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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 3376–3400 of 9051 papers

TitleStatusHype
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
Complexity of Symbolic Representation in Working Memory of Transformer Correlates with the Complexity of a Task—0
Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation—0
Benchmarking Monocular 3D Dog Pose Estimation Using In-The-Wild Motion Capture Data—0
SciDMT: A Large-Scale Corpus for Detecting Scientific Mentions—0
A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments—0
Open Generative Large Language Models for Galician—0
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective—0
Data Collection and Labeling Techniques for Machine Learning—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images—0
Insect Identification in the Wild: The AMI DatasetCode0
TourLLM: Enhancing LLMs with Tourism KnowledgeCode0
Nash CoT: Multi-Path Inference with Preference EquilibriumCode0
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations—0
Top-Down Bayesian Posterior Sampling for Sum-Product Networks—0
HARE: HumAn pRiors, a key to small language model Efficiency—0
Fast uncovering of protein sequence diversity from structure—0
Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming—0
When Box Meets Graph Neural Network in Tag-aware RecommendationCode0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Problematic Tokens: Tokenizer Bias in Large Language ModelsCode0
GameVibe: A Multimodal Affective Game Corpus—0
Decomposed evaluations of geographic disparities in text-to-image models—0
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