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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 1151–1200 of 9051 papers

TitleStatusHype
Biological Sequence Design with GFlowNetsCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Diverse Video Generation using a Gaussian Process TriggerCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Diversified Batch Selection for Training AccelerationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Diversity-Aware Meta Visual PromptingCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Diversity-Measurable Anomaly DetectionCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI ReconstructionCode1
DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arraysCode1
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping DatasetsCode1
Domain-Smoothing Network for Zero-Shot Sketch-Based Image RetrievalCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
DreamDA: Generative Data Augmentation with Diffusion ModelsCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI DataCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
BlendX: Complex Multi-Intent Detection with Blended PatternsCode1
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Multi-Interest Framework for RecommendationCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Contrastive Syn-to-Real GeneralizationCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
BLEU might be Guilty but References are not InnocentCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
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