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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 3776–3800 of 9051 papers

TitleStatusHype
Angle diversity receiver as a key enabler for reliable ORIS-based Visible Light Communication—0
FORCE: Physics-aware Human-object Interaction—0
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals—0
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study—0
Forecasting high-dimensional dynamics exploiting suboptimal embeddings—0
Forecast with Forecasts: Diversity Matters—0
Foreign object segmentation in chest x-rays through anatomy-guided shape insertion—0
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning—0
Forgotten Knowledge: Examining the Citational Amnesia in NLP—0
Offline Diversity Maximization Under Imitation Constraints—0
Formalising lexical and syntactic diversity for data sampling in French—0
Forming Diverse Teams from Sequentially Arriving People—0
Corpus COFLA: A research corpus for the Computational study of Flamenco Music—0
Fostering digital representation of EU regional and minority languages: the Digital Language Diversity Project—0
Fostering Diversity in Spatial Evolutionary Generative Adversarial Networks—0
Attentive Aspect Modeling for Review-aware Recommendation—0
A COLD Approach to Generating Optimal Samples—0
Learning temporal relationships between symbols with Laplace Neural Manifolds—0
Geodesic-HOF: 3D Reconstruction Without Cutting Corners—0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
Diverse Neural Network Learns True Target Functions—0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
Boosting Dialog Response Generation—0
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