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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 2076–2100 of 9051 papers

TitleStatusHype
TourLLM: Enhancing LLMs with Tourism KnowledgeCode0
Nash CoT: Multi-Path Inference with Preference EquilibriumCode0
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Top-Down Bayesian Posterior Sampling for Sum-Product Networks—0
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations—0
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
Insect Identification in the Wild: The AMI DatasetCode0
Can Go AIs be adversarially robust?Code2
GameVibe: A Multimodal Affective Game Corpus—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Fast uncovering of protein sequence diversity from structure—0
Scaling Efficient Masked Image Modeling on Large Remote Sensing DatasetCode2
Decomposed evaluations of geographic disparities in text-to-image models—0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
When Box Meets Graph Neural Network in Tag-aware RecommendationCode0
Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming—0
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers—0
1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis—0
HARE: HumAn pRiors, a key to small language model Efficiency—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
Concept-skill Transferability-based Data Selection for Large Vision-Language ModelsCode1
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