Learn to Compute canonical representations for SPD matrices using orthonormal matrices & SVD, enabling efficient geometric applications and matrix manipulation #deepneuralnetworks
Canonical Representations and Computation in SPD Matrices
hackernoon.com
December 4, 2024 at 7:00 PM
Learn to Compute canonical representations for SPD matrices using orthonormal matrices & SVD, enabling efficient geometric applications and matrix manipulation #deepneuralnetworks
🚀 We've released a new version of DIANNA, our open-source #ExplainableAI (#XAI) tool designed to help researchers get insights into predictions of #DeepNeuralNetworks.
What's new:
👉improved dashboard
👉extensive documentation
👉added tutorials
MORE: www.esciencecenter.nl/news/new-rel...
What's new:
👉improved dashboard
👉extensive documentation
👉added tutorials
MORE: www.esciencecenter.nl/news/new-rel...
New release of eScience Center's explainable AI tool DIANNA - eScience Center
DIANNA is an explainable AI (XAI) tool that helps researchers get insights into the predictions of Deep Neural Networks. New version released!
www.esciencecenter.nl
December 9, 2024 at 12:45 PM
🚀 We've released a new version of DIANNA, our open-source #ExplainableAI (#XAI) tool designed to help researchers get insights into predictions of #DeepNeuralNetworks.
What's new:
👉improved dashboard
👉extensive documentation
👉added tutorials
MORE: www.esciencecenter.nl/news/new-rel...
What's new:
👉improved dashboard
👉extensive documentation
👉added tutorials
MORE: www.esciencecenter.nl/news/new-rel...
Xuke Li et al.: CorFuncSAXSNet: deep-learning-driven extraction of nanostructural parameters from small-angle X-ray scattering data of polymeric materials ##DeepNeuralNetworks##SAXS##CorrelationFunctions... ##IUCrhttps://journals.iucr.org/paper?S1600576725005047
CorFuncSAXSNet: deep-learning-driven extraction of nanostructural parameters from small-angle X-ray scattering data of polymeric materials
This work proposes a deep neural network solution to address the inefficiency challenge of the correlation function analysis method in the interpretation of small-angle X-ray scattering data for phase-separated structures of polymers.
journals.iucr.org
July 27, 2025 at 12:00 AM
Xuke Li et al.: CorFuncSAXSNet: deep-learning-driven extraction of nanostructural parameters from small-angle X-ray scattering data of polymeric materials ##DeepNeuralNetworks##SAXS##CorrelationFunctions... ##IUCrhttps://journals.iucr.org/paper?S1600576725005047
Detailed proofs for SPD spaces, inner products, pseudo-gyrodistances, and FC layers, referencing Nguyen & Yang (2023) and Pennec et al. (2020). #deepneuralnetworks
Mathematical Proofs for SPD Inner Products and Pseudo-Gyrodistances in Manifold Layers
hackernoon.com
December 4, 2024 at 8:00 PM
Detailed proofs for SPD spaces, inner products, pseudo-gyrodistances, and FC layers, referencing Nguyen & Yang (2023) and Pennec et al. (2020). #deepneuralnetworks
Explore how hypergyroplanes and pseudo-gyrodistances revolutionize multinomial logistic regression in structure spaces, adapting it to the Riemannian manifold #deepneuralnetworks
Manifold Geometry Meets Logistic Regression: The Rise of Hypergyroplanes
hackernoon.com
December 1, 2024 at 10:01 PM
Explore how hypergyroplanes and pseudo-gyrodistances revolutionize multinomial logistic regression in structure spaces, adapting it to the Riemannian manifold #deepneuralnetworks
Deep neural networks beat discrete choice models for long commutes, while DCMs edge out on short trips. Combining both may improve planning. Read more: https://getnews.me/deep-neural-networks-beat-discrete-models-in-workplace-location-choice/ #deepneuralnetworks #discretechoicemodels
October 3, 2025 at 10:18 PM
Deep neural networks beat discrete choice models for long commutes, while DCMs edge out on short trips. Combining both may improve planning. Read more: https://getnews.me/deep-neural-networks-beat-discrete-models-in-workplace-location-choice/ #deepneuralnetworks #discretechoicemodels
Deep neural networks solve the identical‑oscillator Kuramoto equation; tanh activation yields convergence, and 4‑8 hidden layers with 64‑256 neurons cut error. Read more: https://getnews.me/neural-network-approaches-to-solving-the-identical-kuramoto-equation/ #kuramatomodel #deepneuralnetworks
September 19, 2025 at 6:32 PM
Deep neural networks solve the identical‑oscillator Kuramoto equation; tanh activation yields convergence, and 4‑8 hidden layers with 64‑256 neurons cut error. Read more: https://getnews.me/neural-network-approaches-to-solving-the-identical-kuramoto-equation/ #kuramatomodel #deepneuralnetworks
Koki Yamada et al.: Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm ##Ptychography##PhaseRetrieval##DeepNeuralNetworks@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724006897
Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm
This paper proposes a ptychographic phase-retrieval algorithm combined with a deep neural network (DNN). The proposed method allows the measurement model to be explicitly incorporated into the DNN-based approach, improving imaging capability and robustness to changes in experimental conditions.
journals.iucr.org
August 20, 2024 at 12:01 AM
Koki Yamada et al.: Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm ##Ptychography##PhaseRetrieval##DeepNeuralNetworks@UTokyo_News...##IUCrhttps://journals.iucr.org/paper?S1600576724006897
Victor Poline et al.: Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data ##DeepNeuralNetworks##DeepLearning##XRDCT... ##IUCrhttps://journals.iucr.org/paper?S1600576724003704
Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data
A small, fast and efficient neural network for phase fraction prediction of X-ray diffraction big data is presented. A data-driven approach allows users to create their own training dataset, making the method fully customizable for each experience.
journals.iucr.org
June 1, 2024 at 12:02 AM
Victor Poline et al.: Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data ##DeepNeuralNetworks##DeepLearning##XRDCT... ##IUCrhttps://journals.iucr.org/paper?S1600576724003704
A reproducible study shows why removing activations makes deep nets collapse into linear models—plus gradient behavior, depth effects, and boundaries. #deepneuralnetworks
What Happens if You Remove ReLU From a Deep Neural Network?
hackernoon.com
March 5, 2026 at 12:06 PM
A reproducible study shows why removing activations makes deep nets collapse into linear models—plus gradient behavior, depth effects, and boundaries. #deepneuralnetworks
Dive into the comparitive analysis between logarithmic and floating-point arithmetic in neural nets using the commonly used MNIST dataset. #deepneuralnetworks
Deep Learning Runs on Floating-Point Math. What If That’s a Mistake?
hackernoon.com
February 11, 2025 at 8:57 AM
Dive into the comparitive analysis between logarithmic and floating-point arithmetic in neural nets using the commonly used MNIST dataset. #deepneuralnetworks
Gyrogroups, gyrocommutative gyrogroups, and gyrovector spaces are key to hyperbolic geometry and AI, offering insights into advanced mathematical structures #deepneuralnetworks
What Are Gyrocommutative Gyrogroups?
hackernoon.com
December 3, 2024 at 9:00 PM
Gyrogroups, gyrocommutative gyrogroups, and gyrovector spaces are key to hyperbolic geometry and AI, offering insights into advanced mathematical structures #deepneuralnetworks
GyroSpd++ and Gr-GCN++ face limitations with hybrid methods and different Riemannian metrics, suggesting a need for optimization in SPD and Grassmann networks. #deepneuralnetworks
The Limitations of GyroSpd++ and Gr-GCN++ in Human Action Recognition and Graph Embedding Tasks
hackernoon.com
December 3, 2024 at 8:00 PM
GyroSpd++ and Gr-GCN++ face limitations with hybrid methods and different Riemannian metrics, suggesting a need for optimization in SPD and Grassmann networks. #deepneuralnetworks
Gr-GCN++ outperforms competitors in node classification across Airport, Pubmed, and Cora datasets, highlighting the value of perspective in Grassmann manifolds #deepneuralnetworks
Evaluating Gr-GCN++ for Node Classification Across Various Datasets: Results and Comparisons
hackernoon.com
December 3, 2024 at 7:00 PM
Gr-GCN++ outperforms competitors in node classification across Airport, Pubmed, and Cora datasets, highlighting the value of perspective in Grassmann manifolds #deepneuralnetworks
Evaluate GyroSpd++ for human action recognition using HDM05, FPHA, NTU60 datasets, with insights on performance, design, and comparisons to other methods #deepneuralnetworks
Exploring the Impact of Riemannian Metrics in Human Action Recognition Tasks Using GyroSpd++
hackernoon.com
December 3, 2024 at 6:00 PM
Evaluate GyroSpd++ for human action recognition using HDM05, FPHA, NTU60 datasets, with insights on performance, design, and comparisons to other methods #deepneuralnetworks
Learn notations and algorithms for computing pseudo-gyrodistances, crucial for MLR in Riemannian manifold-based neural networks. #deepneuralnetworks
Key Notations and Algorithm for Computing Pseudo-Gyrodistances in Structure Spaces
hackernoon.com
December 2, 2024 at 9:00 PM
Learn notations and algorithms for computing pseudo-gyrodistances, crucial for MLR in Riemannian manifold-based neural networks. #deepneuralnetworks
This paper presents novel advancements in SPD neural networks and Grassmann geometry for action recognition and node classification. #deepneuralnetworks
Bridging Geometry and Deep Learning: Key Developments in SPD and Grassmann Networks
hackernoon.com
December 2, 2024 at 8:00 PM
This paper presents novel advancements in SPD neural networks and Grassmann geometry for action recognition and node classification. #deepneuralnetworks
GyroSpd++ and Gr-GCN++ outperform baselines in human action recognition and node classification, showing superior accuracy on NTU60, FPHA, and Pubmed datasets. #deepneuralnetworks
New Riemannian Networks Outperform Traditional Models in Action Recognition and Node Classification
hackernoon.com
December 2, 2024 at 7:00 PM
GyroSpd++ and Gr-GCN++ outperform baselines in human action recognition and node classification, showing superior accuracy on NTU60, FPHA, and Pubmed datasets. #deepneuralnetworks
Explore a novel method for Grassmann logarithmic maps and GCNs, improving graph embeddings and node learning on Grassmann manifolds #deepneuralnetworks
Graph Embeddings and Node Learning on Grassmann Manifolds
hackernoon.com
December 2, 2024 at 6:00 PM
Explore a novel method for Grassmann logarithmic maps and GCNs, improving graph embeddings and node learning on Grassmann manifolds #deepneuralnetworks
Learn how hypergyroplanes and pseudo-gyrodistance redefine fully connected and convolutional layers for SPD neural networks #deepneuralnetworks
Reformulating Neural Layers on SPD Manifolds
hackernoon.com
December 1, 2024 at 10:01 PM
Learn how hypergyroplanes and pseudo-gyrodistance redefine fully connected and convolutional layers for SPD neural networks #deepneuralnetworks
Explore the foundational layers and gyrovector space-inspired advancements in neural networks on SPD and Grassmann manifolds #deepneuralnetworks
Researchers Unlock Advanced Building Blocks for Neural Networks on Matrix Manifolds
hackernoon.com
December 1, 2024 at 10:01 PM
Explore the foundational layers and gyrovector space-inspired advancements in neural networks on SPD and Grassmann manifolds #deepneuralnetworks
Discover how advanced gyrovector space theories are driving innovations in deep neural networks on SPD and Grassmann manifolds. #deepneuralnetworks
Matrix Manifold Neural Networks
hackernoon.com
December 1, 2024 at 10:01 PM
Discover how advanced gyrovector space theories are driving innovations in deep neural networks on SPD and Grassmann manifolds. #deepneuralnetworks
Annals of Computer Science and Information Systems
Presenting an approach to speaker’s #ageclassification using #deepneuralnetworks: “Automatic speaker's age classification in the Common Voice database“ by Adam Nowakowski, Włodzimierz Kasprzak. ACSIS Vol. 35 p. 1087–1091; tinyurl.com/54hrdz7d
Presenting an approach to speaker’s #ageclassification using #deepneuralnetworks: “Automatic speaker's age classification in the Common Voice database“ by Adam Nowakowski, Włodzimierz Kasprzak. ACSIS Vol. 35 p. 1087–1091; tinyurl.com/54hrdz7d
Annals of Computer Science and Information Systems, Volume 35
tinyurl.com
December 6, 2024 at 6:22 AM
Annals of Computer Science and Information Systems
Presenting an approach to speaker’s #ageclassification using #deepneuralnetworks: “Automatic speaker's age classification in the Common Voice database“ by Adam Nowakowski, Włodzimierz Kasprzak. ACSIS Vol. 35 p. 1087–1091; tinyurl.com/54hrdz7d
Presenting an approach to speaker’s #ageclassification using #deepneuralnetworks: “Automatic speaker's age classification in the Common Voice database“ by Adam Nowakowski, Włodzimierz Kasprzak. ACSIS Vol. 35 p. 1087–1091; tinyurl.com/54hrdz7d
About to watch season finale of #Pluribus. So this whole thing is a metaphor for #AI, right? Like the joined synthesize all human knowledge but are unable to explain how any given decision is made. Zosia is Carol's personalized AI assistant, etc.
#deepneuralnetworks #AppleTVPlus
#deepneuralnetworks #AppleTVPlus
December 24, 2025 at 5:42 AM
About to watch season finale of #Pluribus. So this whole thing is a metaphor for #AI, right? Like the joined synthesize all human knowledge but are unable to explain how any given decision is made. Zosia is Carol's personalized AI assistant, etc.
#deepneuralnetworks #AppleTVPlus
#deepneuralnetworks #AppleTVPlus