Lesezeichen (verstecken)616
- The Grid Corpus is a large multitalker audiovisual sentence corpus designed to support joint computational-behavioral studies in speech perception. In brief, the corpus consists of high-quality audio and video (facial) recordings of 1000 sentences spoken by each of 34 talkers (18 male, 16 female), for a total of 34000 sentences. Sentences are of the form "put red at G9 now". audio_25k.zip contains the wav format utterances at a 25 kHz sampling rate in a separate directory per talker alignments.zip provides word-level time alignments, again separated by talker s1.zip, s2.zip etc contain .jpg videos for each talker [note that due to an oversight, no video for talker t21 is available] The Grid Corpus is described in detail in the paper jasagrid.pdf included in the dataset.
- Python implementations of the Boruta all-relevant feature selection method. - GitHub - scikit-learn-contrib/boruta_py: Python implementations of the Boruta all-relevant feature selection method.
- IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz - GitHub - dynamicslab/databook_python: IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
- Port of OpenAI's Whisper model in C/C++. Contribute to ggerganov/whisper.cpp development by creating an account on GitHub.
- Deep Learning Fundamentals -- Code material and exercises - GitHub - Lightning-AI/dl-fundamentals: Deep Learning Fundamentals -- Code material and exercises
- STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020) - GitHub - rasbt/stat451-machine-learning-fs20: STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020)
- A recipe to study Graph Neural Networks (GNNs). Contribute to dair-ai/GNNs-Recipe development by creating an account on GitHub.
- This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA.
- Teaching material for the course of Deep Learning and Applied AI, 2nd semester 2020, Sapienza University of Rome
- A free course in biological modeling at multiple scales.
- notebook du papier https://arxiv.org/pdf/2004.08531.pdf
- A data augmentations library for audio, image, text, and video. - facebookresearch/AugLy
- Pytorch implementation of the paper : A Global-local Attention Framework for Weakly Labelled Audio Tagging. - WangHelin1997/GL-AT
- Lectures for INFO8010 - Deep Learning, ULiège. Contribute to glouppe/info8010-deep-learning development by creating an account on GitHub.
- CVXPY creates powerful new PyTorch and TensorFlow layers
- Code for the paper "Contrastive Learning Inverts the Data Generating Process". - brendel-group/cl-ica
- Generative Models
- torch-optimizer -- collection of optimizers for Pytorch - jettify/pytorch-optimizer
- A PyTorch Implementation of End-to-End Models for Speech-to-Text - awni/speech
- Automatic differentiation with weighted finite-state transducers. - facebookresearch/gtn https://arxiv.org/abs/2010.01003
- https://www.reddit.com/r/NYU_DeepLearning/ https://atcold.github.io/pytorch-Deep-Learning/ https://medium.com/@NYUDataScience/yann-lecuns-deep-learning-course-at-cds-is-now-fully-online-accessible-to-all-787ddc8bf0af
- speech generation
- A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning. - iver56/audiomentations
- Time Series Forecasting Best Practices & Examples. Contribute to microsoft/forecasting development by creating an account on GitHub.
- PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations - sthalles/SimCLR
- Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning - sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning
- Learn how to leverage unlabeled data using FixMatch for semi-supervised learning
- Discover, build and share what you've made with ML.
Publikationen (verstecken)39
- Transactions of the International Society for Music Information Retrieval, 6 (1): 89–102 (2023)
- Information, 14 (2): 137 (Februar 2023)
- (2016)cite arxiv:1606.04838.
- (2021)cite arxiv:2110.05838Comment: Under review.
- (2014)cite arxiv:1406.7872Comment: Notes prepared to accompany a series of tutorial lectures given by the author at the 1st Lake Michigan Workshop on Combinatorics and Graph Theory, Western Michigan University, March 15--16 2014.
- (2020)cite arxiv:2010.10504Comment: 11 pages, 3 figures, 5 tables. Submitted to NeurIPS SAS 2020 Workshop.
- (2018)cite arxiv:1808.08931Comment: The original version was accepted by KDD2018. Code is publicly available at https://github.com/divelab/dilated.
- (2020)cite arxiv:2010.13581Comment: NeurIPS 2020. Code available at https://github.com/mfinzi/constrained-hamiltonian-neural-networks.
- (2014)cite arxiv:1502.05988.
- Frontiers in Neuroscience, (März 2020)
- (2020)
- (2020)cite arxiv:2002.02065Comment: 5 pages.
- (2017)cite arxiv:1704.03976Comment: To be appeared in IEEE Transactions on Pattern Analysis and Machine Intelligence.
- (2019)cite arxiv:1906.02506Comment: NeurIPS 2019.
- (2019)cite arxiv:1911.05722Comment: Technical report.
- (2019)cite arxiv:1911.04252.
- (2019)cite arxiv:1910.06711.
- (2019)cite arxiv:1910.05929Comment: 10 pages, 8 figures.
- (2019)cite arxiv:1909.10893.
- (2019)cite arxiv:1908.04351.
