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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 4351–4375 of 9051 papers

TitleStatusHype
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation—0
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems—0
LLMs and Finetuning: Benchmarking cross-domain performance for hate speech detection—0
Exploring Data Augmentations on Self-/Semi-/Fully- Supervised Pre-trained Models—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities—0
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge DistillationCode0
Techniques for supercharging academic writing with generative AI—0
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
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
Graph Convolutional Networks for Complex Traffic Scenario Classification—0
S^3-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation—0
From Pointwise to Powerhouse: Initialising Neural Networks with Generative Models—0
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting—0
Diversity Enhanced Narrative Question Generation for StorybooksCode0
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
MCC-KD: Multi-CoT Consistent Knowledge DistillationCode0
We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic FieldsCode0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
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