6 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…
CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks
Xuan Rao, Bo Zhao, Derong Liu
In neural architecture search (NAS) methods based on latent space optimization (LSO), a deep generative model is trained to embed discrete neural architectures into a continuous la…