SOTAVerified

Model Optimization

To Optimize already existing models in Training/Inferencing tasks.

Papers

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Showing 226–250 of 380 papers

TitleStatusHype
Vertical Federated Continual Learning via Evolving Prototype Knowledge—0
Video Analytics on IoT devices—0
Vision-language Assisted Attribute Learning—0
Visual Tactile Fusion Object Clustering—0
A keyword-driven approach to science—0
Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts—0
Optimizing Cox Models with Stochastic Gradient Descent: Theoretical Foundations and Practical Guidances—0
2D-Curri-DPO: Two-Dimensional Curriculum Learning for Direct Preference Optimization—0
3D Building Reconstruction From Monocular Remote Sensing Images—0
A Bridging Framework for Model Optimization and Deep Propagation—0
Acoustic Model Optimization Based On Evolutionary Stochastic Gradient Descent with Anchors for Automatic Speech Recognition—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing—0
Adaptive Variational Continual Learning via Task-Heuristic Modelling—0
A deep learning based solution for construction equipment detection: from development to deployment—0
A deep learning network with differentiable dynamic programming for retina OCT surface segmentation—0
AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection—0
AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework—0
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications—0
A Machine Learning-oriented Survey on Tiny Machine Learning—0
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset—0
An Embedding-Dynamic Approach to Self-supervised Learning—0
An empirical investigation into audio pipeline approaches for classifying bird species—0
A New Perspective for Understanding Generalization Gap of Deep Neural Networks Trained with Large Batch Sizes—0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
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