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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 7851–7900 of 9051 papers

TitleStatusHype
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Transfer Learning by Modeling a Distribution over Policies—0
Ensemble Pruning via Margin Maximization—0
Enhanced Optimization with Composite Objectives and Novelty Pulsation—0
A database for face presentation attack using wax figure faces—0
Conversing by Reading: Contentful Neural Conversation with On-demand Machine ReadingCode0
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in VideoCode0
SParC: Cross-Domain Semantic Parsing in ContextCode0
Balanced Ranking with Diversity Constraints—0
Lattice-Based Transformer Encoder for Neural Machine Translation—0
Attributed Graph Clustering via Adaptive Graph ConvolutionCode0
Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasksCode0
Masked Non-Autoregressive Image Captioning—0
Push and Pull Search Embedded in an M2M Framework for Solving Constrained Multi-objective Optimization Problems—0
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Unsupervised Hierarchical Story Infilling—0
Annotating and Characterizing Clinical Sentences with Explicit Why-QA CuesCode0
``Caption'' as a Coherence Relation: Evidence and Implications—0
Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar—0
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data AugmentationCode0
Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection—0
Exploring Context and Visual Pattern of Relationship for Scene Graph Generation—0
Parallel Optimal Transport GAN—0
R3 Adversarial Network for Cross Model Face Recognition—0
Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network—0
MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition—0
Diversity-Inducing Policy Gradient: Using Maximum Mean Discrepancy to Find a Set of Diverse Policies—0
Minimum-Margin Active Learning—0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale AmbiguitiesCode1
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
Nyström landmark sampling and regularized Christoffel functions—0
GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial NetworksCode0
Semi-Implicit Generative Model—0
Spatial Evolutionary Generative Adversarial NetworksCode0
Semantic Diversity by Phonetics for Accurate and Robust Machine Translation—0
Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets—0
Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer SatisfactionCode0
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
High-Mobility Massive MIMO Communications:Doppler Compensation and Transmit Diversity—0
Mask-Guided Portrait Editing with Conditional GANs—0
Training language GANs from ScratchCode0
A COLD Approach to Generating Optimal Samples—0
Comparing and Combining Lexicase Selection and Novelty Search—0
Recurring Concept Meta-learning for Evolving Data Streams—0
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation—0
Explaining Machine Learning Classifiers through Diverse Counterfactual ExplanationsCode2
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