Results 41 to 50 of about 1,532,152 (301)
Convolutional deep rectifier neural nets for phone recognition [PDF]
Interspeech 2013, 2013 Rectifier neurons differ from standard ones only in that the sigmoid activation function is replaced by the rectifier function, max(0, x). Several recent studies suggest that rectifier units may be more suitable building units for deep nets. For example, we found that with deep rectifier networks one can attain a similar speech recognition performance ...
openaire +2 more sources
Efficient Training of Convolutional Neural Nets on Large Distributed Systems [PDF]
2018 IEEE International Conference on Cluster Computing (CLUSTER), 2018 Deep Neural Networks (DNNs) have achieved im- pressive accuracy in many application domains including im- age classification. Training of DNNs is an extremely compute- intensive process and is solved using variants of the stochastic gradient descent (SGD) algorithm. A lot of recent research has focussed on improving the performance of DNN training.
Dheeraj Sreedhar +4 more
openaire +2 more sources
Two Algebraic Process Semantics for Contextual Nets
, 2001 We show that the so-called 'Petri nets are monoids' approach initiated by Meseguer and Montanari can be extended from ordinary place/transition Petri nets to contextual nets by considering suitable non-free monoids of places.
SASSONE V. +5 more
core +1 more source
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Generalizable and efficient cross‐domain person re‐identification model using deep metric learning
IET Computer Vision, 2023 Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets.
Saba Sadat Faghih Imani +2 more
doaj +1 more source
Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks
Entropy, 2021 Paraphrase generation is an important yet challenging task in natural language processing. Neural network-based approaches have achieved remarkable success in sequence-to-sequence learning.
Xiaoqiang Chi, Yang Xiang
doaj +1 more source
Facial Emotions Recognition using Convolutional Neural Net
CoRR, 2020 Facial expressions vary from person to person, and the brightness, contrast, and resolution of every random image are different. This is why recognizing facial expressions is very difficult. This article proposes an efficient system for facial emotion recognition for the seven basic human emotions (angry, disgust, fear, happy, sad, surprise, and ...
openaire +3 more sources
Artificial Neural Nets with Interaction of Afferents [PDF]
, 2011 The aim is to obtain computationally more powerful, neuro physiologically founded, artificial neurons and neural nets. Artificial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1 ...
Blasio, Gabriel de +2 more
core
Arthritis Care &Research, Accepted Article.Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein +3 more
wiley +1 more source
SACNet: Shuffling atrous convolutional U‐Net for medical image segmentation
IET Image Processing, 2023 Medical images exhibit multi‐granularity and high obscurity along boundaries. As representative work, the U‐Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or
Shaofan Wang +3 more
doaj +1 more source