https://uwaterloo.ca/scholar/mcrowley/presentations/compact-representation-multi-dimensional-combustion-manifold-using-deep
The computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it...
compactrepresentationmultidimensionalcombustion
https://deepai.org/publication/end-to-end-multimodal-emotion-recognition-using-deep-neural-networks
04/27/17 - Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can ...
deep neural networksemotion recognitionendmultimodalusing
https://pythongui.org/unlock-the-power-of-python-for-deep-learning-with-recurrent-neural-networks/
Jul 26, 2024 - Deep learning algorithms can work with almost any type of data and require massive amounts of computing power and data to solve complex problems. Let us now tak
the powerdeep learningunlockpythonrecurrent
https://aliamjad.com/exploring-deep-learning-unleashing-the-power-of-neural-networks/
In today's fast-paced and data-driven world, businesses are constantly seeking innovative ways to gain a competitive edge, make smarter decisions, and deliver...
deep learningthe powerneural networksexploringunleashing
https://www.mdpi.com/2075-5309/14/3/578
Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other...
robustbuildingidentificationstreetviews
https://www.digitalocean.com/resources/articles/what-is-deep-learning
Discover deep learning, neural networks, and how businesses can implement computational innovations to automate processes and predict market trends.
what isdeep learningbeginnerguide
https://deepai.org/publication/redundancy-in-deep-linear-neural-networks
06/09/22 - Conventional wisdom states that deep linear neural networks benefit from expressiveness and optimization advantages over a single ...
in deepneural networksredundancylinear
https://openreview.net/forum?id=1mf1ISuyS3&referrer=%5Bthe%20profile%20of%20Binchi%20Zhang%5D(%2Fprofile%3Fid%3D~Binchi_Zhang1)
In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong...
deep neural networkstowardscertifiedunlearning
https://deepai.org/publication/using-deep-neural-networks-to-translate-multi-lingual-threat-intelligence
07/19/18 - The multilingual nature of the Internet increases complications in the cybersecurity community's ongoing efforts to strategically ...
deep neural networksthreat intelligenceusingtranslatemulti
https://www.aiacademy.lk/events/deep-learning-neural-networks-part-1
Free online classroom series conducted in partnership with STEMUP Educational Foundation to educate the young innovators who are at the crossroads of deciding...
deep learningneural networkspartaiacademy
https://arxiv.org/abs/1410.4281
Abstract page for arXiv paper 1410.4281: Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition
short term memoryconstructinglongbaseddeep
https://openreview.net/forum?id=Od6CHhPM7I&referrer=%5Bthe%20profile%20of%20Kaivalya%20Hariharan%5D(%2Fprofile%3Fid%3D~Kaivalya_Hariharan1)
Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area...
deep neural networksred teamingfeaturesynthesistools
https://deepai.org/publication/beyond-dropout-feature-map-distortion-to-regularize-deep-neural-networks
02/23/20 - Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets....
deep neural networksbeyonddropoutfeaturemap
https://deepai.org/publication/aggregated-residual-transformations-for-deep-neural-networks
11/16/16 - We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a b...
deep neural networksresidualtransformations
https://deepai.org/publication/automated-detection-of-coronary-artery-stenosis-in-x-ray-angiography-using-deep-neural-networks
03/04/21 - Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that aff...
coronary artery stenosisx rayautomateddetectionangiography
https://deepai.org/publication/deep-spatio-temporal-neural-networks-for-click-through-rate-prediction
06/10/19 - Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad i...
click through rateneural networksdeeptemporalprediction
https://deepai.org/publication/8-bit-numerical-formats-for-deep-neural-networks
06/06/22 - Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance ...
deep neural networksbitnumericalformats
https://deepai.org/publication/opq-compressing-deep-neural-networks-with-one-shot-pruning-quantization
05/23/22 - As Deep Neural Networks (DNNs) usually are overparameterized and have millions of weight parameters, it is challenging to deploy t...
deep neural networksone shotopqcompressingpruning
https://deepai.org/publication/kernel-convoluted-deep-neural-networks-with-data-augmentation
12/04/20 - The Mixup method (Zhang et al. 2018), which uses linearly interpolated data, has emerged as an effective data augmentation tool to...
deep neural networksdata augmentationkernelconvoluted
https://pubmed.ncbi.nlm.nih.gov/37081386/
Accurate somatic variant calling from next-generation sequencing data is one most important tasks in personalised cancer therapy. The sophistication of the...
validationgeneticvariantsngsdata
https://deepai.org/publication/visual-sentiment-prediction-with-deep-convolutional-neural-networks
11/21/14 - Images have become one of the most popular types of media through which users convey their emotions within online social networks....
convolutional neural networksvisualsentimentpredictiondeep