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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 1451–1475 of 9051 papers

TitleStatusHype
Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS—0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
Diversity and Inclusion Index with Networks and Similarity: Analysis and its Application—0
Does RoBERTa Perform Better than BERT in Continual Learning: An Attention Sink PerspectiveCode0
Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models—0
Quality Diversity Imitation Learning—0
Diversity-Rewarded CFG Distillation—0
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Fill In The Gaps: Model Calibration and Generalization with Synthetic Data—0
Task Diversity Shortens the ICL PlateauCode0
Presto! Distilling Steps and Layers for Accelerating Music Generation—0
Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine—0
Studying and Mitigating Biases in Sign Language Understanding Models—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
Dynamic Post-Hoc Neural Ensemblers—0
Decoding MIE: A Novel Dataset Approach Using Topic Extraction and Affiliation ParsingCode0
Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training—0
Improving Distribution Alignment with Diversity-based Sampling—0
Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online TrollingCode0
Large Language Models can Achieve Social Balance—0
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion ModelsCode0
Can Language Models Reason about Individualistic Human Values and Preferences?—0
LLM-TOPLA: Efficient LLM Ensemble by Maximising DiversityCode0
Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model—0
Text-guided Diffusion Model for 3D Molecule Generation—0
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