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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 5926–5950 of 9051 papers

TitleStatusHype
TriNER: A Series of Named Entity Recognition Models For Hindi, Bengali & Marathi—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
MM-Mixing: Multi-Modal Mixing Alignment for 3D Understanding—0
TroubleLLM: Align to Red Team Expert—0
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness—0
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population—0
TRUNet: Transformer-Recurrent-U Network for Multi-channel Reverberant Sound Source Separation—0
TrustAL: Trustworthy Active Learning using Knowledge Distillation—0
TrustMol: Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics—0
Trying AGAIN instead of Trying Longer: Prior Learning for Automatic Curriculum Learning—0
TSMD: A Database for Static Color Mesh Quality Assessment Study—0
TSO: Self-Training with Scaled Preference Optimization—0
Tuning-Free Amodal Segmentation via the Occlusion-Free Bias of Inpainting Models—0
Tuning-Free Long Video Generation via Global-Local Collaborative Diffusion—0
Turbo-ICL: In-Context Learning-Based Turbo Equalization—0
Turbo-Sim: a generalised generative model with a physical latent space—0
Turning the Tide: Repository-based Code Reflection—0
Position: AI/ML Influencers Have a Place in the Academic Process—0
Twenty Years of Confusion in Human Evaluation: NLG Needs Evaluation Sheets and Standardised Definitions—0
Twitter Bot Detection using Diversity Measures—0
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Two Kinds of Recall—0
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems—0
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling—0
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