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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 4051–4075 of 9051 papers

TitleStatusHype
Array-Informed Waveform Design for Active Sensing: Diversity, Redundancy, and Identifiability—0
Multi-Robot Coordination and Layout Design for Automated WarehousingCode1
Template-based eukaryotic genome editing directed by SviCas3—0
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era—0
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackersCode1
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
On the Limitations of Model Stealing with Uncertainty Quantification Models—0
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication—0
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues—0
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection—0
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system—0
Creative Discovery using QD SearchCode0
Reinforcement Learning for Topic ModelsCode0
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning—0
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized DiversityCode1
Exploring One-shot Semi-supervised Federated Learning with A Pre-trained Diffusion Model—0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human SupervisionCode3
Experimental Validation of Coherent Joint Transmission in a Distributed-MIMO System with Analog Fronthaul for 6G—0
LayoutDM: Transformer-based Diffusion Model for Layout Generation—0
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization—0
An Asynchronous Updating Reinforcement Learning Framework for Task-oriented Dialog SystemCode0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
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