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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 926–950 of 9051 papers

TitleStatusHype
PQD: Post-training Quantization for Efficient Diffusion Models—0
Intrinsic meaning, perception, and matching—0
A Large-Scale Study on Video Action Dataset CondensationCode1
QUBE: Enhancing Automatic Heuristic Design via Quality-Uncertainty Balanced EvolutionCode0
Exploring and Controlling Diversity in LLM-Agent Conversation—0
Toward Intelligent and Secure Cloud: Large Language Model Empowered Proactive DefenseCode1
Enhancing Annotated Bibliography Generation with LLM Ensembles—0
The Synergy of Automated Pipelines with Prompt Engineering and Generative AI in Web Crawling—0
No Preference Left Behind: Group Distributional Preference OptimizationCode1
Revealing the Shape of Genome Space via K-mer TopologyCode0
Multi-scale Latent Point Consistency Models for 3D Shape Generation—0
Diverse Rare Sample Generation with Pretrained GANsCode0
Focusing Image Generation to Mitigate Spurious Correlations—0
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task SynthesisCode3
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process—0
RecLM: Recommendation Instruction TuningCode2
UniAvatar: Taming Lifelike Audio-Driven Talking Head Generation with Comprehensive Motion and Lighting Control—0
Experimental Study of RCS Diversity with Novel No-divergent OAM Beams—0
Enhanced Recommendation Combining Collaborative Filtering and Large Language Models—0
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration PathCode1
Diverse and Effective Red Teaming with Auto-generated Rewards and Multi-step Reinforcement Learning—0
Dissecting CLIP: Decomposition with a Schur Complement-based ApproachCode0
DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation—0
Survey of Pseudonymization, Abstractive Summarization & Spell Checker for Hindi and Marathi—0
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