alexeykrylov,github

daal4py

sources for daal4py - a convenient Python API to DAAL

dafe_python_course_homeworks_2019_2020

difformer

The official codebase for Difformer: Empowering Diffusion Models on the Embedding Space for Text Generation

diffuseq

[ICLR'23] DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models

lattice

[NAACL 2022] Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning.

Note: This is one of the two complementary competitions that together comprise the M5 forecasting challenge. Can you estimate, as precisely as possible, the point forecasts of the unit sales of various products sold in the USA by Walmart? If you are interested in estimating the uncertainty distribution of the realized values of the same series, be sure to check out its companion competition How much camping gear will one store sell each month in a year? To the uninitiated, calculating sales at this level may seem as difficult as predicting the weather. Both types of forecasting rely on science and historical data. While a wrong weather forecast may result in you carrying around an umbrella on a sunny day, inaccurate business forecasts could result in actual or opportunity losses. In this competition, in addition to traditional forecasting methods you’re also challenged to use machine learning to improve forecast accuracy. The Makridakis Open Forecasting Center (MOFC) at the University of Nicosia conducts cutting-edge forecasting research and provides business forecast training. It helps companies achieve accurate predictions, estimate the levels of uncertainty, avoiding costly mistakes, and apply best forecasting practices. The MOFC is well known for its Makridakis Competitions, the first of which ran in the 1980s. In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events. Together, this robust dataset can be used to improve forecasting accuracy. If successful, your work will continue to advance the theory and practice of forecasting. The methods used can be applied in various business areas, such as setting up appropriate inventory or service levels. Through its business support and training, the MOFC will help distribute the tools and knowledge so others can achieve more accurate and better calibrated forecasts, reduce waste and be able to appreciate uncertainty and its risk implications. Acknowledgements Additional thanks go to other partner organizations and prize sponsors, National Technical University of Athens (NTUA), INSEAD, Google, Uber and IIF.