The key to a Transformer model is the self-attention mechanism, which allows the model to analyze an entire sequence in a computationally efficient manner. Recent work has suggested the possibility that general attention mechanisms used by RNNs could be replaced by active-memory mechanisms. In this work, we evaluate whether various active-memory mechanisms could replace self-attention in a Transformer. Our experiments suggest that active-memory alone achieves comparable results to the self-attention mechanism for language modelling, but optimal results are mostly achieved by using both active-memory and self-attention mechanisms together. We also note that, for some specific algorithmic tasks, active-memory mechanisms alone outperform both the self attention and a combination of the two.

ABSTRACT Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry. However, DNNs are vulnerable to adversarial machine learning attacks, in which noise is added to the input to change the network output. We have devised an image-processing-based method to detect adversarial images based on our observation that adversarial noise is reduced after applying these operations while the normal images almost remain unaffected. In addition to detection, this method can be used to restore the adversarial images' original labels, which is crucial to restoring the normal functionalities of DNN-based systems. Testing using an adversarial machine learning database we created for generating several types of attack using images from the ImageNet Large Scale Visual Recognition Challenge database demonstrated the efficiency of our proposed method for both detection and correction.

Regulation of Multi-Agent Systems (MAS) was a research topic of the past decade and one of these proposals was Electronic Institutions. However, with the recent reformulation of Artificial Neural Networks (ANN) as Deep Learning (DL), Security, Privacy, Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of ANN as Agent-based Training of a special type of regulated ANN that we call Institutional Neural Network. This paper introduces the former concept and provides $\mathcal{I}$, a language previously used to model and extend Electronic Institutions, as a means to implement and regulate DL.

Understanding stories is a challenging reading comprehension problem for machines as it requires reading a large volume of text and following long-range dependencies. In this paper, we introduce the Shmoop Corpus: a dataset of 231 stories that are paired with detailed multi-paragraph summaries for each individual chapter (7,234 chapters), where the summary is chronologically aligned with respect to the story chapter. From the corpus, we construct a set of common NLP tasks, including Cloze-form question answering and a simplified form of abstractive summarization, as benchmarks for reading comprehension on stories. We then show that the chronological alignment provides a strong supervisory signal that learning-based methods can exploit leading to significant improvements on these tasks. We believe that the unique structure of this corpus provides an important foothold towards making machine story comprehension more approachable.

In recent years, numerous benchmarks for commonsense reasoning have been presented which cover different areas: the Choice of Plausible Alternatives Challenge (COP A) [17] requires causal reasoning in everyday situations, the Winograd Schema Challenge [8] addresses difficult cases of pronoun disambiguation, the TriangleCOP A Challenge [9] focuses on human relationships and emotions, and the Story Cloze Test with the ROCStories Corpora [11] focuses on the ability to determine a plausible ending for a given short story, to name just a few. In our system, we focus on the COP A challenge where each problem consists of a problem description (the premise), a question, and two answer candidates (called alternatives). See Figure 1 for an example. Most approaches tackling these problems are based on machine learning or exploit statistical properties of the natural language input (see e.g.

A couple of days ago a friend of mine, who started exploring deep learning, asked me "Hey man, can you explain this Cross-entropy thing to me?". Now, that is a tough question, because this topic is never set with me right. My self-doubt kicked in, so in my mind, this question actually sounded more like "Can you explain Cross-entropy to yourself?". Even in my conference talks, I usually avoid mentioning it. Every time I start talking about it, I get all confused and convoluted.

Deep learning capstone project coursera Economics extended essay topic. Source: Coursera Deep Learning course The input layer and hidden layer are density connected, because every input feature is connected to every hidden layer feature. I have tried to provide multiple solutions for same problem like Using for loop & amp; Vectorized Implementation (optimiz course1:Neural Networks and Deep Learning c1_week1: Introduction to deep learning. May 21, 2018 · Coursera's, Introduction to Data Science in Python is a decent course to start off with Python as a tool. When I started as a machine learning engineer, my skills for exploring a dataset were subpar.

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Artificial Intelligence (AI), sometimes called machine intelligence, is Computer systems theory and engineering capable of performing tasks that typically require human intelligence, such as visual processing, speech recognition, decision-making, and language translation. To further expand this concept of AI in the scope of radiology results in "a computer science unit dealing with the processing, reconstruction, analysis and/or analysis of medical images by simulating intelligent human behavior in computers." Radiology, also defined as diagnostic imaging, is a series of different tests that take images of different parts of the body. Radiologists perform a wide array of diagnostic tests, including x-rays, ultrasound, densitometry of bone minerals, fluoroscopy, mammography, nuclear medicine, CT, and MRI.

The European Laboratory for Learning and Intelligent systems is a pan-European effort initiated in 2018 to foster European research excellence in machine learning and related fields. It aims to offer European researchers outstanding opportunities to carry out their research in Europe, and to nurture the next generation of European young researchers in this field of strategic importance. Its goal is to enable Europe to be competitive in modern AI and benefit from positive economic and societal impact. Today, ELLIS announces the establishment of the first 17 ELLIS units across 10 European countries and Israel. Built around outstanding AI researchers, the newly established research units are devoted to tackling fundamental challenges in AI with a focus on research excellence and societal impact.

A group of researchers from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) took 4th place in the international MRI-based adolescent intelligence prediction competition. For the first time ever, the Skoltech scientists used ensemble methods based on deep learning 3D networks to deal with this challenging prediction task. The results of their study were published in the journal Adolescent Brain Cognitive Development Neurocognitive Prediction. In 2013, the US National Institutes of Health (NIH) launched the first grand-scale study of its kind in adolescent brain research, Adolescent Brain Cognitive Development (ABCD, https://abcdstudy.org/), to see if and how teenagers' hobbies and habits affect their further brain development. Magnetic Resonance Imaging (MRI) is a common technique used to obtain images of human internal organs and tissues.