/kinggugu/basrec

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Augmenting Sequential Recommendation with Balanced Relevance and Diversity

https://github.com/kinit-sk/Crowd-vs-ChatGPT-Crowdsourcing-of-paraphrases-for-intent-classification

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ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

https://github.com/kinit-sk/crowd-vs-gpt-intent-class

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ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness

https://github.com/kinit-sk/llm-div-incts

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Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation

https://github.com/kirill-vish/beyond-inet

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ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy

https://github.com/kiwi12138/realistictta

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Unraveling Batch Normalization for Realistic Test-Time Adaptation

https://github.com/kkakkkka/MambaTalk

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MambaTalk: Efficient Holistic Gesture Synthesis with Selective State Space Models

https://github.com/kkane99/MANGER_code

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Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

https://github.com/kkane99/manger

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Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

https://github.com/kkyuhun94/dalda

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DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance Scaling

https://github.com/klmyyaqihu/mmwchanmod-gan

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Multi-Frequency Channel Modeling for Millimeter Wave and THz Wireless Communication via Generative Adversarial Networks

https://github.com/kngwyu/infomax-option-critic

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Learning Diverse Options via InfoMax Termination Critic

https://github.com/kniter1/tailor

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Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition

https://github.com/knoveleng/rainbowplus

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RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity Search

https://github.com/komeijiforce/incubator

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Incubating Text Classifiers Following User Instruction with Nothing but LLM

https://github.com/kong996/ml_cbp_hz

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Analyzing the Habitable Zones of Circumbinary Planets Using Machine Learning

https://github.com/konglonggefdu/transfertod

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TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities

https://github.com/koninik/diffusionpen

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DiffusionPen: Towards Controlling the Style of Handwritten Text Generation

https://github.com/koninik/htg_evaluation

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Rethinking HTG Evaluation: Bridging Generation and Recognition

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