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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 5751–5775 of 9051 papers

TitleStatusHype
This Person (Probably) Exists. Identity Membership Attacks Against GAN Generated Faces—0
THOR: Text to Human-Object Interaction Diffusion via Relation Intervention—0
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs—0
Throughput Optimization in Cache-aided Networks: An Opportunistic Probing and Scheduling Approach—0
Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions—0
Tighter Performance Theory of FedExProx—0
TIGTEC : Token Importance Guided TExt Counterfactuals—0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection—0
Time-Modulated Intelligent Reflecting Surface for Waveform Security—0
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection—0
Time Series Forecasting With Deep Learning: A Survey—0
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram—0
Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots—0
Tit for Tattling: Cooperation, communication, and how each could stabilize the other—0
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation—0
Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities—0
To Bin or not to Bin: Alternative Representations of Mass Spectra—0
Toddler-Inspired Visual Object Learning—0
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation—0
ToDRE: Visual Token Pruning via Diversity and Task Awareness for Efficient Large Vision-Language Models—0
Token-Importance Guided Direct Preference Optimization—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis—0
Don't Change Me! User-Controllable Selective Paraphrase Generation—0
Top-Down Bayesian Posterior Sampling for Sum-Product Networks—0
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