167 citations · 495 across the 52 of their papers we have counts for
12 papers · 1 filter
DeepLABNet: End-to-end Learning of Deep Radial Basis Networks with Fully Learnable Basis Functions
Andrew Hryniowski, Alexander Wong
From fully connected neural networks to convolutional neural networks, the learned parameters within a neural network have been primarily relegated to the linear parameters (e.g.,…
State of Compact Architecture Search For Deep Neural Networks
Mohammad Javad Shafiee, Andrew Hryniowski, Francis Li +2
The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge and mobile scenarios. Du…
Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift
Rene Bidart, Alexander Wong
In this study, we propose the Affine Variational Autoencoder (AVAE), a variant of Variational Autoencoder (VAE) designed to improve robustness by overcoming the inability of VAEs t…
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks
Zhong Qiu Lin, Alexander Wong
Much of the focus in the area of knowledge distillation has been on distilling knowledge from a larger teacher network to a smaller student network. However, there has been little…
PolyNeuron: Automatic Neuron Discovery via Learned Polyharmonic Spline Activations
Andrew Hryniowski, Alexander Wong
Automated deep neural network architecture design has received a significant amount of recent attention. However, this attention has not been equally shared by one of the fundament…
Dynamic Representations Toward Efficient Inference on Deep Neural Networks by Decision Gates
Mohammad Saeed Shafiee, Mohammad Javad Shafiee, Alexander Wong
While deep neural networks extract rich features from the input data, the current trade-off between depth and computational cost makes it difficult to adopt deep neural networks fo…