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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 8051–8100 of 9051 papers

TitleStatusHype
Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks—0
Cooperation guides evolution in a minimal model of biological evolution—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning in the Open World—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects—0
Deep Architectures and Ensembles for Semantic Video Classification—0
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting—0
Deep Billboards towards Lossless Real2Sim in Virtual Reality—0
Convolutional autoencoder-based multimodal one-class classification—0
Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images—0
Deep Concept Identification for Generative Design—0
Deep Convolutional Compression for Massive MIMO CSI Feedback—0
VisualToolAgent (VisTA): A Reinforcement Learning Framework for Visual Tool Selection—0
Deep Delay Loop Reservoir Computing for Specific Emitter Identification—0
Deep Determinantal Point Processes—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System—0
Conversational AI-Powered Design: ChatGPT as Designer, User, and Product—0
Deep Ensemble Collaborative Learning by using Knowledge-transfer Graph for Fine-grained Object Classification—0
Deep Ensemble Policy Learning—0
Deep Ensembles for Low-Data Transfer Learning—0
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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