47 citations · 176 across the 19 of their papers we have counts for
3 papers · 1 filter
Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat, Yizhe Zhang, Jamie Lin +1
In this work, we tackle model efficiency by exploiting redundancy in the \textit{implicit structure} of the building blocks of convolutional neural networks. We start our analysis…
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel +2
Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that are frequently employed in popular efficient architectures can also result in negative activation values, w…
Learned Threshold Pruning
Kambiz Azarian, Yash Bhalgat, Jinwon Lee +1
This paper presents a novel differentiable method for unstructured weight pruning of deep neural networks. Our learned-threshold pruning (LTP) method learns per-layer thresholds vi…