73 skills found · Page 2 of 3
CodingZeal / Hash DiffDiff tool for deep Ruby hash comparison
Agri-Hub / Deep Learning For Cloud Gap Filling On Normalized Difference Vegetation IndexA CNN-RNN based model that identifies correlations between optical and SAR data and exports dense Normalized Difference Vegetation Index (NDVI) time-series of a static 6-day time resolution and can be used for Events Detection tasks
usnistgov / Oscal Deep DiffOpen Security Controls Assessment Language (OSCAL) Deep Differencing Tool
bpohoriletz / Hash Deep DiffNo description available
afiaka87 / Latent Diffusion DeepspeedFinetune the 1.4B latent diffusion text2img-large checkpoint from CompVis using deepspeed. (work-in-progress)
LAION-AI / Deep Image Diffusion PriorInverts CLIP text embeds to image embeds and visualizes with deep-image-prior.
kushwahavishal646 / Load Forecasting Using Different Deep Learning Architecturesthis project is to implement different deep learning architectures and evaluate them based on their performance on the hour-ahead electricity price and load prediction task. More specifically, we will evaluate (i) Random Forest, (ii) CNN-Univariate, (iii) CNN-Multivariate, (iv) RNN-LSTM and (v) BiLSTM architectures, using the root mean squared error (RMSE). Furthermore, we will experiment on different task formulations and types of frameworks, alongside the two following dimensions: • We will compare the performance of univariate time series forecasting and multivariate time series forecasting. Univariate time series forecasting is a framework on which the predicted quantity (i.e. electricity price) is the sole feature that is used by the models, whereas the multivariate variant of the task also uses other features which may prove important for the prediction, such as the load of the energy grid, the temperature, etc. • We will compare the performance of using different time-steps (3, 10 and 25 time-lags) as a way of reframing the time-series prediction task into a supervised learning problem, i.e. using the past 3, 10 and 25 values of the features which are fed into our models.
cz4e / All Optical Machine Learning Using Diffractive Deep Neural Networks看過All-optical machine learning using diffractive deep neural networks論文，實現代碼
lillian-hao / Deep Learning With Differential PrivacyAn implementation of Deep Learning with Differential Privacy
invertase / DeepsPerformant utilities to manage deeply nested objects. get, set, flatten, diff etc.
sunyumark / ScaDec Deep Learning Diffractive TomographyEfficient and accurate inversion of multiple scattering with deep learning
wwilsman / Redux Deep DiffHigher order reducer to deep diff redux states
kevinczhou / Deep Prior Diffraction Tomography3D reconstruction code for deep prior diffraction tomography
therohk / Datum MergeSimplified diff and merge for deeply nested objects.
ateniolatobi / Differential Privacy For Deeplearning ProjectFinal project for Lesson 6: Differential privacy for deep learning in the Facebook and Udacity Secure and Private AI scholarship nanodegree program
QData / DeepDiffChrome"DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications", Bioinformatics, Volume 34, Issue 17,
dispix / Deep DiffSmall library who deeply check for difference between two objects
yijiufly / SigmaDiffImplementation of the NDSS'24 paper "SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing"
awsomecat / Diffractive Deep Neural NetworkNo description available
dyedd / Deepspeed Diffusers🚀 原生使用 Deepspeed 训练 Diffusers | Native Training of Diffusers with Deepspeed
