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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 8951–8975 of 9051 papers

TitleStatusHype
Surrogate-Assisted Reference Vector Adaptation to Various Pareto Front Shapes for Many-Objective Bayesian OptimizationCode0
XCAT-3.0: A Comprehensive Library of Personalized Digital Twins Derived from CT ScansCode0
An Automated Ensemble Learning Framework Using Genetic Programming for Image ClassificationCode0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
On the Diversity of Realistic Image SynthesisCode0
Deep Reinforcement Learning for Dialogue GenerationCode0
RORA: Robust Free-Text Rationale EvaluationCode0
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
A Unified Substrate for Body-Brain Co-evolutionCode0
EuLearn: A 3D database for learning Euler characteristicsCode0
Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining DatasetsCode0
ETS: Efficient Tree Search for Inference-Time ScalingCode0
On the Encoder-Decoder Incompatibility in Variational Text Modeling and BeyondCode0
Wild Face Anti-Spoofing Challenge 2023: Benchmark and ResultsCode0
On the Evaluation of Conditional GANsCode0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
UltraEdit: Instruction-based Fine-Grained Image Editing at ScaleCode0
On-the-fly Denoising for Data Augmentation in Natural Language UnderstandingCode0
DeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningCode0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Deep Metric Learning with BIER: Boosting Independent Embeddings RobustlyCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Semi-Discriminative Representation Loss for Online Continual LearningCode0
On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBsCode0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
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