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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 7176–7200 of 9051 papers

TitleStatusHype
Affinity-based extension of non-extensive entropy and statistical mechanics—0
ON NEURAL NETWORK GENERALIZATION VIA PROMOTING WITHIN-LAYER ACTIVATION DIVERSITY—0
On Nondeterminism and Instability in Optimizing Neural Networks—0
On Nondeterminism and Instability in Neural Network Optimization—0
On Paraphrase Identification Corpora—0
On Performance of Fluid Antenna System using Maximum Ratio Combining—0
On properties driving diversity index selection—0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
On Sampling and Greedy MAP Inference of Constrained Determinantal Point Processes—0
On Stein Variational Neural Network Ensembles—0
On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective—0
The quest for the definition of life—0
On the Arithmetic and Geometric Fusion of Beliefs for Distributed Inference—0
On the clustering of correlated random variables—0
On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks—0
On the Computational Tractability of the (Many) Shapley Values—0
On the Connection between Invariant Learning and Adversarial Training for Out-of-Distribution Generalization—0
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness—0
On the Diversity and Coded Modulation Design of Fluid Antenna Systems—0
On the Diversity and Limits of Human Explanations—0
On the Diversity and Limits of Human Explanations—0
On the Diversity of Synthetic Data and its Impact on Training Large Language Models—0
On the Dynamics of Mating Preferences in Genetic Programming—0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
On the Effectiveness of Genetic Operations in Symbolic Regression—0
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