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opencc-by-4.0Jun 2021View details →

Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks- dataset

Dataset associated with the paper "Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks"

opencc-by-4.0May 2024View details →

Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)

The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments.  The data folder has three subfolders: <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> The data folder has to be integrated in the Git repository nn-toc, to execute the code.

opencc-by-4.0Mar 2018View details →

The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility

Contains all the data: Bentsen, T., T.May, A. A. Kresnner, and T. Dau. The benefit of combining a deep neural network architecture with ideal ratio mask estimation in computational speech segregation to improve speech intelligibility. PLOS ONE., in review. There are two folders: <ol> <li>WRSs: the Word Recognition Scores (WRSs) from the listener study. The matrix has dimensions 9 conditions x 20 subjects. Data is ordered corresponding to the following condition order: 'UP', 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)', 'DNN (IBM)'; 'DNN (IBM, 40 ms)'; 'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> <li>Masks: <ul> <li>GMM-IBMs: IBMs and estimated IBMs for the models 'GMM', 'GMM (3 subbands)', 'GMM (7 subbands)', 'GMM (11 subbands)'</li> <li>DNN-IBMs: IBMs and estimated IBMs for the models 'DNN (IBM)'; 'DNN (IBM, 40 ms)'</li> <li>DNN-IRMs: IRMs and estimated IRMs for the models  'DNN (IRM)'; 'DNN (IRM, 40 ms)'</li> </ul> </li> </ol>

opencc-by-nc-4.0Jul 2019View details →

Deep convolutional neural network for owl vocal identification

This repository contains all the code and data necessary to replicate the results presented in Ruff et al. 2019, "Automated identification of avian vocalizations with deep convolutional neural networks", and is published in support of that manuscript. The folder includes several Python scripts, our trained convolutional neural network (CNN), and a set of 164,210 spectrogram images that were reviewed to generate CNN performance metrics. We include the CNN's predicted class scores for the test images as well as the set of labels assigned to the same images by experienced human technicians. The published article can be found here: <a href="https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125">https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.125</a> As presented, the CNN is designed to accept grayscale PNG images at 500x129 resolution and will generate a set of seven class scores for each image. Class scores are the softmax activation from the final (seven unit) fully-connected layer of the CNN. Scores are bounded between 0 and 1 and sum to 1 for each image. This means target classes are implicitly treated as mutually exclusive (i.e., each image belongs to exactly one class), although in reality some images contain calls from >1 target species. The different scripts and their functions are as follows: - Code used to construct and train the CNN is in Owl_CNN_train_model.py - Code to generate spectrograms with randomized parameters based on tagged calls in audio files is in Owl_CNN_generate_training_data.py - Code to generate random spectrograms from a set of audio files (used to generate training data for the Noise class) can be generated with Owl_CNN_make_noise_data.py - Code used to process raw audio files, including segmenting them into 12 s clips, generating spectrograms, and generating class scores using a pre-trained CNN is in Owl_CNN_process_audio.py - Code to generate class scores for an existing set of spectrogram images using a pre-trained CNN are in Owl_CNN_process_images.py Our seven target classes are as follows: AEAC - Northern saw-whet owl, Aegolius acadicus. BUVI - Great horned owl, Bubo virginianus. GLGN - Northern pygmy-owl, Glaucidium gnoma. MEKE - Western screech-owl, Megascops kennicottii. STOC - (Northern) spotted owl, Strix occidentalis caurina. STVA - Barred owl, Strix varia. Noise - Catch-all for any clip that did not contain vocalizations of at least one of the six owl species listed above. The CNN was trained for 100 epochs and saved only after epochs in which validation loss improved. Loss was measured as categorical cross-entropy. The CNN was last saved at epoch 97 with reported metrics: Training loss = 0.218 Training accuracy = 0.972 Validation loss = 0.165 Validation accuracy = 0.987 Although this code has been tested and works on our system, we make no guarantee that it will work for others without modification. Created using Python version 2.7.14, TensorFlow version 1.2.1, Keras version 2.2, and SoX version 14.4. Code was developed by Bharath Padmaraju, Zack Ruff, and Chris Sullivan. Questions and comments may be directed to zjruff at gmail dot com. Zack Ruff 15 July 2019

opencc-by-4.0Nov 2019View details →

Dataset for supervised learning with a deep neural network to assess azimuthal localisation in sound field synthesis

Dataset for supervised learning with a deep neural network to assess azimuthal localisation in sound field synthesis. Released as part of the Master Thesis 'An Auditory Model for Azimuthal Localisation in Sound Field Synthesis'. This database is calculated from the data of listening experiments.

opencc-by-4.0Jun 2024View details →

Phyloformer: Fast, accurate and versatile phylogenetic reconstruction with deep neural networks

This record is composed of:  <ul> <li>The <code>results.tar.gz</code> file which  contains all the output files necessary to reproduce the figures and tables from the linked paper</li> <li>The 3 datasets used to fine tune different versions of Phylofofmer: <ol> <li><code>cherry_fine_tune.tar.xz</code> used to fine tune Phyloformer on the CherryML model</li> <li><code>LG_fine_tune_mre.tar.xz</code> used to fine tune Phyloformer on LG+GC data with an MRE loss</li> <li><code>pastek_fine_tune.tar.xz</code> used to fine tune Phyloformer on the SelReg model</li> </ol> </li> <li>The <code>paper_test_sets.tar.xz</code> file contains the test sets used to generate data in <code>results.tar.gz</code>, with simulated tree/msa pairs and trees inferred by different methods</li> </ul>

Surfaces/regoliths used in the training and testing of the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the surfaces/regoliths in terms of elemental surface composition used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for exospheric simulations used in the deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains the inputs and outputs of the exospheric simulations performed for v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Inputs and outputs for the training and testing of a deep neural network for surface reconstruction from simulated exospheric measurements

This dataset contains inputs (datasets) and outputs (trainings and tests) used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".

Deep learning based on convolutional neural networks to classify nanobiomechanical data

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opencc-by-4.0Oct 2022View details →

Dataset for: Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks

Datasets for the paper Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks, published in BMC Biology.

opencc-by-4.0Nov 2022View details →

Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"

Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks" Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik* *Contact: sbpaik@kaist.ac.kr   To run demo codes for "<a href="https://github.com/vsnnlab/Invariance">Invariance of Object Detection in Untrained Deep Neural Networks</a>", please download files below.   1. Image.zip - Object dataset (Foldername: selectivity_var): This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class. - Viewpoint dataset for invariance test (Folder name: invariance_test): This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset. - Viewpoint dataset for finding invariant unit (Folder name: invariance_unit): This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit - SVM dataset (Folder name: SVM_var): This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.

opencc-by-4.0May 2023View details →

SlumberNet: Deep learning classification of sleep stages using residual neural networks

Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of accuracy (~98%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~50x faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet demonstrates the power of deep learning in sleep research, and looking forward, SlumberNet could be adapted to human EEG analysis and sleep stage classification. Thus, SlumberNet could be a valuable tool in understanding both sleep physiology and disorders in mammals.

openother-openMay 2023View details →

YudengLin/memristorBDNN: Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning

This code repository is partly to support risk-sensitive reinforcement learning experiment in the manuscript "Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning" submitted to Nature Machine Intelligence.

ClinicalTrials.gov36/100

Deep Neural Network for Stroke Patient Gait Analysis and Classification

ClinicalTrials.gov study NCT04968418. IPD Sharing: NO. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →

publicJul 2022View details →

Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation: supporting data and outputs

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publicDec 2025View details →

PhyloCNN: Improving tree representation and neural network architecture for deep learning from trees in phylodynamics and diversification studies

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publicAug 2020View details →

Re-evaluating deep neural networks for phylogeny estimation: the issue of taxon sampling

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