Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules, and efficiently generates novel molecules fulfilling various target conditions.

Deep neural networks (DNNs) have achieved great success in solving several longstanding tasks with near human-level intelligence, e.g., the ancient game of Go, image classification, and natural language processing. As a result, many potential applications are envisaged. However, major concerns have been raised about the readiness of applying this technique to safety-and security-critical systems, where faulty behaviour carries the risk of endangering human lives or potential damage to business. To address these concerns, similar to product development in avionics and automotive industries, a (safety or security) critical system implemented with DNNs, or comprising DNNs components, needs to be thoroughly tested and certified. The software industry relies on testing as a primary means to provide stakeholders with information about the quality of the software product or service under test [1].

Convolutional and Recurrent, deep neural networks have been successful in machine learning systems for computer vision, reinforcement learning, and other allied fields. However, the robustness of such neural networks is seldom apprised, especially after high classification accuracy has been attained. In this paper, we evaluate the robustness of three recurrent neural networks to tiny perturbations, on three widely used datasets, to argue that high accuracy does not always mean a stable and a robust (to bounded perturbations, adversarial attacks, etc.) system. Especially, normalizing the spectrum of the discrete recurrent network to bound the spectrum (using power method, Rayleigh quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that training of recurrent networks, say using gradient-based methods, often result in non-normal matrices that may or may not be diagonalizable. Therefore, the open problem lies in constructing methods that optimize not only for accuracy but also for the stability and the robustness of the underlying neural network, a criterion that is distinct from the other.

I've read that RNNs are particularly well suited for time series predictions, especially equipped with LSTM units that can learn from past data and estimate dependence between instants. This makes me wonder: can RNNs perform estimation like conventional Kalman filters do? For example, in the case of an IMU, typically Kalman filters predict the orientations given raw data from the individual sensors. Would the RNN be able to learn the mapping between raw IMU data and filtered orientations, and predict for future timesteps? If so, it brings me to my second question: would they also be able to'learn' the parameters that model the IMU: such as bias, noise etc.?

In December, Brazilian federal auditor Luis Andre Dutra e Silva improved the accuracy of cervical cancer screening by 81 percent using the Intel Deep Learning SDK and GoogleNet using Caffe to train a Supervised Semantics-Preserving Deep Hashing (SSDH) network. The driving factor behind the deep learning-based research that Silva and others are working on is that, as a leading cause of death, cancer affects millions of lives every year. Early detection and diagnosis can save not only billions of dollars but also countless lives. Much of the data generated from research, trials and tests, however, comes from incompatible and innumerable sources that can be complex and labour-intensive to properly examine. As a result, the bulk of this data remains largely underutilised.

This is going to be the most important focus area for 2018. Most enterprises have done proof of concepts on ML and are looking to realize the full value of their data with full fledged production implementations of the algorithms. The key technologies in this space may be Clipper. Clipper is the state-of-art ML serving system from Rise labs, Berkeley university and uses distributed computing concepts to scale models, containerized model deployment to handle models created in any platform and also performs cross-framework caching and batching to leverage parallel architectures like GPUs. Finally, Clipper can also perform cross-framework model composition using ML techniques like ensembling and multi-armed bandits.

The Summits will bring together 550 experts and 60 speakers using AI and deep learning to improve operations in manufacturing, and creating the next generation of intelligent robots.

Algorithmic trading using financial models has a rich multi-decade history, and artificial intelligence (AI) is the new emerging trend in the asset management landscape. Traditional trading algorithms were built to exploit specific opportunities, whereas the new generation of algorithms use the power of AI to truly act as independent agents participating in market action, and working day and night in a way that humans simply can't match. AI is a mixed bag of tricks including machine learning, deep learning, conversational interactive systems, and various other "bot" technologies. And the investment landscape is also being affected by AI- after all, AI can read and understand billions of pieces of data, which means one can spot trends better with its aid. It will help investors, portfolio managers, sovereign wealth funds (SWFs), and other financial institutions to predict the future prices more accurately and without the burden of emotional investing.

Actually, they're complimentary as in snacks on an airplane, since they're free, and complementary as in they work together. Deeplearning4j has a model import function. Our model import is chiefly focuses on models created with Keras 1 and 2, as well as TensorFlow. So Tensorflow users who need to deploy their models on a JVM stack have an easy way to do it. Deeplearning4j also makes it easy to perform more complex inference-related tasks; e.g.

Actually, they're complimentary as in snacks on an airplane, since they're free, and complementary as in they work together. Deeplearning4j has a model import function. Our model import is chiefly focuses on models created with Keras 1 and 2, as well as TensorFlow. So Tensorflow users who need to deploy their models on a JVM stack have an easy way to do it. Deeplearning4j also makes it easy to perform more complex inference-related tasks; e.g.