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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 326–350 of 9051 papers

TitleStatusHype
RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary Quality-Diversity SearchCode1
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language ModelsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
CamContextI2V: Context-aware Controllable Video GenerationCode1
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
A Doubly Decoupled Network for edge detectionCode1
The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in GamesCode1
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-TrainingCode1
Unseen from Seen: Rewriting Observation-Instruction Using Foundation Models for Augmenting Vision-Language NavigationCode1
Unraveling the Effects of Synthetic Data on End-to-End Autonomous DrivingCode1
Probabilistic Prompt Distribution Learning for Animal Pose EstimationCode1
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level AlignmentCode1
Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identificationCode1
Oasis: One Image is All You Need for Multimodal Instruction Data SynthesisCode1
Process-Supervised LLM Recommenders via Flow-guided TuningCode1
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
Recent Advances on Generalizable Diffusion-generated Image DetectionCode1
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference OptimizationCode1
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable MetricCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree SearchCode1
Diverse Topology Optimization using Modulated Neural FieldsCode1
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval AlgorithmsCode1
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