SOTAVerified

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 6076–6100 of 9051 papers

TitleStatusHype
OmniEval: A Benchmark for Evaluating Omni-modal Models with Visual, Auditory, and Textual Inputs—0
OmniMotionGPT: Animal Motion Generation with Limited Data—0
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB—0
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition—0
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning—0
On Accurate Evaluation of GANs for Language Generation—0
On a Scale-Invariant Approach to Bundle Recommendations in Candy Crush Saga—0
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity—0
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models—0
On Decoding Strategies for Neural Text Generators—0
On-demand Quantization for Green Federated Generative Diffusion in Mobile Edge Networks—0
A Sample Selection Approach for Universal Domain Adaptation—0
On Diverse Asynchronous Activity Anticipation—0
On Diversity in Discriminative Neural Networks—0
On Domain-Specific Post-Training for Multimodal Large Language Models—0
A Rule for Committee Selection with Soft Diversity Constraints—0
Where Are We? Evaluating LLM Performance on African Languages—0
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?—0
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition—0
One Model for All Quantization: A Quantized Network Supporting Hot-Swap Bit-Width Adjustment—0
Uncertainty And Evolutionary Optimization: A Novel Approach—0
One-Pot Multi-Frame Denoising—0
ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation—0
One-Shot Generative Domain Adaptation—0
One-shot Imitation in a Non-Stationary Environment via Multi-Modal Skill—0
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