In recent years, supervised machine learning approaches showed spectacular results in various image analysis problems [1]. Based on massive, annotated data sets, deep learning systems have come to the point where they are on par or even outperform humans in specific tasks [2] [3] [4]. However, fully annotated data sets are typically not available or even feasible to create in many domains. Manual reference annotations for pixel-level semantic segmentation in biomedical imaging are particularly costly as they can be too time-consuming and require considerable expert knowledge that might not readily be available. Here, semi-and self-supervised learning methods are promising approaches to build generalizable segmentation tools as they can leverage raw data and require only a few or no labels at all. These methods yield encouraging results in computer vision tasks on natural images.

In this paper, we present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers and we provide ellipsoidal inner approximations of the corresponding region of attraction. We consider a feedback interconnection using a neural network controller in connection with an integrator, which allows for offset-free tracking of a desired piecewise constant reference that enters the controller as an external input. The feedback interconnection considered in this paper allows for general configurations of the neural network controller that include the special cases of output error and state feedback. Exploiting the fact that activation functions used in neural networks are slope-restricted, we derive linear matrix inequalities to verify stability using Lyapunov theory. After stating a global stability result, we present less conservative local stability conditions (i) for a given reference and (ii) for any reference from a certain set. The latter result even enables guaranteed tracking under setpoint changes using a reference governor which can lead to a significant increase of the region of attraction. Finally, we demonstrate the applicability of our analysis by verifying stability and offset-free tracking of a neural network controller that was trained to stabilize an inverted pendulum.

Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease, and could be further developed to generate other useful predictions such as new cases or hospitalizations intuitively.

Knowledge distillation between machine learning models has opened many new avenues for parameter count reduction, performance improvements, or amortizing training time when changing architectures between the teacher and student network. In the case of reinforcement learning, this technique has also been applied to distill teacher policies to students. Until now, policy distillation required access to a simulator or real world trajectories. In this paper we introduce a simulator-free approach to knowledge distillation in the context of reinforcement learning. A key challenge is having the student learn the multiplicity of cases that correspond to a given action. While prior work has shown that data-free knowledge distillation is possible with supervised learning models by generating synthetic examples, these approaches to are vulnerable to only producing a single prototype example for each class. We propose an extension to explicitly handle multiple observations per output class that seeks to find as many exemplars as possible for a given output class by reinitializing our data generator and making use of an adversarial loss. To the best of our knowledge, this is the first demonstration of simulator-free knowledge distillation between a teacher and a student policy. This new approach improves over the state of the art on data-free learning of student networks on benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10), and we also demonstrate that it specifically tackles issues with multiple input modes. We also identify open problems when distilling agents trained in high dimensional environments such as Pong, Breakout, or Seaquest.

We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks. Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task, despite the presence of other predictive features that fail to be discovered. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks. Using tools from Dynamical Systems theory, we identify simple properties of learning dynamics during gradient descent that lead to this imbalance, and prove that such a situation can be expected given certain statistical structure in training data. Based on our proposed formalism, we develop guarantees for a novel regularization method aimed at decoupling feature learning dynamics, improving accuracy and robustness in cases hindered by gradient starvation. We illustrate our findings with simple and real-world out-of-distribution (OOD) generalization experiments.

In building a deep neural network, especially using some of the higher level frameworks such as Keras, we often don't fully understand what's happening in each layer. The sequential model will get you far indeed, but when it's time to do something more complex or intriguing, you will need to dive into the details. In this article, I'm going to explain exactly what's happening as you pass a batch of data through an LSTM layer with an example from PyTorch. I want to note that, I won't be covering any of the exact mechanics of the LSTM cells or why they are useful. If you're reading this, you're probably aware of the vanishing gradient problem and understand the basics of the gating mechanisms.

PyTorch Lightning has its humble beginnings as a project that I developed during the first few years of my Ph.D. at NYU CILVR and later at Facebook AI Research. At NYU it gained the powers of rapid iteration and standardization that makes Lightning a pleasure to work with today -- it standardizes AI research code so everyone's code can be formatted the same way, and thus it becomes more readable and reproducible. At FAIR it learned how to train massive neural networks across hundreds of GPUs. But had I remained the only developer of the project it would be nowhere near where it is today as a quickly rising favorite for deep learning research. Our first non-facebook contributor Jirka, forced much-needed formatting and structuring to the internals.

MIT researchers have developed a way for deep learning neural networks to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision making. A faster way to estimate uncertainty in AI-assisted decision-making could lead to safer outcomes. Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making.

Deep learning has been quite popular for image recognition and classification tasks in recent years due to its high performances. However, traditional deep learning approaches usually require a large dataset for the model to be trained on to distinguish very few different classes, which is drastically different from how humans are able to learn from even very few examples. Few-shot or one-shot learning is a categorization problem that aims to classify objects given only a limited amount of samples, with the ultimate goal of creating a more human-like learning algorithm. In this article, we will dive into the deep learning approaches to solving the one-shot learning problem by using a special network structure: Siamese Network. We will build the network using PyTorch and test it on the Omniglot handwritten character dataset and performed several experiments to compare the results of different network structures and hyperparameters, using a one-shot learning evaluation metric.

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