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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 5051–5075 of 9051 papers

TitleStatusHype
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks—0
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference—0
Batch Tournament Selection for Genetic Programming—0
Towards Generating Diverse Audio Captions via Adversarial Training—0
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks—0
Learning from Neighbors about a Changing State—0
Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training—0
Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes—0
Learning General World Models in a Handful of Reward-Free Deployments—0
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization—0
BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
Basic Formal Properties of A Relational Model of The Mathematical Theory of Evidence—0
Learning High-Resolution Domain-Specific Representations with a GAN Generator—0
Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation—0
Learning immune receptor representations with protein language models—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
Learning Compact and Robust Representations for Anomaly Detection—0
Learning in Sparse Rewards settings through Quality-Diversity algorithms—0
Learning Invariant Representation of Tasks for Robust Surgical State Estimation—0
Bandit Learning for Diversified Interactive Recommendation—0
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