Now, imagine the same cat, but with coal-black fur. Now, imagine the cat strutting along the Great Wall of China. Doing this, a quick series of neuron activations in your brain will come up with variations of the picture presented, based on your previous knowledge of the world. In other words, as humans, it's easy to envision an object with different attributes. But, despite advances in deep neural networks that match or surpass human performance in certain tasks, computers still struggle with the very human skill of "imagination."

Deep learning owes a lot of its success to automatic differentiation. Popular libraries such as TensorFlow and PyTorch keep track of gradients over neural network parameters during training with both comprising high-level APIs for implementing the commonly used neural network functionality for deep learning. JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research. Along with a Deep Learning framework, JAX has created a super polished linear algebra library with automatic differentiation and XLA support. JAX is a new machine learning library from Google designed for high-performance numerical computing.

Last year DeepMind presented AlphaFold v2, which predicts 3D structures of proteins down to atomic accuracy. Today they share the methods in their latest paper at Nature along with open source codes. It is inspiring to see the research this enables. This new model, AlphaFold v2.0 has been published in Nature and entered into the CASP14 competition. Deepmind has pushed the boundaries of computing.

Students who have been concerned about data science and machine learning will often discuss deep learning and neural networks. If you are interested in deep learning but have not actually done it, you will learn from here. In this article, you will learn about TensorFlow and its practical role in neural networks and will try to solve real-life problems. Before reading this article, you need to know the basic knowledge of neural networks and some programming concepts. The code in the article is written using Python, so you also need to understand some of the basic syntaxes of Python in order to better understand the article. Neural networks, also known as simulated neural networks (SNNs), or artificial neural networks (ANNs) are a subset of machine learning.

Google has proved time and again that it will always be one of the forerunners when it comes to Deep Learning solutions. Recently a team at Google Brain introduced a newer method for Image Super-Resolution. From being just a Science Fiction dream to one of the most researched fields Image Super-Resolution has seen constant growth and innovation over the years especially with the aid of Deep Learning architectures. From the first iterations of SRCNN to SRResNET and then SRGAN that re-established how Super-Resolution was looked at. As a young contributor to the field, I can say that it is if not one of the most interesting problem spaces to work in.

Magic Leap's mission is to deliver enterprise a powerful tool for transformation-- an augmented reality platform of great utility and simplicity. Our ultimate vision is to amplify human potential. Our office in Zurich, Switzerland is a center of excellence for Computer Vision and Deep Learning. We are looking for exceptional engineers, passionate about shaping the future of computing. As a Computer Vision R&D Engineer, you'll be responsible for delivering high-performance production software with state-of-the-art computer vision capabilities in the field of SLAM and sensor fusion.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals.

The researchers, led by Patrick Aloy, are applying deep machine learning to a massive dataset: the Chemical Checker, which provides processed, …

Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dynamics of random event timings directly from visual or audio inputs, requiring timing annotations during training that are usually unavailable for real-world applications. For instance, neuroscience perspectives on postdiction imply that there exist variable temporal ranges within which the incoming sensory inputs can affect the earlier perception, but such temporal ranges are mostly unannotated for real applications such as automatic speech recognition (ASR). In this paper, we present a probabilistic ordinary differential equation (ODE), called STochastic boundaRy ODE (STRODE), that learns both the timings and the dynamics of time series data without requiring any timing annotations during training. STRODE allows the usage of differential equations to sample from the posterior point processes, efficiently and analytically. We further provide theoretical guarantees on the learning of STRODE. Our empirical results show that our approach successfully infers event timings of time series data. Our method achieves competitive or superior performances compared to existing state-of-the-art methods for both synthetic and real-world datasets.

We study the problem of post-hoc calibration for multiclass classification, with an emphasis on histogram binning. Multiple works have focused on calibration with respect to the confidence of just the predicted class (or 'top-label'). We find that the popular notion of confidence calibration [Guo et al., 2017] is not sufficiently strong -- there exist predictors that are not calibrated in any meaningful way but are perfectly confidence calibrated. We propose a closely related (but subtly different) notion, top-label calibration, that accurately captures the intuition and simplicity of confidence calibration, but addresses its drawbacks. We formalize a histogram binning (HB) algorithm that reduces top-label multiclass calibration to the binary case, prove that it has clean theoretical guarantees without distributional assumptions, and perform a methodical study of its practical performance. Some prediction tasks require stricter notions of multiclass calibration such as class-wise or canonical calibration. We formalize appropriate HB algorithms corresponding to each of these goals. In experiments with deep neural nets, we find that our principled versions of HB are often better than temperature scaling, for both top-label and class-wise calibration. Code for this work will be made publicly available at https://github.com/aigen/df-posthoc-calibration.