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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 4751–4800 of 9051 papers

TitleStatusHype
Probabilistic Regressor Chains with Monte Carlo Methods—0
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs—0
Probing Contextualized Sentence Representations with Visual Awareness—0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models—0
Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study—0
PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow—0
Program Translation via Code Distillation—0
Progress and open problems in evolutionary dynamics—0
Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval—0
Progressive growing of self-organized hierarchical representations for exploration—0
Progressive Mirror Detection—0
Progressive Multimodal Reasoning via Active Retrieval—0
Progressive Multi-view Human Mesh Recovery with Self-Supervision—0
Progressive Open-Domain Response Generation with Multiple Controllable Attributes—0
Progressive Random Convolutions for Single Domain Generalization—0
Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification—0
Progressive trajectory matching for medical dataset distillation—0
Progress or Regress? Self-Improvement Reversal in Post-training—0
Teuken-7B-Base & Teuken-7B-Instruct: Towards European LLMs—0
Progress towards quantitative design principles of multicellular systems—0
Projections of Economic Impacts of Climate Change on Marine Protected Areas: Palau, the Great Barrier Reef, and the Bering Sea—0
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system—0
Promoting Diversity for End-to-End Conversation Response Generation—0
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation—0
Promoting Semantics in Multi-objective Genetic Programming based on Decomposition—0
Promoting the linguistic diversity of TEI in the Maghreb and the Arab region—0
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting—0
PromptEVC: Controllable Emotional Voice Conversion with Natural Language Prompts—0
Prompting a Large Language Model to Generate Diverse Motivational Messages: A Comparison with Human-Written Messages—0
Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization—0
Prompting Audios Using Acoustic Properties For Emotion Representation—0
Prompting Diverse Ideas: Increasing AI Idea Variance—0
Prompting for a conversation: How to control a dialog model?—0
Prompting Towards Alleviating Code-Switched Data Scarcity in Under-Resourced Languages with GPT as a Pivot—0
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks—0
Prompt Selection and Augmentation for Few Examples Code Generation in Large Language Model and its Application in Robotics Control—0
Proof of Efficient Liquidity: A Staking Mechanism for Capital Efficient Liquidity—0
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy—0
Proportional Selection in Networks—0
Prosody-controllable spontaneous TTS with neural HMMs—0
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval—0
Protected group bias and stereotypes in Large Language Models—0
Protein residue networks from a local search perspective—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
Prototype Discovery using Quality-Diversity—0
Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning—0
Provable Hierarchy-Based Meta-Reinforcement Learning—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Provenance-Based Assessment of Plans in Context—0
Provenance-Based Interpretation of Multi-Agent Information Analysis—0
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