5 papers
UniMixer: A Unified Architecture for Scaling Laws in Recommendation Systems
Mingming Ha, Guanchen Wang, Linxun Chen +9
In recent years, the scaling laws of recommendation models have attracted increasing attention, which govern the relationship between performance and parameters/FLOPs of recommende…
Scalable Analytic Classifiers with Associative Drift Compensation for Class-Incremental Learning of Vision Transformers
Xuan Rao, Mingming Ha, Bo Zhao +2
Class-incremental learning (CIL) with Vision Transformers (ViTs) faces a major computational bottleneck during the classifier reconstruction phase, where most existing methods rely…
Compensating Distribution Drifts in Class-incremental Learning of Pre-trained Vision Transformers
Xuan Rao, Simian Xu, Zheng Li +4
Recent advances have shown that sequential fine-tuning (SeqFT) of pre-trained vision transformers (ViTs), followed by classifier refinement using approximate distributions of class…
DNAD: Differentiable Neural Architecture Distillation
Xuan Rao, Bo Zhao, Derong Liu
To meet the demand for designing efficient neural networks with appropriate trade-offs between model performance (e.g., classification accuracy) and computational complexity, the d…
FX-DARTS: Designing Topology-unconstrained Architectures with Differentiable Architecture Search and Entropy-based Super-network Shrinking
Xuan Rao, Bo Zhao, Derong Liu +1
Strong priors are imposed on the search space of Differentiable Architecture Search (DARTS), such that cells of the same type share the same topological structure and each intermed…