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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 1326–1350 of 9051 papers

TitleStatusHype
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
Exploring structure diversity in atomic resolution microscopy with graph neural networks—0
Towards a Similarity-adjusted Surprisal Theory—0
Quantifying the Risks of Tool-assisted Rephrasing to Linguistic Diversity—0
Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks—0
R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal ModelsCode5
Differentially Private Learning Needs Better Model Initialization and Self-DistillationCode0
Scaling Robot Policy Learning via Zero-Shot Labeling with Foundation Models—0
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
Deep Generative Models for 3D Medical Image Synthesis—0
AutoRNet: Automatically Optimizing Heuristics for Robust Network Design via Large Language Models—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
Annotation-Free MIDI-to-Audio Synthesis via Concatenative Synthesis and Generative Refinement—0
Audio-to-Score Conversion Model Based on Whisper methodology—0
Convex Markov Games: A New Frontier for Multi-Agent Reinforcement Learning—0
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning—0
MiniPLM: Knowledge Distillation for Pre-Training Language ModelsCode2
Test-time Adaptation for Cross-modal Retrieval with Query Shift—0
Are Large-scale Soft Labels Necessary for Large-scale Dataset Distillation?Code1
A Paradigm Shift in Mouza Map Vectorization: A Human-Machine Collaboration Approach—0
ComPO: Community Preferences for Language Model Personalization—0
Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery—0
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