A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft. Existing sense-and-avoid methods often require specialized sensors that are too large or power intensive for use on small unmanned vehicles. This paper presents a method to estimate object distances based on visual image sequences, allowing for the use of low-cost, on-board monocular cameras as simple collision avoidance sensors. We present a deep recurrent convolutional neural network and training method to generate depth maps from video sequences. Our network is trained using simulated camera and depth data generated with Microsoft's AirSim simulator. Empirically, we show that our model achieves superior performance compared to models generated using prior methods.We further demonstrate that the method can be used for sense-and-avoid of obstacles in simulation.

Model-based predictions of future trajectories of a dynamical system often suffer from inaccuracies, forcing model-based control algorithms to re-plan often, thus being computationally expensive, sub-optimal and not reliable. In this work, we propose a model-agnostic method for estimating the uncertainty of a model's predictions based on reconstruction error, using it in control and exploration. As our experiments show, this uncertainty estimation can be used to improve control performance on a wide variety of environments by choosing predictions of which the model is confident. It can also be used for active learning to explore more efficiently the environment by planning for trajectories with high uncertainty, allowing faster model learning.

It is a widely accepted principle that software without tests has bugs. Testing reinforcement learning agents is especially difficult because of the stochastic nature of both agents and environments, the complexity of state-of-the-art models, and the sequential nature of their predictions. Recently, the Arcade Learning Environment (ALE) has become one of the most widely used benchmark suites for deep learning research, and state-of-the-art Reinforcement Learning (RL) agents have been shown to routinely equal or exceed human performance on many ALE tasks. Since ALE is based on emulation of original Atari games, the environment does not provide semantically meaningful representations of internal game state. This means that ALE has limited utility as an environment for supporting testing or model introspection. We propose TOYBOX, a collection of reimplementations of these games that solves this critical problem and enables robust testing of RL agents.

A problem with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas too few may result in an underfit model. Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. In this tutorial, you will discover the Keras API for adding early stopping to overfit deep learning neural network models. How to Stop Training Deep Neural Networks At the Right Time With Using Early Stopping Photo by Ian D. Keating, some rights reserved. Callbacks provide a way to execute code and interact with the training model process automatically. Callbacks can be provided to the fit() function via the "callbacks" argument. First, callbacks must be instantiated.

This Hedge Fund Stocks Package is designed for investors and analysts who need predictions of the best-performing stocks according to I Know First's stock market algorithm. Package Name: Hedge Fund Stocks Recommended Positions: Long Forecast Length: 1 Year (12/06/2017 – 12/06/2018) I Know First Average: 23.31% Several predictions in this long-term 1 Year forecast saw significant returns. The algorithm had correctly predicted 6 out 10 stock movements. UAA was the top performing prediction with a return of 85.01%. With these notable trade returns, the package itself registered an average return of 23.31% compared to the S&P 500's return of 27.52% for the same period.

We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median 68.3 years [range 32.5–93.3], Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n 771, age median 68.0 years [range 32.5–93.3], We then employed a transfer learning approach to achieve the same for surgery patients (n 391, age median 69.1 years [range 37.2–88.0], We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] 0.70 [95% CI 0.63–0.78], The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p 0.001) and surgery (p 0.03) datasets.

This week, at the International Electron Devices Meeting (IEDM) and the Conference on Neural Information Processing Systems (NeurIPS), IBM researchers will showcase new hardware that will take AI further than it's been before: right to the edge. Our novel approaches for digital and analog AI chips boost speed and slash energy demand for deep learning, without sacrificing accuracy. On the digital side, we're setting the stage for a new industry standard in AI training with an approach that achieves full accuracy with eight-bit precision, accelerating training time by two to four times over today's systems. On the analog side, we report eight-bit precision--the highest yet--for an analog chip, roughly doubling accuracy compared with previous analog chips while consuming 33x less energy than a digital architecture of similar precision. These achievements herald a new era of computing hardware designed to unleash the full potential of AI.

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

E-commerce sponsored search contributes an important part of revenue for the e-commerce company. In consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. We name these stages as ad retrieval, ad pre-ranking and ad ranking. Ad retrieval and ad pre-ranking are collectively referred to as ad matching in this paper. We propose an end-to-end neural matching framework (EENMF) to model two tasks---vector-based ad retrieval and neural networks based ad pre-ranking. Under the deep matching framework, vector-based ad retrieval harnesses user recent behavior sequence to retrieve relevant ad candidates without the constraint of keyword bidding. Simultaneously, the deep model is employed to perform the global pre-ranking of ad candidates from multiple retrieval paths effectively and efficiently. Besides, the proposed model tries to optimize the pointwise cross-entropy loss which is consistent with the objective of predict models in the ranking stage. We conduct extensive evaluation to validate the performance of the proposed framework. In the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.

This survey paper presents a detailed overview of the applications for deep learning in ophthalmic diagnosis using retinal imaging techniques. The need of automated computer-aided deep learning models for medical diagnosis is discussed. Then a detailed review of the available retinal image datasets is provided. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of red lesions are reviewed.Recent deep learning models for classification of retinal disease including age-related macular degeneration, glaucoma, diabetic macular edema and diabetic retinopathy are also reported.