Results 11 to 20 of about 212,856 (334)
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Network pruning is an important research field aiming at reducing computational costs of neural networks. Conventional approaches follow a fixed paradigm which first trains a large and redundant network, and then determines which units (e.g., channels) are less important and thus can be removed.
Wang, Yulong +6 more
openaire +6 more sources
The Itch-Scratch Cycle: A Review of the Mechanisms
Dermatology Practical & Conceptual, 2019 Background: Despite being one of the most common presenting dermatological symptoms, itching continues to perplex health care professionals because it is notoriously difficult to control.
Giulia Rinaldi
doaj +2 more sources
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet [PDF]
IEEE International Conference on Computer Vision, 2021 Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then
Li Yuan +7 more
semanticscholar +1 more source
LitterBox: A Linter for Scratch Programs [PDF]
2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 2021 Creating programs with block-based programming languages like Scratch is easy and fun. Block-based programs can nevertheless contain bugs, in particular when learners have misconceptions about programming.
G. Fraser +4 more
semanticscholar +1 more source
A new lightweight deep neural network for surface scratch detection
The International Journal of Advanced Manufacturing Technology, 2022 This paper aims to develop a lightweight convolutional neural network, WearNet, to realise automatic scratch detection for components in contact sliding such as those in metal forming.
Wei Li +4 more
semanticscholar +1 more source
Time series classification from scratch with deep neural networks: A strong baseline [PDF]
IEEE International Joint Conference on Neural Network, 2016 We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting.
Zhiguang Wang, Weizhong Yan, T. Oates
semanticscholar +1 more source
DSOD: Learning Deeply Supervised Object Detectors from Scratch [PDF]
IEEE International Conference on Computer Vision, 2017 We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off the-shelf networks pre-trained on large-scale classification datasets like Image Net,
Zhiqiang Shen +5 more
semanticscholar +1 more source
Universal Dependency Parsing from Scratch [PDF]
Conference on Computational Natural Language Learning, 2019 This paper describes Stanford’s system at the CoNLL 2018 UD Shared Task. We introduce a complete neural pipeline system that takes raw text as input, and performs all tasks required by the shared task, ranging from tokenization and sentence segmentation,
Peng Qi +3 more
semanticscholar +1 more source
An image J plugin for the high throughput image analysis of in vitro scratch wound healing assays
bioRxiv, 2020 In vitro scratch wound healing assay, a simple and low-cost technique that works along with other image analysis tools, is one of the most widely used 2D methods to determine the cellular migration and proliferation in processes such as regeneration and ...
Alejandra Suarez-Arnedo +5 more
semanticscholar +1 more source
De Computis, 2021 The use of information and communications technologies (ICTs) has emerged as an educational response amidst the COVID-19 pandemic, providing students the technological tools that enable them to acquire or strengthen the necessary digital skills to ...
Hugo Montiel +1 more
semanticscholar +1 more source