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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 2976–3000 of 9051 papers

TitleStatusHype
Chain of LoRA: Efficient Fine-tuning of Language Models via Residual Learning—0
We Need to Talk About Classification Evaluation Metrics in NLP—0
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs—0
Improved motif-scaffolding with SE(3) flow matchingCode3
A multimodal gesture recognition dataset for desktop human-computer interaction—0
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting—0
FurniScene: A Large-scale 3D Room Dataset with Intricate Furnishing Scenes—0
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement LearningCode0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)—0
From Audio to Photoreal Embodiment: Synthesizing Humans in ConversationsCode7
Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation—0
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review—0
Few-shot Image Generation via Information Transfer from the Built Geodesic Surface—0
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation—0
GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction—0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation—0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
HomoFormer: Homogenized Transformer for Image Shadow RemovalCode1
Class Incremental Learning with Multi-Teacher DistillationCode0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
Motion Diversification Networks—0
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