Personal use of this material is permitted. Abstract --Pre-training a deep neural network on the ImageNet dataset is a common practice for training deep learning models, and generally yields improved performance and faster training times. The technique of pre-training on one task and then retraining on a new one is called transfer learning. We perform three sets of experiments with varying levels of similarity between source and target tasks to investigate the behaviour of different types of knowledge transfer . We transfer both parameters and features and analyse their behaviour . Our results demonstrate that no significant advantage is gained by using a transfer learning approach over a traditional machine learning approach for our character recognition tasks. This suggests that using transfer learning does not necessarily presuppose a better performing model in all cases. Learning to drive a car makes learning to drive a truck easier, and knowing how to speak Spanish makes learning Portuguese easier.

Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.

Machine learning (ML) is quickly emerging as a powerful tool with diverse applications across an extremely broad spectrum of disciplines and commercial endeavors. Typically, ML is used as a black box that provides little illuminating rationalization of its output. In the current work, we aim to better understand the generic intuition underlying unsupervised ML with a focus on physical systems. The systems that are studied here as test cases comprise of six different 2-dimensional (2-D) particulate systems of different complexities. It is noted that the findings of this study are generic to any unsupervised ML problem and are not restricted to materials systems alone. Three rudimentary unsupervised ML techniques are employed on the adjacency (connectivity) matrix of the six studied systems: (i) using principal eigenvalue and eigenvectors of the adjacency matrix, (ii) spectral decomposition, and (iii) a Potts model based community detection technique in which a modularity function is maximized. We demonstrate that, while solving a completely classical problem, ML technique produces features that are distinctly connected to quantum mechanical solutions. Dissecting these features help us to understand the deep connection between the classical non-linear world and the quantum mechanical linear world through the kaleidoscope of ML technique, which might have far reaching consequences both in the arena of physical sciences and ML.

The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Skorokhodov and Burtsev, 2019) demonstrated that one can empirically find arbitrary 2D binary patterns inside loss surfaces of popular neural networks. In this paper we prove that: (i) this is a general property of deep universal approximators; and (ii) this property holds for arbitrary smooth patterns, for other dimensionalities, for every dataset, and any neural network that is sufficiently deep and wide. Our analysis predicts not only the existence of all such low-dimensional patterns, but also two other properties that were observed empirically: (i) that it is easy to find these patterns; and (ii) that they transfer to other data-sets (e.g. a test-set).

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines.

Many of those researchers will have PhDs and long CVs full of peer-reviewed research articles. ML engineering is often the applied side of the (more theoretical) research that began in theirs and their peers' minds. I would recommend identifying pathways for collaborating with established ML researchers on published (and refereed) research papers. Another major difference between an ML engineer and an ML researcher to keep in mind is that the ML researchers at major companies and universities are world-class experts in their respective very very narrow subfields. Some of them will have invented the entire topic that they're an expert in.

Artificial Intelligence (AI) and machine learning innovation have been growing quickly as of late, with conceivable outcomes developing in tandem with more noteworthy accessibility of data and headways in computing capability and storage solutions. Truth be told, if you look in the background, you can spot numerous instances of machine learning innovation as of now already in a wide range of industries ranging from consumer products and social media to financial services and manufacturing. Machine learning can turn into a strong analytical tool for huge volumes of data. The blend of machine learning and edge computing can channel a large portion of the commotion gathered by IoT gadgets and leave the significant information to be analyzed by the edge and cloud analytic engines. The advances in Artificial Intelligence have enabled us to see self-driving vehicles, speech recognition, active web search, and facial and image recognition. Machine learning is the establishment of those frameworks.

Wattpad announced it was making its debut into the physical book world using its own its Story DNA... [ ] Machine Learning technology. Last week the online publishing website Wattpad announced it was making its debut into the physical book world with its own direct publishing division, Wattpad Books. The company already picked six debut stories to bring to print in 2019, with the help of its Story DNA Machine Learning technology. Unlike past books from Wattpad that have been published via other publishers, Wattpad Books will be its own direct division. Wattpad partnered with Macmillan in the United States and Raincoast Books in Canada to help distribute their books across North America; all books will be available as paperback or e-books, and a few will get hardcover publication.

Artificial Intelligence (AI) is making its way into the realm of clinical trials. While most of the talk I hear seems to center on clinical trial recruitment and using AI to mine electronic medical records (EHRs), that application seems to only scratch the surface. Experts predict monitoring drug adherence, pre-emptive risk monitoring, decision-making, diagnostics, and process optimization are other areas where the technology is expected to make an impact. By the middle of 2020, the AI market for healthcare is expected to top $35 billion, and big names such as Microsoft, Google, and IBM are already collaborating with top universities to further AI. We engaged experts from four of the largest companies in the industry to provide insights on the implementation of AI in clinical trials and the challenges companies are facing.

Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. To make it clearer: you don't really know what your model actually learned and if you have a flaw in your training / data approach it might work well according to your metrics while having learnt the wrong thing. As a self-respecting developer you want to do better than that, so today I will show you a method you can use to get some better introspection into your model by using visualization techniques. So what is a visualization techniqe when we talk about deep neural networks?