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Found 5 papers, 0 papers with code
Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management
Deep Reinforcement Learning approaches to Online Portfolio Selection have grown in popularity in recent years.
Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift
Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information System. In our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems.
Seq2Seq and Multi-Task Learning for joint intent and content extraction for domain specific interpreters
This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot.
Stock Chart Pattern recognition with Deep Learning
This study evaluates the performances of CNN and LSTM for recognizing common charts patterns in a stock historical data.
Using NLP on news headlines to predict index trends
This paper attempts to provide a state of the art in trend prediction using news headlines.
