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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 3501–3525 of 9051 papers

TitleStatusHype
Achieving Distributed MIMO Performance with Repeater-Assisted Cellular Massive MIMO—0
Typography Leads Semantic Diversifying: Amplifying Adversarial Transferability across Multimodal Large Language Models—0
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs—0
ROAST: Review-level Opinion Aspect Sentiment Target Joint Detection for ABSACode0
PostDoc: Generating Poster from a Long Multimodal Document Using Deep Submodular Optimization—0
PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering—0
EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision—0
GIST: Greedy Independent Set Thresholding for Diverse Data Summarization—0
Towards Open Domain Text-Driven Synthesis of Multi-Person Motions—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
Large Language Models as Partners in Student Essay Evaluation—0
Neutral phylogenetic models and their role in tree-based biodiversity measures—0
RAGSys: Item-Cold-Start Recommender as RAG System—0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle—0
Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark—0
Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning—0
Multilingual Diversity Improves Vision-Language Representations—0
Benchmarking General-Purpose In-Context Learning—0
Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling—0
Enhancing Feature Diversity Boosts Channel-Adaptive Vision TransformersCode0
Aperture Selection for CAP Arrays (CAPAs)—0
Pessimistic Backward Policy for GFlowNetsCode0
Towards a Probabilistic Fusion Approach for Robust Battery Prognostics—0
Randomized heuristic repair for large-scale multidimensional knapsack problem—0
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search—0
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