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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/deezer/MusicGenreTranslation

공식 코드

Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation

https://github.com/degardinbruno/kinetic-gan

공식 코드

Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

https://github.com/dell-research-harvard/effocr

공식 코드

Efficient OCR for Building a Diverse Digital History

https://github.com/denisyarats/exorl

공식 코드

Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement Learning

https://github.com/densoitlab/featurepi

공식 코드

Feature Space Particle Inference for Neural Network Ensembles

https://github.com/deqiankong/spi

공식 코드

Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference

https://github.com/derekkk/llm4arggen

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Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument Generation

https://github.com/desire2020/Cooperative-Training

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CoT: Cooperative Training for Generative Modeling of Discrete Data

https://github.com/deskk/AbGPT

공식 코드

AbGPT: De Novo Antibody Design via Generative Language Modeling

https://github.com/dessertlab/robustness-of-ai-offensive-code-generators

공식 코드

Enhancing Robustness of AI Offensive Code Generators via Data Augmentation

https://github.com/destiny301/uefl

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Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos

https://github.com/dhfbk/variationist

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Variationist: Exploring Multifaceted Variation and Bias in Written Language Data

https://github.com/dhlab-epfl/dhSegment

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dhSegment: A generic deep-learning approach for document segmentation

https://github.com/diandyu/attribute_alignment

공식 코드

Attribute Alignment: Controlling Text Generation from Pre-trained Language Models

https://github.com/dice-group/MST5

공식 코드

MST5 -- Multilingual Question Answering over Knowledge Graphs

https://github.com/dice-group/hybridfc

공식 코드

HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs

https://github.com/dice-group/lola

공식 코드

LOLA -- An Open-Source Massively Multilingual Large Language Model

https://github.com/diol-unitn/hn-moea-im

공식 코드

Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms

https://github.com/diptakuet/mad-en-microarchitectural-attack-detection

공식 코드

MAD-EN: Microarchitectural Attack Detection through System-wide Energy Consumption

https://github.com/disl-lab/balancemix

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Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

https://github.com/div-lab/dendromap

공식 코드

DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps

https://github.com/diversemultilabelclassificationrules/corset

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Concise and interpretable multi-label rule sets

https://github.com/divmf/divmf

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Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation

https://github.com/dkoslicki/MinimizeBiologicalDiversity

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Finer Metagenomic Reconstruction via Biodiversity Optimization

https://github.com/dkoterwa/optimizing_bertopic

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Enhancing BERTopic with Intermediate Layer Representations

https://github.com/dkxocl/rosseta

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Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism

https://github.com/dl-model-recommend/model-trust

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Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks

https://github.com/dle666/r-cot

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R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal Models

https://github.com/dlr-mi/dati

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Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive Imitation

https://github.com/doctordream/mdia

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MDIA: A Benchmark for Multilingual Dialogue Generation in 46 Languages

https://github.com/dogabasaran/lesionmix

공식 코드

LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

https://github.com/dohyeoklee/SPQR

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SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning

https://github.com/dome272/paella

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A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces

https://github.com/donghao51/simmmdg

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SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

https://github.com/dongheehand/unsplash2K

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Noise Conditional Flow Model for Learning the Super-Resolution Space

https://github.com/dongliangchang/Mutual-Channel-Loss

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The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification

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