609 citations · 1.3k across the 11 of their papers we have counts for
31 papers
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
Yang Li, Si Si, Gang Li +2
Attentional mechanisms are order-invariant. Positional encoding is a crucial component to allow attention-based deep model architectures such as Transformer to address sequences or…
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang, Pierre Foret, Scott Yak +7
Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of pro…
Data Augmentation via Structured Adversarial Perturbations
Calvin Luo, Hossein Mobahi, Samy Bengio
Data augmentation is a major component of many machine learning methods with state-of-the-art performance. Common augmentation strategies work by drawing random samples from a spac…
Characterising Bias in Compressed Models
Sara Hooker, Nyalleng Moorosi, Gregory Clark +2
The popularity and widespread use of pruning and quantization is driven by the severe resource constraints of deploying deep neural networks to environments with strict latency, me…
Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders
Yang Li, Julien Amelot, Xin Zhou +2
It has been of increasing interest in the field to develop automatic machineries to facilitate the design process. In this paper, we focus on assisting graphical user interface (UI…
Fantastic Generalization Measures and Where to Find Them
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi +2
Generalization of deep networks has been of great interest in recent years, resulting in a number of theoretically and empirically motivated complexity measures. However, most pape…