Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Bayesian learning for neural networks: an algorithmic survey
The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …
the topic and the multitude of ingredients involved therein, besides the complexity of turning …
Deeplob: Deep convolutional neural networks for limit order books
We develop a large-scale deep learning model to predict price movements from limit order
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …
book (LOB) data of cash equities. The architecture utilizes convolutional filters to capture the …
Fighting money laundering with statistics and machine learning
Money laundering is a profound global problem. Nonetheless, there is little scientific
literature on statistical and machine learning methods for anti-money laundering. In this …
literature on statistical and machine learning methods for anti-money laundering. In this …
Deep adaptive input normalization for time series forecasting
Deep learning (DL) models can be used to tackle time series analysis tasks with great
success. However, the performance of DL models can degenerate rapidly if the data are not …
success. However, the performance of DL models can degenerate rapidly if the data are not …
Evolutionary deep learning-based energy consumption prediction for buildings
Today's energy resources are closer to consumers due to sustainable energy and advanced
technology. To that end, ensuring a precise prediction of energy consumption at the …
technology. To that end, ensuring a precise prediction of energy consumption at the …
Deep reinforcement learning for financial trading using price trailing
Develo** accurate financial analysis tools can be useful both for speculative trading, as
well as for analyzing the behavior of markets and promptly responding to unstable …
well as for analyzing the behavior of markets and promptly responding to unstable …
Robust architecture-agnostic and noise resilient training of photonic deep learning models
Neuromorphic photonic accelerators for Deep Learning (DL) have increasingly gained
attention over the recent years due to their ability for ultra fast matrix-based calculations and …
attention over the recent years due to their ability for ultra fast matrix-based calculations and …
Price change prediction of ultra high frequency financial data based on temporal convolutional network
W Dai, Y An, W Long - Procedia Computer Science, 2022 - Elsevier
Through in-depth analysis of Ultra high frequency (UHF) stock price change data, more
reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we …
reasonable discrete dynamic distribution models are proposed in this paper. Firstly, we …
Exploiting intra-day patterns for market shock prediction: A machine learning approach
Discovering hidden patterns under unexpected market shocks is a significant and
challenging problem, which continually attracts attention from research communities of …
challenging problem, which continually attracts attention from research communities of …
Predicting high-frequency stock movement with differential transformer neural network
Predicting stock prices has long been the holy grail for providing guidance to investors.
Extracting effective information from Limit Order Books (LOBs) is a key point in high …
Extracting effective information from Limit Order Books (LOBs) is a key point in high …