Learning expressive stochastic policies instead of deterministic ones has been proposed to
achieve better stability, sample complexity, and robustness. Notably, in Maximum Entropy …

Colorizing a given gray-level image is an important task in the media and advertising industry.
Due to the ambiguity inherent to colorization (many shades are often plausible), recent …

The recent growth of web video sharing platforms has increased the demand for systems
that can efficiently browse, retrieve and summarize video content. Query-aware multi-video …

Adversarial training is widely acknowledged as the most effective defense against
adversarial attacks. However, it is also well established that achieving both robustness and …

The fast development in power electronics and embedded processors has allowed an
increasing electrification of embedded systems during the last decade [5]. The vehicle industry …

Adversarial training has been empirically shown to be more prone to overfitting than standard
training. The exact underlying reasons are still not fully understood. In this paper, we …

Despite being a heavily researched topic, Adversarial Training (AT) is rarely, if ever, deployed
in practical AI systems for two primary reasons: (i) the gained robustness is frequently …

While deep learning has achieved huge success across different disciplines from computer
vision and natural language processing to computational biology and physical sciences, …

(57) ABSTRACT Methods and systems for accelerated input data conversion include partially
parsing an input data set to convert the data set from a first format to a second format in an …

Computational models are valuable tools for micro-metabolic studies on cellular specimens.
For example, they can be used for the approximation of absolute cell metabolic rates (given …