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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 3976–4000 of 9051 papers

TitleStatusHype
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models—0
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution—0
Allowing for equal opportunities for artists in music recommendation—0
A unified framework based on graph consensus term for multi-view learning—0
Generating Molecular Conformations via Normalizing Flows and Neural ODEs—0
Generating Multiple Diverse Responses for Short-Text Conversation—0
Cuid: A new study of perceived image quality and its subjective assessment—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Diverse Image Style Transfer via Invertible Cross-Space Mapping—0
BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis—0
ACO for Continuous Function Optimization: A Performance Analysis—0
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation—0
Generating Responses with a Specific Emotion in Dialog—0
Informed Sampling for Diversity in Concept-to-Text NLG—0
Cultivating DNN Diversity for Large Scale Video Labelling—0
Diverse Imagenet Models Transfer Better—0
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers—0
Semantic and Expressive Variation in Image Captions Across Languages—0
Generating Synthetic Net Load Data with Physics-informed Diffusion Model—0
Generation of Multimedia Artifacts: An Extractive Summarization-based Approach—0
Block-Wise MAP Inference for Determinantal Point Processes with Application to Change-Point Detection—0
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks—0
Generative Adversarial Networks for Unsupervised Object Co-localization—0
An Exhaustive Evaluation of TTS- and VC-based Data Augmentation for ASR—0
A Co-analysis Framework for Exploring Multivariate Scientific Data—0
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