Convolutional neural networks (CNNs) and transfer learning have recently been used for 6 degrees of freedom (6-DoF) camera pose estimation. While they do not reach the same accuracy as visual SLAMbased approaches and are restricted to a specific environment, they excel in robustness and can be applied even to a single image. In this paper, we study PoseNet [1] and investigate modifications based on datasets' characteristics to improve the accuracy of the pose estimates. In particular, we emphasize the importance of field-of-view over image resolution; we present a data augmentation scheme to reduce overfitting; we study the effect of Long-Short-T erm-Memory (LSTM) cells. Lastly, we combine these modifications and improve PoseNet's performance for monocular CNN based camera pose regression.

Emotional Intelligence in Human-Computer Interaction has attracted increasing attention from researchers in multidisciplinary research fields including psychology, computer vision, neuroscience, artificial intelligence, and related disciplines. Human prone to naturally interact with computers face-to-face. Human Expressions is an important key to better link human and computers. Thus, designing interfaces able to understand human expressions and emotions can improve Human-Computer Interaction (HCI) for better communication. In this paper, we investigate HCI via a deep multi-facial patches aggregation network for Face Expression Recognition (FER). Deep features are extracted from facial parts and aggregated for expression classification. Several problems may affect the performance of the proposed framework like the small size of FER datasets and the high number of parameters to learn. For That, two data augmentation techniques are proposed for facial expression generation to expand the labeled training. The proposed framework is evaluated on the extended Cohn-Konade dataset (CK+) and promising results are achieved.

The Clarke subdifferential is not suited to tackle nonsmooth deep learning issues: backpropagation, mini-batches and steady states are not properly modelled. As a remedy, we introduce set valued conservative fields as surrogates to standard subdifferential mappings. We study their properties and provide elements of a calculus. Functions having a conservative field are called path differentiable. Convex/concave, semi-algebraic, or Clarke regular Lipschitz continuous functions are path differentiable as their corresponding subdifferentials are conservative. Another concrete and considerable class of examples of conservative fields, which are not subdifferential mappings, is given by the automatic differentiation oracle, as for instance the "subgradients" provided by the backpropagation algorithm in deep learning. Our differential model is eventually used to ensure subsequential convergence for nonsmooth stochastic gradient methods in the tame Lipschitz continuous setting offering the possibility of using mini-batches, the actual backpropagation oracle and $o(1/\log k)$ stepsizes.

Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously extract linguistic information from both student and reference answers; and (2) accurately model the semantic relations between free-text student and reference answers in open-ended domain. The proposed ASAG model is evaluated on a large real-world K-12 dataset and can outperform the state-of-the-art baselines in terms of various evaluation metrics. 1 Introduction Assessing the knowledge acquired by students is one of the most important aspects of the learning process as it provides feedback to help students correct their misunderstanding of knowledge and improves their overall learning performance. Traditionally, the assessing paradigm is often conducted by instructors or teachers. However, this access paradigm is not suitable in many cases especially when teaching resources are not readily available.

Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations. However, there exists a dark side of these models -- due to the vulnerability of neural networks, a neural dialogue model can be manipulated by users to say what they want, which brings in concerns about the security of practical chatbot services. In this work, we investigate whether we can craft inputs that lead a well-trained black-box neural dialogue model to generate targeted outputs. We formulate this as a reinforcement learning (RL) problem and train a Reverse Dialogue Generator which efficiently finds such inputs for targeted outputs. Experiments conducted on a representative neural dialogue model show that our proposed model is able to discover such desired inputs in a considerable portion of cases. Overall, our work reveals this weakness of neural dialogue models and may prompt further researches of developing corresponding solutions to avoid it.

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.

Interest in data science has risen remarkably in the last five years. And while there are many programming languages suited for data science and machine learning, Python is the most popular. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. David Cournapeau started it as a Google Summer of Code project. Since then, it's grown to over 20,000 commits and more than 90 releases.

Interest in data science has risen remarkably in the last five years. And while there are many programming languages suited for data science and machine learning, Python is the most popular. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. David Cournapeau started it as a Google Summer of Code project. Since then, it's grown to over 20,000 commits and more than 90 releases.

This past May I worked with the Internet Archive's Television News Archive to apply Google's suite of cloud AI APIs to analyze a week of television news coverage to examine how AI "sees" television and what insights we might gain into the world of non-consumptive deep learning-powered video understanding. Using Google's video, image, speech and natural language APIs as lenses, more than 600GB of machine annotations trace how deep learning algorithms today understand video. What lessons can we learn about the state of AI today and how it can be applied in creative ways to catalog and explore the vast world of video? Working with the Internet Archive's Television News Archive, a week of television news was selected covering CNN, MSNBC and Fox News and the morning and evening broadcasts of San Francisco affiliates KGO (ABC), KPIX (CBS), KNTV (NBC) and KQED (PBS) from April 15 to April 22, 2019, totaling 812 hours of television news. This week was selected due to it having two major stories, one national (the Mueller report release on April 18th) and one international (the Notre Dame fire on April 15th).

This library automates evaluation and analysis of any agent on these benchmarks. It serves to facilitate reproducible, and accessible, research on the core issues in RL, and ultimately the design of superior learning algorithms. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of the experiments from a committee of prominent researchers. For a more comprehensive overview, see the accompanying paper. This means any experiment will automatically output data in the correct format for analysis using the notebook, without any constraints on the structure of agents or algorithms.