Selective manipulation of data attributes using deep generative models is an active area of research. In this paper, we present a novel method to structure the latent space of a Variational Auto-Encoder (VAE) to encode different continuous-valued attributes explicitly. This is accomplished by using an attribute regularization loss which enforces a monotonic relationship between the attribute values and the latent code of the dimension along which the attribute is to be encoded. Consequently, post-training, the model can be used to manipulate the attribute by simply changing the latent code of the corresponding regularized dimension. The results obtained from several quantitative and qualitative experiments show that the proposed method leads to disentangled and interpretable latent spaces that can be used to effectively manipulate a wide range of data attributes spanning image and symbolic music domains.

A popular way to estimate the causal effect of a variable x on y from observational data is to use an instrumental variable (IV): a third variable z that affects y only through x. The more strongly z is associated with x, the more reliable the estimate is, but such strong IVs are difficult to find. Instead, practitioners combine more commonly available IV candidates---which are not necessarily strong, or even valid, IVs---into a single "summary" that is plugged into causal effect estimators in place of an IV. In genetic epidemiology, such approaches are known as allele scores. Allele scores require strong assumptions---independence and validity of all IV candidates---for the resulting estimate to be reliable. To relax these assumptions, we propose Ivy, a new method to combine IV candidates that can handle correlated and invalid IV candidates in a robust manner. Theoretically, we characterize this robustness, its limits, and its impact on the resulting causal estimates. Empirically, Ivy can correctly identify the directionality of known relationships and is robust against false discovery (median effect size <= 0.025) on three real-world datasets with no causal effects, while allele scores return more biased estimates (median effect size >= 0.118).

Following the great minds of machine learning can help you discover new things and deepen your knowledge. It's fascinating to learn from the best scientists. Among them, you will find influencers, teachers, business leaders, and even many more. Undeniably their expertise can help to change the world and make it a better place. On this list, you will find not only influencers but also renowned personalities from the world of Data Science.

Google's AI phone assistant Duplex is contacting businesses across the UK and asking them what their coronavirus business hours are. It is using the responses to update company listings shown on Google Search and Google Maps. The Duplex AI assistant can be used by people in the US and New Zealand to make restaurant bookings and other reservations. But those features are not yet coming to the UK. Google chief executive Sundar Pichai wrote in a blog post that Google was planning to start using Duplex "where possible" to contact businesses about opening times.

Understanding cross-cultural differences is an important application of natural language understanding. This problem is difficult due to the relativism between cultures. We present a computational approach to learn cultural models that encode the general opinions and values of cultures from multi-lingual Wikipedia. Specifically, we assume a language is a symbol of a culture and different languages represent different cultures. Our model can automatically identify statements that potentially reflect cultural differences. Experiments on English and Chinese languages show that on a held out set of diverse topics, including marriage, gun control, democracy, etc., our model achieves high correlation with human judgements regarding within-culture values and cultural differences.

Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to model sequence-to-sequence maps. RNNs have been shown to have excellent expressive power but lack stability or robustness guarantees that would be necessary for safety-critical applications. In this paper we formulate convex sets of RNNs with guaranteed stability and robustness properties. The guarantees are derived using differential IQC methods and can ensure contraction (global exponential stability of all solutions) and bounds on incremental l2 gain (the Lipschitz constant of the learnt sequence-to-sequence mapping). An implicit model structure is employed to construct a jointly-convex representation of an RNN and its certificate of stability or robustness. We prove that the proposed model structure includes all previously-proposed convex sets of contracting RNNs as special cases, and also includes all stable linear dynamical systems. We demonstrate the utility of the proposed model class in the context of nonlinear system identification.

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which leads to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.

Reinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, causing the agent to experience ambiguity in terms of whether those actions are effective, an issue called the credit assignment problem. In this paper, we propose two strategies, inspired by behavioural psychology, to estimate a more informative reward value for actions with no reward. The first strategy, called self-punishment, discourages the agent to avoid making mistakes, i.e., actions which lead to a terminal state. The second strategy, called the rewards backfill, backpropagates the rewards between two rewarded actions. We prove that, under certain assumptions, these two strategies maintain the order of the policies in the space of all possible policies in terms of their total reward, and, by extension, maintain the optimal policy. We incorporated these two strategies into three popular deep reinforcement learning approaches and evaluated the results on thirty Atari games. After parameter tuning, our results indicate that the proposed strategies improve the tested methods in over 65 percent of tested games by up to over 25 times performance improvement.

San Francisco – Long maligned as job-stealers and aspiring overlords, robots are being increasingly relied on as fast, efficient, contagion-proof champions in the war against the deadly coronavirus. One team of robots temporarily cared for patients in a makeshift hospital in Wuhan, the Chinese city where the COVID-19 outbreak began. Meals were served, temperatures taken and communications handled by machines, one of them named "Cloud Ginger" by its maker CloudMinds, which has operations in Beijing and California. "It provided useful information, conversational engagement, entertainment with dancing, and even led patients through stretching exercises," CloudMinds president Karl Zhao said of the humanoid robot. "The smart field hospital was completely run by robots."