Most people in the deep learning and computer vision communities understand what image classification is: we want our model to tell us what single object or scene is present in the image. Classification is very coarse and high-level. Many are also familiar with object detection, where we try to locate and classify multiple objects within the image, by drawing bounding boxes around them and then classifying what's in the box. Detection is mid-level, where we have some pretty useful and detailed information, but it's still a bit rough since we're only drawing bounding boxes and don't really get an accurate idea of object shape. Semantic Segmentation is the most informative of these three, where we wish to classify each and every pixel in the image, just like you see in the gif above!

Artificial intelligence holds a great deal of promise for medical professionals who want to get the most out of medical imaging. However, when it comes to studying brain tumors, there's an inherent problem with the data: abnormal brain images are, by definition, uncommon. New research from Nvidia aims to solve that. A group of researchers from Nvidia, the Mayo Clinic, and the MGH & BWH Center for Clinical Data Science this weekend are presenting a paper on their work using generative adversarial networks (GANs) to create synthetic brain MRI images. GANs are effectively two AI systems that are pitted against each other -- one that creates synthetic results within a category, and one that identifies the fake results.

"Deepfake" is the name being given to videos created through artificially intelligent deep learning techniques. Also referred to as "face-swapping", the process involves inputting a source video of a person into a computer, and then inputting multiple images and videos of another person. The neural network then learns the movements and expressions of the person in the source video in order to map the other's image onto it to look as if they are carrying out the speech or act. This practice was first used extensively in the production of fake pornography in late 2017 – where the faces of famous female celebrities were swapped in. Research has consistently shown that pornography leads the way in technological adoption and advancement when it comes to communication technologies, from the Polaroid camera to the internet.

Creating agents that can learn like children is one of the ultimate goals of artificial intelligence. Disciplines such as reinforcement learning(RL) are fully devoted to create self-learning models that can use a combination of punishment and reward feedback to master a new task. However, most RL techniques suffer from two main challenges. One very well known is the exploration-explotaition dilemma in which an agent needs to decide how many resources to dedicate to exploring the environment vs. taking specific actions. The other and far less know challenge of RL methods is what I like to call the prior knowledge imbalance dynamic.

Microsoft today announced that is has acquired Lobe, a startup that lets you build machine learning models with the help of a simple drag-and-drop interface. Microsoft plans to use Lobe, which only launched into beta earlier this year, to build upon its own efforts to make building AI models easier, though, for the time being, Lobe will operate as before. "As part of Microsoft, Lobe will be able to leverage world-class AI research, global infrastructure, and decades of experience building developer tools," the team writes. "We plan to continue developing Lobe as a standalone service, supporting open source standards and multiple platforms." Lobe was co-founded by Mike Matas, who previously worked on the iPhone and iPad, as well as Facebook's Paper and Instant Articles products.

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We introduce a dynamic artificial neural network-based (ANN) adaptive inference process, which learns temporal predictive models of dynamical systems. We term the process REPRISE, a REtrospective and PRospective Inference SchEme. REPRISE infers the unobservable contextual state that best explains its recently encountered sensorimotor experiences as well as accompanying, context-dependent temporal predictive models retrospectively. Meanwhile, it executes prospective inference, optimizing upcoming motor activities in a goal-directed manner. In a first implementation, a recurrent neural network (RNN) is trained to learn a temporal forward model, which predicts the sensorimotor contingencies of different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the compact encoding of distinct, but related sensorimotor dynamics. We show that REPRISE is able to concurrently learn to separate and approximate the encountered sensorimotor dynamics. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to a given goal. Meanwhile, the system evaluates the encountered sensorimotor contingencies retrospectively, adapting its neural hidden states for maintaining model coherence. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing both, hidden state and motor activities. In conclusion, the combination of temporal predictive structures with modulatory, generative encodings offers a way to develop compact event codes, which selectively activate particular types of sensorimotor event-specific dynamics.

Recently, path norm was proposed as a new capacity measure for neural networks with Rectified Linear Unit (ReLU) activation function, which takes the rescaling-invariant property of ReLU into account. It has been shown that the generalization error bound in terms of the path norm explains the empirical generalization behaviors of the ReLU neural networks better than that of other capacity measures. Moreover, optimization algorithms which take path norm as the regularization term to the loss function, like Path-SGD, have been shown to achieve better generalization performance. However, the path norm counts the values of all paths, and hence the capacity measure based on path norm could be improperly influenced by the dependency among different paths. It is also known that each path of a ReLU network can be represented by a small group of linearly independent basis paths with multiplication and division operation, which indicates that the generalization behavior of the network only depends on only a few basis paths. Motivated by this, we propose a new norm \emph{Basis-path Norm} based on a group of linearly independent paths to measure the capacity of neural networks more accurately. We establish a generalization error bound based on this basis path norm, and show it explains the generalization behaviors of ReLU networks more accurately than previous capacity measures via extensive experiments. In addition, we develop optimization algorithms which minimize the empirical risk regularized by the basis-path norm. Our experiments on benchmark datasets demonstrate that the proposed regularization method achieves clearly better performance on the test set than the previous regularization approaches.

The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), eg, in reinforcement learning and meta-learning. While deriving the first-order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher-order derivatives is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order derivative involves increasingly cumbersome graph manipulations. Lastly, to match the first-order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher-order derivatives. To address all these shortcomings in a unified way, we introduce DiCE, which provides a single objective that can be differentiated repeatedly, generating correct estimators of derivatives of any order in SCGs. Unlike SL, DiCE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DiCE both through a proof and numerical evaluation of the DiCE derivative estimates. We also use DiCE to propose and evaluate a novel approach for multi-agent learning. Our code is available at https://www.github.com/alshedivat/lola.

The morphological structure of left ventricle segmented from cardiac magnetic resonance images can be used to calculate key clinical parameters, and it is of great significance to the accurate and efficient diagnosis of cardiovascular diseases. Compared with traditional methods, the segmentation algorithms based on fully convolutional neural network greatly improve the accuracy of semantic segmentation. For the problem of left ventricular segmentation, a new fully convolutional neural network structure named MS-FCN is proposed in this paper. The MS-FCN network employs a multi-scale pooling module to ensure that the network maximises the feature extraction ability and uses a dense connectivity decoder to refine the boundaries of the object. Based on the Sunnybrook cine-MR dataset provided by the MICCAI 2009 challenge, numerical experiments demonstrate that our proposed model has obtained state-of-the-art segmentation results: the Dice score of our method reaches 0.93 on the endocardium, and 0.96 on the epicardium. Keywords: left ventricle, semantic segmentation, convolutional neural network, multi-scale.