Maciej Pawlikowski | Searcharxiv

arXiv2019

Weighted Ensemble of Statistical Models

We present a detailed description of our submission for the M4 forecasting competition, in which it ranked 3rd overall. Our solution utilizes several commonly used statistical models, which are weighted according to their performance on historical data. We cluster series within each type of frequency with respect to the existence of trend and seasonality. Every class of series is assigned a different set of models to combine. Combination weights are chosen separately for each series. We conduct experiments with a holdout set to manually pick pools of models that perform best for a given series type, as well as to choose the combination approaches.

stat.AP↗

arXiv2018

A Talker Ensemble: the University of Wrocław's Entry to the NIPS 2017 Conversational Intelligence Challenge

We present Poetwannabe, a chatbot submitted by the University of Wrocław to the NIPS 2017 Conversational Intelligence Challenge, in which it ranked first ex-aequo. It is able to conduct a conversation with a user in a natural language. The primary functionality of our dialogue system is context-aware question answering (QA), while its secondary function is maintaining user engagement. The chatbot is composed of a number of sub-modules, which independently prepare replies to user's prompts and assess their own confidence. To answer questions, our dialogue system relies heavily on factual data, sourced mostly from Wikipedia and DBpedia, data of real user interactions in public forums, as well as data concerning general literature. Where applicable, modules are trained on large datasets using GPUs. However, to comply with the competition's requirements, the final system is compact and runs on commodity hardware.

cs.CL↗