11 citations · 16 across the 13 of their papers we have counts for
6 papers · 1 filter
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
Yiwei Lu, Yaoliang Yu, Xinlin Li +1
In neural network binarization, BinaryConnect (BC) and its variants are considered the standard. These methods apply the sign function in their forward pass and their respective gr…
Mathematical Challenges in Deep Learning
Vahid Partovi Nia, Guojun Zhang, Ivan Kobyzev +8
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012. The size of deep models is increasing ever since, which brings new challe…
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
Xinlin Li, Mariana Parazeres, Adam Oberman +3
With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices. There are establish…
Deep Neural Networks pruning via the Structured Perspective Regularization
Matteo Cacciola, Antonio Frangioni, Xinlin Li +1
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications. Often, the selected (deep) architectures include many layers, an…
Tensor train decompositions on recurrent networks
Alejandro Murua, Ramchalam Ramakrishnan, Xinlin Li +2
Recurrent neural networks (RNN) such as long-short-term memory (LSTM) networks are essential in a multitude of daily live tasks such as speech, language, video, and multimodal lear…
Random Bias Initialization Improves Quantized Training
Xinlin Li, Vahid Partovi Nia
Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful full-precision training…