Novelty Detection Via Blurring. (arXiv:1911.11943v1 [cs.LG])

Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based…

Trading Convergence Rate with Computational Budget in High Dimensional Bayesian Optimization. (arXiv:1911.11950v1 [stat.ML])

Scaling Bayesian optimisation (BO) to high-dimensional search spaces is a active and open research problems particularly when no assumptions are made on function structure. The main reason is that at each iteration, BO requires to find global maximisation of acquisition function, which itself is a non-convex optimization problem in the original search space. With growing…

Label Dependent Deep Variational Paraphrase Generation. (arXiv:1911.11952v1 [cs.LG])

Generating paraphrases that are lexically similar but semantically different is a challenging task. Paraphrases of this form can be used to augment data sets for various NLP tasks such as machine reading comprehension and question answering with non-trivial negative examples. In this article, we propose a deep variational model to generate paraphrases conditioned on a…

Learning Endmember Dynamics in Multitemporal Hyperspectral Data Using a State-Space Model Formulation. (arXiv:1911.12020v1 [eess.IV])

Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a tremendous applicative potential and the avent of new satellite sensors with high temporal resolution, multitemporal hyperspectral unmixing is…

PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN. (arXiv:1911.12046v1 [cs.CR])

With more and more adoption of Deep Learning (DL) in the field of image processing, computer vision and NLP, researchers have begun to apply DL to tackle with encrypted traffic classification problems. Although these methods can automatically extract traffic features to overcome the difficulty of traditional classification methods like DPI in terms of feature engineering,…

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors. (arXiv:1911.12065v1 [eess.SY])

The performance of model predictive controllers (MPC) strongly depends on the model quality. In the field of electric drive control, white-box (WB) modeling approaches derived from first-order physical principles are most common. This procedure typically does not cover parasitic effects and parameter deviations are frequent. These issues are particularly crucial in the domain of self-commissioning…

SpoC: Spoofing Camera Fingerprints. (arXiv:1911.12069v1 [cs.CV])

Thanks to the fast progress in synthetic media generation, creating realistic false images has become very easy. Such images can be used to wrap rich fake news with enhanced credibility, spawning a new wave of high-impact, high-risk misinformation campaigns. Therefore, there is a fast-growing interest in reliable detectors of manipulated media. The most powerful detectors,…

Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines. (arXiv:1911.11819v1 [q-fin.TR])

Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to…

Dynamic Portfolio Management with Reinforcement Learning. (arXiv:1911.11880v1 [q-fin.PM])

Dynamic Portfolio Management is a domain that concerns the continuous redistribution of assets within a portfolio to maximize the total return in a given period of time. With the recent advancement in machine learning and artificial intelligence, many efforts have been put in designing and discovering efficient algorithmic ways to manage the portfolio. This paper…

With or without replacement? Sampling uncertainty in Shepp’s urn scheme. (arXiv:1911.11971v1 [math.PR])

We introduce a variant of Shepp’s classical urn problem in which the optimal stopper does not know whether sampling from the urn is done with or without replacement. By considering the problem’s continuous-time analog, we provide bounds on the value function and in the case of a balanced urn (with an equal number of each…