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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 2301–2325 of 9051 papers

TitleStatusHype
A Gap in Time: The Challenge of Processing Heterogeneous IoT Data in Digitalized Buildings—0
CoinRobot: Generalized End-to-end Robotic Learning for Physical Intelligence—0
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues—0
Against Filter Bubbles: Diversified Music Recommendation via Weighted Hypergraph Embedding Learning—0
Coherent Visual Storytelling via Parallel Top-Down Visual and Topic Attention—0
Coherent FDA Radar: Transmitter and Receiver Design and Analysis—0
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies—0
Contrastive Learning for Image Complexity Representation—0
Diversified Texture Synthesis with Feed-forward Networks—0
Coherent Dialogue with Attention-based Language Models—0
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States—0
Coherent and Archimedean choice in general Banach spaces—0
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising—0
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels—0
A cross-study analysis of drug response prediction in cancer cell lines—0
Cognitive Learning-Aided Multi-Antenna Communications—0
CoFinDiff: Controllable Financial Diffusion Model for Time Series Generation—0
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
A Regression Framework for Predicting User's Next Location using Call Detail Records—0
AGAD: Adversarial Generative Anomaly Detection—0
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications—0
Co-existence of Micro, Pico and Atto Cells in Optical Wireless Communication—0
Coexistence of critical sensitivity and subcritical specificity can yield optimal population coding—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
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