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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 8501–8550 of 9051 papers

TitleStatusHype
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Diverse Prompts: Illuminating the Prompt Space of Large Language Models with MAP-Elites—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
XAGen: 3D Expressive Human Avatars Generation—0
Diverse Score Distillation—0
Compress Guidance in Conditional Diffusion Sampling—0
Diverse Shape Completion via Style Modulated Generative Adversarial Networks—0
Diverse Single Image Generation with Controllable Global Structure—0
Compressed Sensing with Approximate Priors via Conditional Resampling—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Table-to-Text Generation with Pretrained Diffusion Models—0
Diverse Video Captioning Through Latent Variable Expansion—0
Comprehensive Video Understanding: Video summarization with content-based video recommender design—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Diverse Yet Efficient Retrieval using Hash Functions—0
Diversidade linguística e inclusão digital: desafios para uma ia brasileira—0
Advanced Strategies of Portfolio Management in the Heston Market Model—0
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
TaCIE: Enhancing Instruction Comprehension in Large Language Models through Task-Centred Instruction Evolution—0
Comprehensive Annotation of Multiword Expressions in a Social Web Corpus—0
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning—0
Diversified Hidden Markov Models for Sequential Labeling—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Diversified Late Acceptance Search—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Diversified Mutual Learning for Deep Metric Learning—0
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Diversified Sampling Improves Scaling LLM inference—0
Diversified Texture Synthesis with Feed-forward Networks—0
Diversified Visual Attention Networks for Fine-Grained Object Classification—0
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience—0
Compositional Operators in Distributional Semantics—0
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images—0
Diversifying Agent's Behaviors in Interactive Decision Models—0
Diversifying AI: Towards Creative Chess with AlphaZero—0
Compositional Fine-Grained Low-Shot Learning—0
Diversifying Database Activity Monitoring with Bandits—0
Voice Conversion with Diverse Intonation using Conditional Variational Auto-Encoder—0
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning—0
Compositional diversity in visual concept learning—0
Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies—0
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition—0
Diversifying Multi-aspect Search Results Using Simpson's Diversity Index—0
Diversifying Neural Conversation Model with Maximal Marginal Relevance—0
Diversifying Neural Text Generation with Part-of-Speech Guided Softmax and Sampling—0
Diversifying Question Generation over Knowledge Base via External Natural Questions—0
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