Results 81 to 90 of about 2,817,119 (330)

Efficient Deep Feature Learning and Extraction via StochasticNets

, 2015
Deep neural networks are a powerful tool for feature learning and extraction given their ability to model high-level abstractions in highly complex data.
Fieguth, Paul, Shafiee, Mohammad Javad, Siva, Parthipan, Wong, Alexander   +3 more
core   +1 more source

Deep Learning With DAGs

SSRN Electronic Journal
Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships.
Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou   +4 more
openaire   +2 more sources

Deep Learning in Neuroradiology [PDF]

American Journal of Neuroradiology, 2018
Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging.
G. Zaharchuk, E. Gong, M. Wintermark, D. Rubin, C.P. Langlotz   +4 more
openaire   +3 more sources

Rethinking plastic waste: innovations in enzymatic breakdown of oil‐based polyesters and bioplastics

FEBS Open Bio, EarlyView.
Plastic pollution remains a critical environmental challenge, and current mechanical and chemical recycling methods are insufficient to achieve a fully circular economy. This review highlights recent breakthroughs in the enzymatic depolymerization of both oil‐derived polyesters and bioplastics, including high‐throughput protein engineering, de novo ...
Elena Rosini, Nicolò Antonelli, Gianluca Molla   +2 more
wiley   +1 more source

Deep(er) Learning [PDF]

The Journal of Neuroscience, 2018
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptable to their environmental niche.
Shyam Srinivasan, Ralph J. Greenspan, Charles F. Stevens, Dhruv Grover   +3 more
openaire   +3 more sources

Mitochondria‐associated membranes (MAMs): molecular organization, cellular functions, and their role in health and disease

FEBS Open Bio, EarlyView.
Mitochondria‐associated membranes (MAMs) are contact sites between the endoplasmic reticulum and mitochondria that regulate calcium signaling, lipid metabolism, autophagy, and stress responses. This review outlines their molecular organization, roles in cellular homeostasis, and how dysfunction drives neurodegeneration, metabolic disease, cancer, and ...
Viet Bui, Maryline Santerre, Natalia Shcherbik, Bassel E Sawaya   +3 more
wiley   +1 more source

Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms

Heliyon
Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide.
Saad Sahriar, Sanjida Akther, Jannatul Mauya, Ruhul Amin, Md Shahajada Mia, Sabba Ruhi, Md Shamim Reza   +6 more
doaj   +1 more source

A Classifier Based on K-Nearest Neighbors Using Weighted Summation of Reconstruction Errors [PDF]

مجله مدل سازی در مهندسی
In this paper, a classifier is introduced based on the nearest neighbor classifier and the reconstruction error for data classification. In the proposed method, first, K nearest data points (neighbors) from each category in the training data are ...
Rassoul Hajizadeh, Mohammad Ali Hosseinzadeh   +1 more
doaj   +1 more source

Learning Deep Structured Models [PDF]

, 2015
Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such relationships.
Chen, Liang-Chieh, Schwing, Alexander G., Urtasun, Raquel, Yuille, Alan L.   +3 more
core   +1 more source

Deep Learning for IoT

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), 2020
Deep learning and other machine learning approaches are deployed to many systems related to Internet of Things or IoT. However, it faces challenges that adversaries can take loopholes to hack these systems through tampering history data. This paper first presents overall points of adversarial machine learning.
openaire   +3 more sources