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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 1851–1875 of 9051 papers

TitleStatusHype
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy LossCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
Controlled Evaluation of Syntactic Knowledge in Multilingual Language ModelsCode0
Improving Language Generation with Sentence Coherence ObjectiveCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial RobustnessCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
Improving Ensemble Distillation With Weight Averaging and Diversifying PerturbationCode0
Improving Generalization with Domain Convex GameCode0
Improving Neural Language Modeling via Adversarial TrainingCode0
Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region FittingCode0
Inference of cell dynamics on perturbation data using adjoint sensitivityCode0
Complete 3D Scene Parsing from an RGBD ImageCode0
Conversation Graph: Data Augmentation, Training and Evaluation for Non-Deterministic Dialogue ManagementCode0
Conversing by Reading: Contentful Neural Conversation with On-demand Machine ReadingCode0
Convex Hull Approximation of Nearly Optimal Lasso SolutionsCode0
Improving Contextualized Topic Models with Negative SamplingCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Improving Adversarial Robustness via Decoupled Visual Representation MaskingCode0
Improving Computed Tomography (CT) Reconstruction via 3D Shape InductionCode0
Competition and Diversity in Generative AICode0
Improved Image Segmentation via Cost Minimization of Multiple HypothesesCode0
Improved Generalization of Weight Space Networks via AugmentationsCode0
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