Recognizing facial expressions from static images or video sequences is a widely studied but still challenging problem. The recent progresses obtained by deep neural architectures, or by ensembles of heterogeneous models, have shown that integrating multiple input representations leads to state-of-the-art results. In particular, the appearance and the shape of the input face, or the representations of some face parts, are commonly used to boost the quality of the recognizer. This paper investigates the application of Convolutional Neural Networks (CNNs) with the aim of building a versatile recognizer of expressions in static images that can be further applied to video sequences. We first study the importance of different face parts in the recognition task, focussing on appearance and shape-related features. Then we cast the learning problem in the Semi-Supervised setting, exploiting video data, where only a few frames are supervised. The unsupervised portion of the training data is used to enforce three types of coherence, namely temporal coherence, coherence among the predictions on the face parts and coherence between appearance and shape-based representation. Our experimental analysis shows that coherence constraints can improve the quality of the expression recognizer, thus offering a suitable basis to profitably exploit unsupervised video sequences. Finally we present some examples with occlusions where the shape-based predictor performs better than the appearance one.

We study universal finite sample expressivity of neural networks, defined as the capability to perfectly memorize arbitrary datasets. For scalar outputs, existing results require a hidden layer as wide as $N$ to memorize $N$ data points. In contrast, we prove that a 3-layer (2-hidden-layer) ReLU network with $4 \sqrt {N}$ hidden nodes can perfectly fit any arbitrary dataset. For $K$-class classification, we prove that a 4-layer ReLU network with $4 \sqrt{N} + 4K$ hidden neurons can memorize arbitrary datasets. For example, a 4-layer ReLU network with only 8,000 hidden nodes can memorize datasets with $N$ = 1M and $K$ = 1k (e.g., ImageNet). Our results show that even small networks already have tremendous overfitting capability, admitting zero empirical risk for any dataset. We also extend our results to deeper and narrower networks, and prove converse results showing necessity of $\Omega(N)$ parameters for shallow networks.

By using the viewpoint of modern computational algebraic geometry, we explore properties of the optimization landscapes of the deep linear neural network models. After clarifying on the various definitions of "flat" minima, we show that the geometrically flat minima, which are merely artifacts of residual continuous symmetries of the deep linear networks, can be straightforwardly removed by a generalized $L_2$ regularization. Then, we establish upper bounds on the number of isolated stationary points of these networks with the help of algebraic geometry. Using these upper bounds and utilizing a numerical algebraic geometry method, we find all stationary points of modest depth and matrix size. We show that in the presence of the non-zero regularization, deep linear networks indeed possess local minima which are not the global minima. Our computational results clarify certain aspects of the loss surfaces of deep linear networks and provide novel insights.

We present a novel intelligent tutoring system which builds upon well-established hypotheses in educational psychology and incorporates them inside of a scalable software architecture. Specifically, we build upon the known benefits of knowledge vocalization, parallel learning, and immediate feedback in the context of student learning. We show that open-source data combined with state-of-the-art techniques in deep learning and natural language processing can apply the benefits of these three factors at scale, while still operating at the granularity of individual student needs and recommendations. Additionally, we allow teachers to retain full control of the outputs of the algorithms, and provide student statistics to help better guide classroom discussions towards topics that would benefit from more in-person review and coverage. Our experiments and pilot programs show promising results, and cement our hypothesis that the system is flexible enough to serve a wide variety of purposes in both classroom and classroom-free settings.

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.

Buzzwords like "deep learning" and "neural networks" are everywhere, but so much of the popular understanding is misguided, says Terrence Sejnowski, a computational neuroscientist at the Salk Institute for Biological Studies. Sejnowski, a pioneer in the study of learning algorithms, is the author of The Deep Learning Revolution (out next week from MIT Press). He argues that the hype about killer AI or robots making us obsolete ignores exciting possibilities happening in the fields of computer science and neuroscience, and what can happen when artificial intelligence meets human intelligence. The Verge spoke to Sejnkowski about how "deep learning" suddenly became everywhere, what it can and cannot do, and the problem of hype. This interview has been lightly edited for clarity.

People throw around words like "artificial intelligence" and "neural networks" and "deep learning" and "machine learning" almost interchangeably.

Artificial Intelligence (AI) is getting a lot of attention these days, particularly in the technology industry and in corporate boardrooms. AI is also becoming prevalent in consumers everyday lives. Consumers don't always recognize it as such, as corporate marketing experts prefer to avoid technical jargon and instead use consumer friendly names like Siri and Alexa -- but for people that are more technically inclined, the ubiquitous presence of AI is hard to miss. AI is not a new concept. In fact, its roots go back several decades. Is this just another technology hype that is going to fade, or does it truly have the potential to bring about transformations, either good or bad, of epic proportions? Let's take a look at how we got here and why AI is suddenly capturing so much attention.

CarePredict, a leading AI-driven digital health company based in Fort Lauderdale, Florida and Menlo Park, California, has selected Lacuna Health, a wholly-owned subsidiary of Kindred Healthcare, LLC for an AI-powered Wellness Platform Collaboration. The partnership enables CarePredict to leverage Lacuna Health's highly trained registered nurse (RN) led clinical staff to offer providers a suite of care management enabled predictive health technology products to engage and support seniors and their caregivers. CarePredict, an AI-driven digital health company, develops proprietary connected remote sensing technologies, wearables, and deep learning platforms that continuously observe, learn, and trigger just-in-time care for seniors. CarePredict employs lightweight sensors and wearables, designed for seniors, to unobtrusively and autonomously collect rich data sets on the senior's activity and behaviors. Machine learning and unique kinematics algorithms are used to quantify activities performed by the senior, such as drinking, eating, sleeping, walking, grooming, etc. CarePredict uses these unique data sets to train its deep learning neural nets to surface insights such as signs and symptoms of self-neglect indicative of depression, unusual toileting patterns indicative of a urinary tract infection (UTI), or increased fall risk due to malnutrition, gait changes, lack of rest, and dehydration.

Nothing you've written is special enough to be worth selling or licensing, you can't use the code as proof of ability for a portfolio if you can't prove the code isn't plagiarized or badly designed and written, and it's certainly not any kind of tutorial. What you're doing doesn't fit any motives I can think of except a combination of stroking your own ego and delusion. What is your goal here, seriously?