306 citations · 2.2k across the 100 of their papers we have counts for
16 papers · 1 filter
Network Compression via Central Filter
Yuanzhi Duan, Xiaofang Hu, Yue Zhou +2
Neural network pruning has remarkable performance for reducing the complexity of deep network models. Recent network pruning methods usually focused on removing unimportant or redu…
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
Xingchao Liu, Chengyue Gong, Lemeng Wu +3
Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP…
Speeding up Deep Model Training by Sharing Weights and Then Unsharing
Shuo Yang, Le Hou, Xiaodan Song +2
We propose a simple and efficient approach for training the BERT model. Our approach exploits the special structure of BERT that contains a stack of repeated modules (i.e., transfo…
Conflict-Averse Gradient Descent for Multi-task Learning
Bo Liu, Xingchao Liu, Xiaojie Jin +2
The goal of multi-task learning is to enable more efficient learning than single task learning by sharing model structures for a diverse set of tasks. A standard multi-task learnin…
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference
Mao Ye, Qiang Liu
Bootstrap is a principled and powerful frequentist statistical tool for uncertainty quantification. Unfortunately, standard bootstrap methods are computationally intensive due to t…
Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set
Mao Ye, Qiang Liu
Many modern machine learning applications, such as multi-task learning, require finding optimal model parameters to trade-off multiple objective functions that may conflict with ea…