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Found 15 papers, 3 papers with code
Understanding Neural Network Binarization with Forward and Backward Proximal Quantizers
In neural network binarization, BinaryConnect (BC) and its variants are considered the standard.
BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models
With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources.
Mathematical Challenges in Deep Learning
Deep models are dominating the artificial intelligence (AI) industry since the ImageNet challenge in 2012.
EuclidNets: An Alternative Operation for Efficient Inference of Deep Learning Models
With the advent of deep learning application on edge devices, researchers actively try to optimize their deployments on low-power and restricted memory devices.
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
We further propose a sign-scale decomposition design to enhance training efficiency and a low-variance random initialization strategy to improve the model's transfer learning performance.
Low-bit Shift Network for End-to-End Spoken Language Understanding
Deep neural networks (DNN) have achieved impressive success in multiple domains.
Deep Neural Networks pruning via the Structured Perspective Regularization
In Machine Learning, Artificial Neural Networks (ANNs) are a very powerful tool, broadly used in many applications.
$S^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast and energy efficient compared to conventional neural networks.
EuclidNets: Combining hardware and architecture design for Efficient Inference and Training
In order to deploy deep neural networks on edge devices, compressed (resource efficient) networks need to be developed.
S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Shift neural networks reduce computation complexity by removing expensive multiplication operations and quantizing continuous weights into low-bit discrete values, which are fast and energy-efficient compared to conventional neural networks.
Tensor train decompositions on recurrent networks
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 learning.
A Causal Direction Test for Heterogeneous Populations
Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data.
Importance of Data Loading Pipeline in Training Deep Neural Networks
Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate.
Random Bias Initialization Improves Quantized Training
Binary neural networks improve computationally efficiency of deep models with a large margin.
Random Bias Initialization Improving Binary Neural Network Training
Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently.
