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12 results for “neurips”
Dataset for 'The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track'
<p>This packages comprises of analyses and evaluations of 60 datasets from the NeurIPS Datasets and Benchmarks track. It is part of a paper currently under review at the 2024 the NeurIPS Datasets and Benchmarks track, titled, "The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track". </p>
HEAR NeurIPS 2021 Datasets (Holistic Evaluation of Audio Representations)
<p><strong>NOTES:</strong></p> <p><strong>* On Zenodo, please make sure you download datasets version 2021.3, not earlier versions. (2021.3 added the Vocal Imitations dataset at 48KHz. 2021.2 updated the tfds datasets.)</strong></p> <p><strong>* The datasets have different open licenses. Please see LICENSE.txt for each individual dataset's license.</strong></p> <p>These are the evaluation tasks for the HEAR (Holistic Evaluation of Audio Representations) 2021 NeurIPS challenge.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning?<br> <br> HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p> <p>For more information, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a> and upcoming PMLR journal article.</p> <p>Datasets were all normalized to a common human-readable format using <a href="https://github.com/neuralaudio/hear-preprocess">hearpreprocess</a>. Until 2022-04-01, datasets will be mirrored at <a href="https://data.neuralaudio.ai/">data.neuralaudio.ai</a>. This Zenodo mirror has all audio task but only at 48000Hz sampling rate. For other sampling rates (16000, 22050, 32000, 44100), please download files (requester pays) from Google Storage gs://hear2021-archive/tasks/ or AWS s3://hear2021-archive/tasks/</p>
Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>Each .zip file contains a saved model. Details on these are coming soon.</p><p>For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (<a href="https://openreview.net/forum?id=8dfboOQfYt3">link</a>).</p>
CANOPUS dataset filtered with NeurIPS MS/MS structures
<p>The 4,175,091 CANOPUS structures (<span>https://doi.org/10.1038/s41587-020-0740-8</span>) were filtered by a hit (MCES-distance <= 2) to a structure of the NeurIPS MS/MS dataset (https://zenodo.org/doi/10.5281/zenodo.11210205). 2304 structures from the CANOPUS dataset did not have SMILES and were excluded. The MS/MS-dataset structures were PubChem-standardized with 3 compounds failing standardization.</p>
HEAR NeurIPS 2021 Submitted Models (Holistic Evaluation of Audio Representations)
<p>This is a mirror of audio representation models that are HEAR API compatible, submitted to the HEAR 2021 NeurIPS challenge: Holistic Evaluation of Audio Representations.</p> <p>To use these models, please see the <a href="https://github.com/neuralaudio/hear2021-submitted-models">hear2021-submitted-models</a> repository. For more information about HEAR, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a>, upcoming PMLR journal article, and the <a href="https://zenodo.org/record/5887964">HEAR 2021 datasets</a> on Zenodo.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning? HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p>
Data for paper "SizeShiftReg" accepted at NeurIPS 2022
<p>Data for paper "SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks", by Davide Buffelli, Pietro Liò, and Fabio Vandin, accepted at NeurIPS 2022.</p>
Testing script and data needed to run it for NeurIPS 2024 submission 18634
<p>Dependencies:<br>pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121 (get specific command for your environment from Pytorch website. If you can't use CUDA, you must edit the code to use the CPU.)<br>pip install timm<br>pip install opencv-python<br>pip install pillow<br>pip install joblib<br>pip install scipy<br>pip install scikit-learn</p> <p>Please make the following edits to run the script. Also, either move the models folder (https://zenodo.org/records/11245477) or change the directory.<br>Also, add a folder to the directory with the testing script called "Results"</p> <p>The testing script also includes the models for directionality, another statistic we attempted to predict. We did not report the values for directionality in our paper, so feel free to comment lines 2175-2253 and lines 2296-2308 to speed up the computation for the statistics presented in the paper. Due to time constraints, this version also doesn't include the class-modifying arguments for Brownian and straight-trajectory motion. These will be included in the testing script on the official release of DeepTrakStat on Github. <br>line 485: sim_directory='ground_truth_trajectories/'<br>line 486: tmate_directory='tmate_trajectories/'</p> <p>lines 509-525:<br>directories = [<br>'simulated_imagery/1000part_16xspeed_heterogeneous/','simulated_imagery/1000part_32xspeed_heterogeneous/',<br>'simulated_imagery/1000part_4xspeed_heterogeneous/',<br>'simulated_imagery/2000part_16xspeed_heterogeneous/','simulated_imagery/2000part_32xspeed_heterogeneous/', ## Group 1<br>'simulated_imagery/500part_16xspeed_heterogeneous/','simulated_imagery/500part_32xspeed_heterogeneous/',<br>'simulated_imagery/500part_4xspeed_heterogeneous/',<br>'simulated_imagery/sim111_brown/', 'simulated_imagery/sim112_brown/',<br>'simulated_imagery/sim113_brown/','simulated_imagery/sim114_brown/', # Group 2<br>'simulated_imagery/sim115_brown/','simulated_imagery/sim119_brown/','simulated_imagery/sim120_brown/',<br>'simulated_imagery/sim2201/', 'simulated_imagery/sim2210/', 'simulated_imagery/sim2215/',<br>'simulated_imagery/sim2220/','simulated_imagery/sim2230/','simulated_imagery/sim2235/','simulated_imagery/sim2240/', # Group 3<br>'simulated_imagery/sim2241/','simulated_imagery/sim2242/','simulated_imagery/sim2243/','simulated_imagery/sim2244/', #Group 4<br>'simulated_imagery/sim2251/','simulated_imagery/sim2252/','simulated_imagery/sim2253/',<br>'simulated_imagery/sim2254/','simulated_imagery/sim2255/','simulated_imagery/sim2256/',<br>'simulated_imagery/sim2257/','simulated_imagery/sim2258/','simulated_imagery/sim2259/','simulated_imagery/sim2260/', # Group 5<br>'simulated_imagery/test1/','simulated_imagery/test2/','simulated_imagery/test5/','simulated_imagery/test8/' #Group 6<br>]</p> <p>line 606:<br> sorted_frames = sorted(files, key=lambda x: int(x[4:-4]))</p>
NeurIPS 2022 Cell Segmentation Competition Dataset
<p>The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images.</p> <p>https://neurips22-cellseg.grand-challenge.org/</p> <p>Please cite the following paper if this dataset is used in your research. </p> <p> </p> <pre><code><span>@article</span><span>{</span><span>NeurIPS</span><span>-</span><span>CellSeg</span><span>,</span> <span>title</span> <span>=</span> <span>{</span><span>The</span> <span>Multi</span><span>-</span><span>modality</span> <span>Cell</span> <span>Segmentation</span> <span>Challenge</span><span>:</span> <span>Towards</span> <span>Universal</span> <span>Solutions</span><span>}</span><span>,</span> <span>author</span> <span>=</span> <span>{</span><span>Jun</span> <span>Ma</span> <span>and</span> <span>Ronald</span> <span>Xie</span> <span>and</span> <span>Shamini</span> <span>Ayyadhury</span> <span>and</span> <span>Cheng</span> <span>Ge</span> <span>and</span> <span>Anubha</span> <span>Gupta</span> <span>and</span> <span>Ritu</span> <span>Gupta</span> <span>and</span> <span>Song</span> <span>Gu</span> <span>and</span> <span>Yao</span> <span>Zhang</span> <span>and</span> <span>Gihun</span> <span>Lee</span> <span>and</span> <span>Joonkee</span> <span>Kim</span> <span>and</span> <span>Wei</span> <span>Lou</span> <span>and</span> <span>Haofeng</span> <span>Li</span> <span>and</span> <span>Eric</span> <span>Upschulte</span> <span>and</span> <span>Timo</span> <span>Dickscheid</span> <span>and</span> <span>José</span> <span>Guilherme</span> <span>de</span> <span>Almeida</span> <span>and</span> <span>Yixin</span> <span>Wang</span> <span>and</span> <span>Lin</span> <span>Han</span> <span>and</span> <span>Xin</span> <span>Yang</span> <span>and</span> <span>Marco</span> <span>Labagnara</span> <span>and</span> <span>Vojislav</span> <span>Gligorovski</span> <span>and</span> <span>Maxime</span> <span>Scheder</span> <span>and</span> <span>Sahand</span> <span>Jamal</span> <span>Rahi</span> <span>and</span> <span>Carly</span> <span>Kempster</span> <span>and</span> <span>Alice</span> <span>Pollitt</span> <span>and</span> <span>Leon</span> <span>Espinosa</span> <span>and</span> <span>Tâm</span> <span>Mignot</span> <span>and</span> <span>Jan</span> <span>Moritz</span> <span>Middeke</span> <span>and</span> <span>Jan</span><span>-</span><span>Niklas</span> <span>Eckardt</span> <span>and</span> <span>Wangkai</span> <span>Li</span> <span>and</span> <span>Zhaoyang</span> <span>Li</span> <span>and</span> <span>Xiaochen</span> <span>Cai</span> <span>and</span> <span>Bizhe</span> <span>Bai</span> <span>and</span> <span>Noah</span> <span>F</span><span>.</span> <span>Greenwald</span> <span>and</span> <span>David</span> <span>Van</span> <span>Valen</span> <span>and</span> <span>Erin</span> <span>Weisbart</span> <span>and</span> <span>Beth</span> <span>A</span><span>.</span> <span>Cimini</span> <span>and</span> <span>Trevor</span> <span>Cheung</span> <span>and</span> <span>Oscar</span> <span>Brück</span> <span>and</span> <span>Gary</span> <span>D</span><span>.</span> <span>Bader</span> <span>and</span> <span>Bo</span> <span>Wang</span><span>}</span><span>,</span> <span>journal</span> <span>=</span> <span>{</span><span>Nature</span> <span>Methods</span><span>}</span><span>,</span><span><br> volume={21},<br> pages={1103–1113},<br> <span>year</span> <span>=</span> <span>{</span><span>2024</span><span>}</span>,</span> <span>doi</span> <span>=</span> <span>{</span><span>https</span><span>:</span><span>//</span><span>doi</span><span>.</span><span>org</span><span>/</span><span>10.1038</span><span>/</span><span>s41592</span><span>-</span><span>024</span><span>-</span><span>02233</span><span>-</span><span>6</span><span>}</span> <span>}</span></code></pre> <p> </p> <p>This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning.</p> <p> </p> <p>The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats.</p> <p> </p> <p><strong>We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website!</strong></p> <p> </p> <p><strong>Dataset License: CC-BY-NC-ND</strong></p>
Spike Dataset for NeurIPS Submission
<p>Anonymous</p>
Anonymous NeurIPS submission [2970]
<p>Anonymous NeurIPS submission for ID [2970]</p>
Trained Models for Sliced Iterative Generator (NeurIPS submission)
<p>Trained SIG Models on MNIST, Fashion-MNIST, CIFAR-10 and CelebA, as well as trained autoencoders on CelebA</p>
NeurIPS_Mass_Spectrometry_data
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