6 citations · 31 across the 13 of their papers we have counts for
5 papers · 1 filter
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Yihua Zhang, Ruisi Cai, Tianlong Chen +6
Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite t…
Neural Implicit Dictionary via Mixture-of-Expert Training
Peihao Wang, Zhiwen Fan, Tianlong Chen +1
Representing visual signals by coordinate-based deep fully-connected networks has been shown advantageous in fitting complex details and solving inverse problems than discrete grid…
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations
Tianlong Chen, Peihao Wang, Zhiwen Fan +1
Neural Radiance Field (NeRF) regresses a neural parameterized scene by differentially rendering multi-view images with ground-truth supervision. However, when interpolating novel v…
Training Your Sparse Neural Network Better with Any Mask
Ajay Jaiswal, Haoyu Ma, Tianlong Chen +2
Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable du…
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
Ziyu Jiang, Tianlong Chen, Ting Chen +1
Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes. That implies a tantalizing possibility of scal…