Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Уровень фенотипической устойчивости и тип генных мутаций у M.tuberculosis

The adjustment of the minimum inhibitory concentrations for anti-tuberculosis drugs used in classical cultivation methods and the obtaining of more accurate data on the level of drug resistance, makes it possible to evaluate cases with different results to phenotypic and genotypic methods of anti-tuberculosis resistance testing. The evaluation of different types of genotypic resistance, with the description of mutations that confer low or high resistance, is consistent with the level of phenotypic resistance, and also makes it possible to adjust treatment regimens, which ultimately will positively influence the duration and results of treatment. Whenever testing of M. tuberculosis resistance by molecular methods allows, the results should be reported with the specific mutations detected and the description of the clinical implications of this mutationAjustarea concentraţiilor minime inhibitorii pentru preparatele antituberculoase utilizate în metodele de cultură clasice şi obţinerea unor date mai exacte despre nivelul de rezistenţă la medicamente, face posibilă evaluarea cazurilor cu rezultate diferite la metodele fenotipice şi genice de testare a rezistenţei antituberculoase. Evaluarea diferitor tipuri de rezistenţă genică, cu descrierea mutaţiilor care conferă rezistenţă joasă sau înaltă, este în coerenţă nivelul de rezistenţă fenotipică, şi de asemenea face posibilă ajustarea schemelor de tratament, care la rândul lor influenţează pozitiv la durata şi rezultatele tratamentului. Ori de câte ori testarea rezistenţei M. tuberculosis prin metode moleculare permite, rezultatele trebuie să fie raportate cu mutaţiile specifice detectate şi cu descrierea implicaţiilor clinice ale prezentei mutaţieiРегулировка минимальных ингибирующих концентраций противотуберкулезных препаратов, используемых в классических методах культивирования, и получение более точных данных об уровне лекарственной устойчивости, позволяет оценивать случаи с разными результатами фенотипических и генотипических методов противотуберкулезной устойчивости. Оценка различных типов генотипической устойчивости с описанием мутаций, придающих низкую или высокую устойчивость, согласуется с уровнем фенотипической устойчивости, а также позволяет корректировать схемы лечения, что в конечном итоге положительно влияет на продолжительность и результаты лечение. Если позволяет тестирование устойчивости M. tuberculosis молекулярными методами, результаты должны быть сообщены с указанием конкретных обнаруженных мутаций и описанием клинических последствий этой мутаци

Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case

Deep learning is finding its way into high energy physics by replacing traditional Monte Carlo simulations. However, deep learning still requires an excessive amount of computational resources. A promising approach to make deep learning more efficient is to quantize the parameters of the neural networks to reduced precision. Reduced precision computing is extensively used in modern deep learning and results to lower execution inference time, smaller memory footprint and less memory bandwidth. In this paper we analyse the effects of low precision inference on a complex deep generative adversarial network model. The use case which we are addressing is calorimeter detector simulations of subatomic particle interactions in accelerator based high energy physics. We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite. In the performance benchmark we gain a speed-up of 1.73x on Intel hardware for the quantized iLoT model compared to the initial, not quantized, model. With different physics-inspired self-developed metrics, we validate that the quantized iLoT model shows a lower loss of physical accuracy in comparison to the TensorFlow Lite model.Comment: Submitted at ICPRAM 2021; from CERN openlab - Intel collaboratio