Results 101 to 110 of about 1,256,985 (257)

Speech Emotion Recognition Using Two-Stage Multiple Instance Learning Networks [PDF]

Jisuanji kexue yu tansuo
In the task of speech emotion recognition (SER), each utterance is usually divided into several equal-length segments when processing the speech signals with unequal lengths, and finally emotion classification is obtained based on the average of the ...
ZHANG Shiqing, CHEN Chen, ZHAO Xiaoming
doaj   +1 more source

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

, 2018
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework ...
Chen, Huajun, Chen, Xi, Deng, Shumin, Sun, Zhanlin, Zhang, Ningyu, Zhang, Wei   +5 more
core  

3D (Bio) Printing Combined Fiber Fabrication Methods for Tissue Engineering Applications: Possibilities and Limitations

Advanced Functional Materials, EarlyView.
Biofabrication aims at providing innovative technologies and tools for the fabrication of tissue‐like constructs for tissue engineering and regenerative medicine applications. By integrating multiple biofabrication technologies, such as 3D (bio) printing with fiber fabrication methods, it would be more realistic to reconstruct native tissue's ...
Waseem Kitana, Indra Apsite, Leonid Ionov   +2 more
wiley   +1 more source

Bag2image: a multi-instance network traffic representation for network security event prediction

Cybersecurity
In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics.
Jiachen Zhang, Daoqi Han, Zhaoxuan Lv, Yueming Lu, Junke Duan, Yang Liu, Xinyu Zhang   +6 more
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Nearest Labelset Using Double Distances for Multi-label Classification

, 2017
Multi-label classification is a type of supervised learning where an instance may belong to multiple labels simultaneously. Predicting each label independently has been criticized for not exploiting any correlation between labels.
Gweon, Hyukjun, Schonlau, Matthias, Steiner, Stefan   +2 more
core  

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Fusing crops representation into snippet via mutual learning for weakly supervised surveillance anomaly detection

IET Computer Vision
In recent years, the challenge of detecting anomalies in real‐world surveillance videos using weakly supervised data has emerged. Traditional methods, utilising multi‐instance learning (MIL) with video snippets, struggle with background noise and tend to
Bohua Zhang, Jianru Xue
doaj   +1 more source

ISBDD Model for Classification of Hyperspectral Remote Sensing Imagery

Sensors, 2018
The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances
Na Li, Zhaopeng Xu, Huijie Zhao, Xinchen Huang, Zhenhong Li, Jane Drummond, Daming Wang   +6 more
doaj   +1 more source

A Convex Relaxation for Weakly Supervised Classifiers

, 2012
This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (
Bach, Francis, Joulin, Armand
core   +3 more sources