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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 3651–3675 of 9051 papers

TitleStatusHype
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
Exploring the Efficacy of Meta-Learning: Unveiling Superior Data Diversity Utilization of MAML Over Pre-training—0
Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons—0
Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task—0
Conceptual capacity and effective complexity of neural networks—0
FedShift: Tackling Dual Heterogeneity Problem of Federated Learning via Weight Shift Aggregation—0
A high fidelity synthetic face framework for computer vision—0
Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings—0
Feedback-based Approach to Introduce Freshness in Recommendations—0
Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out—0
Exploring the Design Space of Diffusion Bridge Models via Stochasticity Control—0
Exploring Textual Semantics Diversity for Image Transmission in Semantic Communication Systems using Visual Language Model—0
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Feint Behaviors and Strategies: Formalization, Implementation and Evaluation—0
Exploring Story Generation with Multi-task Objectives in Variational Autoencoders—0
Conceptors: an easy introduction—0
Few-shot 3D Shape Generation—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model—0
Concept-Monitor: Understanding DNN training through individual neurons—0
A Simulation Study of Bandit Algorithms to Address External Validity of Software Fault Prediction—0
A high-accuracy multi-model mixing retrosynthetic method—0
Active learning for interactive satellite image change detection—0
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