6 citations · 10 across the 5 of their papers we have counts for
5 papers
Deep Transformers Thirst for Comprehensive-Frequency Data
Rui Xia, Chao Xue, Boyu Deng +2
Current researches indicate that inductive bias (IB) can improve Vision Transformer (ViT) performance. However, they introduce a pyramid structure concurrently to counteract the in…
Multi-Label Few-Shot Learning for Aspect Category Detection
Mengting Hu, Shiwan Zhao, Honglei Guo +5
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence. In this paper, we formulate ACD in the few-shot learning scena…
Automatic low-bit hybrid quantization of neural networks through meta learning
Tao Wang, Junsong Wang, Chang Xu +1
Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference, especially when deploying to edge or IoT devices with limited computat…
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Sivan Doveh, Eli Schwartz, Chao Xue +4
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typically, in FSL a model is trained on a dataset consisting of many small tasks (meta-tasks) and learns to adapt to…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…