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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 3326–3350 of 9051 papers

TitleStatusHype
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models—0
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
TarGEN: Targeted Data Generation with Large Language ModelsCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single ImageCode2
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge DistillationCode0
Semantic Generative Augmentations for Few-Shot CountingCode1
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
Techniques for supercharging academic writing with generative AI—0
Graph Convolutional Networks for Complex Traffic Scenario Classification—0
Generative Fractional Diffusion ModelsCode1
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting—0
S^3-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation—0
Diversity Enhanced Narrative Question Generation for StorybooksCode0
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models—0
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
BLESS: Benchmarking Large Language Models on Sentence SimplificationCode0
What Makes it Ok to Set a Fire? Iterative Self-distillation of Contexts and Rationales for Disambiguating Defeasible Social and Moral Situations—0
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
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