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Diversity

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.

Papers

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Showing 2301–2325 of 9051 papers

TitleStatusHype
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine TranslationCode0
GFlowNets and variational inferenceCode0
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule GenerationCode0
Ankh: Optimized Protein Language Model Unlocks General-Purpose ModellingCode0
Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model EvaluationCode0
Bridging Information Gaps with Comprehensive Answers: Improving the Diversity and Informativeness of Follow-Up QuestionsCode0
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning AlgorithmsCode0
GiantHunter: Accurate detection of giant virus in metagenomic data using reinforcement-learning and Monte Carlo tree searchCode0
GenZSL: Generative Zero-Shot Learning Via Inductive Variational AutoencoderCode0
An Investigation of the (In)effectiveness of Counterfactually Augmented DataCode0
Topology-Preserved Human Reconstruction with DetailsCode0
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary AlgorithmsCode0
Generative Monoculture in Large Language ModelsCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component AnalysisCode0
Brain Tumor Synthetic Data Generation with Adaptive StyleGANsCode0
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language ModelingCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning ModelsCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUCode0
Generating Neural Networks with Neural NetworksCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
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