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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 4001–4050 of 9051 papers

TitleStatusHype
Single Domain Dynamic Generalization for Iris Presentation Attack Detection—0
Multi-factor Sequential Re-ranking with Perception-Aware Diversification—0
Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies—0
Enhancing biodiversity through intraspecific suppression in large ecosystems—0
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data—0
CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion DiffusionCode1
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training—0
Modeling the Q-Diversity in a Min-max Play Game for Robust OptimizationCode0
Boosting Human-Object Interaction Detection with Text-to-Image Diffusion ModelCode1
DiffCap: Exploring Continuous Diffusion on Image Captioning—0
Self-QA: Unsupervised Knowledge Guided Language Model AlignmentCode3
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis—0
Few-shot 3D Shape Generation—0
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments—0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time SeriesCode2
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction—0
Visualizing Linguistic Diversity of Text Datasets Synthesized by Large Language ModelsCode2
DMDD: A Large-Scale Dataset for Dataset Mentions Detection—0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
Learning Diverse Risk Preferences in Population-based Self-playCode1
Augmented Message Passing Stein Variational Gradient Descent—0
Improving Recommendation System Serendipity Through Lexicase Selection—0
Learning In-context Learning for Named Entity RecognitionCode1
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning—0
Use of Speech Impairment Severity for Dysarthric Speech Recognition—0
Confidence-Guided Semi-supervised Learning in Land Cover Classification—0
Bridging the Gap: Enhancing the Utility of Synthetic Data via Post-Processing Techniques—0
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Comparison of classifiers in challenge scheme—0
Fairness and Diversity in Information Access Systems—0
Dynamics of niche construction in adaptable populations evolving in diverse environmentsCode0
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarking—0
Private Training Set Inspection in MLaaS—0
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
DarkBERT: A Language Model for the Dark Side of the Internet—0
The Structure and Dynamics of Knowledge Graphs, with SuperficialityCode0
Zero-shot racially balanced dataset generation using an existing biased StyleGAN2Code0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Harvesting Event Schemas from Large Language ModelsCode1
Comparison of machine learning models applied on anonymized data with different techniques—0
Device-Robust Acoustic Scene Classification via Impulse Response AugmentationCode1
Deep Learning for Asynchronous Massive Access with Data Frame Length Diversity—0
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
Semantic uncertainty guides the extension of conventions to new referents—0
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
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