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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 3551–3575 of 9051 papers

TitleStatusHype
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Distributionally-Informed Recommender System Evaluation—0
Strong-Weak Integrated Semi-supervision for Unsupervised Single and Multi Target Domain Adaptation—0
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling—0
Unveiling the Complexity of Neural Populations: Evaluating the Validity and Limitations of the Wilson-Cowan Model—0
Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables—0
Balance Measures Derived from Insole Sensor Differentiate Prodromal Dementia with Lewy Bodies—0
Exploring the Law of Numbers: Evidence from China's Real Estate—0
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss—0
Does Writing with Language Models Reduce Content Diversity?Code0
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning—0
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining—0
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Sparse Codesigned Communication and Radar Systems—0
Unsupervised Multi-document Summarization with Holistic Inference—0
Score-PA: Score-based 3D Part AssemblyCode1
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers—0
Behind Recommender Systems: the Geography of the ACM RecSys CommunityCode0
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation—0
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
Projections of Economic Impacts of Climate Change on Marine Protected Areas: Palau, the Great Barrier Reef, and the Bering Sea—0
Bilevel Scheduled Sampling for Dialogue Generation—0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Aggregating Correlated Estimations with (Almost) no Training—0
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