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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 8076–8100 of 9051 papers

TitleStatusHype
Visual Variational Autoencoder Prompt Tuning—0
Surrogate Fitness Metrics for Interpretable Reinforcement Learning—0
Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19—0
Convergence and Diversity in the Control Hierarchy—0
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks—0
Convergence analysis of OT-Flow for sample generation—0
Deep Gait Tracking With Inertial Measurement Unit—0
DeepGen: Diverse Search Ad Generation and Real-Time Customization—0
Deep Generative Inpainting with Comparative Sample Augmentation—0
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models—0
Deep Generative Modelling of Human Reach-and-Place Action—0
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering—0
Deep Generative Models for 3D Medical Image Synthesis—0
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN—0
ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model—0
Surveying Facial Recognition Models for Diverse Indian Demographics: A Comparative Analysis on LFW and Custom Dataset—0
Deep Hierarchical-Hyperspherical Learning (DH^2L)—0
ControlMath: Controllable Data Generation Promotes Math Generalist Models—0
DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection—0
DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection—0
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density—0
Deep Incomplete Multi-View Multiple Clusterings—0
Deep Internal Learning: Deep Learning from a Single Input—0
Deep Latent-Variable Models for Text Generation—0
Deep Leaning-Based Ultra-Fast Stair Detection—0
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