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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 8626–8650 of 9051 papers

TitleStatusHype
A Comparative Study of Question Answering over Knowledge BasesCode0
Annotator-Centric Active Learning for Subjective NLP TasksCode0
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language ModelsCode0
Multiple Word Embeddings for Increased Diversity of RepresentationCode0
Anytime Inference with Distilled Hierarchical Neural EnsemblesCode0
Python is Not Always the Best Choice: Embracing Multilingual Program of ThoughtsCode0
Annotating and Characterizing Clinical Sentences with Explicit Why-QA CuesCode0
Regularizing Variational Autoencoder with Diversity and Uncertainty AwarenessCode0
Fast and Practical Neural Architecture SearchCode0
Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium PhysicsCode0
SteinGen: Generating Fidelitous and Diverse Graph SamplesCode0
Step-wise Policy for Rare-tool Knowledge (SPaRK): Offline RL that Drives Diverse Tool Use in LLMsCode0
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing dataCode0
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake DetectionCode0
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model RobustnessCode0
STICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation using Neurons, Precise Timing, Delays, and SynchronyCode0
Unsupervised Neural Dialect Translation with Commonality and Diversity ModelingCode0
Multi-sided Exposure Bias in RecommendationCode0
Ankh: Optimized Protein Language Model Unlocks General-Purpose ModellingCode0
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
An Investigation of the (In)effectiveness of Counterfactually Augmented DataCode0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal TransportCode0
Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT SolversCode0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
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