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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 3301–3325 of 9051 papers

TitleStatusHype
Training Task Experts through Retrieval Based Distillation—0
UltraEdit: Instruction-based Fine-Grained Image Editing at ScaleCode0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
The Solution for Language-Enhanced Image New Category Discovery—0
Progress or Regress? Self-Improvement Reversal in Post-training—0
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors—0
Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling—0
VRSD: Rethinking Similarity and Diversity for Retrieval in Large Language Models—0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
A Survey of Data Synthesis ApproachesCode0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data—0
DiffRetouch: Using Diffusion to Retouch on the Shoulder of Experts—0
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning—0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
Reinforcement Learning for Sequence Design Leveraging Protein Language Models—0
Regurgitative Training: The Value of Real Data in Training Large Language Models—0
Advanced Framework for Animal Sound Classification With Features Optimization—0
Semantically Rich Local Dataset Generation for Explainable AI in GenomicsCode0
Towards a Scalable Reference-Free Evaluation of Generative ModelsCode0
Investigating the Effects of Large-Scale Pseudo-Stereo Data and Different Speech Foundation Model on Dialogue Generative Spoken Language Model—0
ValueScope: Unveiling Implicit Norms and Values via Return Potential Model of Social InteractionsCode0
Generative Monoculture in Large Language ModelsCode0
Pictures Of MIDI: Controlled Music Generation via Graphical Prompts for Image-Based Diffusion Inpainting—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
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