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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 2801–2825 of 9051 papers

TitleStatusHype
Diversity-Sensitive Conditional Generative Adversarial Networks—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
Diversity driven Query Rewriting in Search Advertising—0
A Novel BiLevel Paradigm for Image-to-Image Translation—0
Diversity waves in collapse-driven population dynamics—0
Can LLMs Simulate Human Behavioral Variability? A Case Study in the Phonemic Fluency Task—0
Canny2Palm: Realistic and Controllable Palmprint Generation for Large-scale Pre-training—0
DivGAN: Towards Diverse Paraphrase Generation via Diversified Generative Adversarial Network—0
Divide-and-Conquer for Lane-Aware Diverse Trajectory Prediction—0
Divide and Fuse: A Re-ranking Approach for Person Re-identification—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN—0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
Dizygotic Conditional Variational AutoEncoder for Multi-Modal and Partial Modality Absent Few-Shot Learning—0
DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness—0
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Energy Clustering for Unsupervised Person Re-identification—0
DMDD: A Large-Scale Dataset for Dataset Mentions Detection—0
Can the Transformer Be Used as a Drop-in Replacement for RNNs in Text-Generating GANs?—0
Energy-Efficient Design of Broad Beams for Massive MIMO Systems—0
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