In many data analysis problems, the columns of A are corresponding to data points, for instance, images of pixel intensities. NMF has been successfully applied into many fields including image processing, text data mining and so on. It has been demonstrated that NMF is a powerful technique for dimension reduction. Compared to other well-known method, like singular value decomposition or principal component analysis, NMF is able to give more interpretable results due to its combinations of nonnegative basic vectors. In general, NMF is NPhard and the solution is not unique. It is necessary to impose additional constraints on the factor matrix like orthogonality constraints.

Decentralized optimization has been demonstrated to be very useful in machine learning. This work studies the communication-efficiency issue in decentralized optimization. We analyze the Periodic Decentralized Stochastic Gradient Descent (PD-SGD) algorithm, a straightforward combination of federated averaging and decentralized SGD. For the setting of for non-convex objective and non-identically distributed data, we prove that PD-SGD converges to a critical point. In particular, the number of local SGDs trades off communication and local computation. From an algorithmic perspective, we analyze a novel version of PD-SGD, which alternates between multiple local updates and multiple decentralized SGDs. We also show that when we periodically shrink the length of local updates, this generalized PD-SGD can better balance the communication-convergence trade-off both theoretically and empirically.

Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. By enforcing their integration in an iterative manner, we introduce an incremental learning method that is scalable to the number of sequential tasks in a continual learning process. Our approach is easy to implement and owns several favorable characteristics. First, it can avoid forgetting (i.e., learn new tasks while remembering all previous tasks). Second, it allows model expansion but can maintain the model compactness when handling sequential tasks. Besides, through our compaction and selection/expansion mechanism, we show that the knowledge accumulated through learning previous tasks is helpful to build a better model for the new tasks compared to training the models independently with tasks. Experimental results show that our approach can incrementally learn a deep model tackling multiple tasks without forgetting, while the model compactness is maintained with the performance more satisfiable than individual task training.

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.

In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear. We generate a simple instructional language for the GridWorld environment, that is built around three language elements (order connectors) defining the order of execution: one linear - "comma" and two non-linear - "but first", "but before". We apply one of the deep reinforcement learning baselines - Double DQN with frame stacking and ablate several extensions such as Prioritized Experience Replay and Gated-Attention architecture. Our results show that the introduction of non-linear order connectors improves the success rate on instructions with a higher number of sub-goals in 2-3 times, but it still does not exceed 20%. Also, we observe that the usage of Gated-Attention provides no competitive advantage against concatenation in this setting. Source code and experiments' results are available at https://github.com/vkurenkov/language-grounding-multigoal

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.

Recently, the traffic congestion in modern cities has become a growing worry for the residents. As presented in Baidu traffic report, the commuting stress index has reached surprising 1.973 in Beijing during rush hours, which results in longer trip time and increased vehicular queueing. Previous works have demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing systems and optimized bus transportation, the traffic efficiency could be significantly improved with little resource consumption. However, there are still two disadvantages that restrict their performance: (1) they only consider single scheduling in a short time, but ignoring the layout after first reposition, and (2) they only focus on the single transport. However, the multi-modal characteristics of urban public transportation are largely under-exploited. In this paper, we propose an efficient and economical multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal prediction, which adopts reinforcement learning to obtain optimal long-term and joint schedule. In JLRLS, we combines multiple transportation to conduct scheduling by their own characteristics, which potentially helps the system to reach the optimal performance. Our implementation of an example by PaddlePaddle is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling, with an explaining video at https://youtu.be/t5M2wVPhTyk.

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which may not be available to public health authorities wishing to elicit such behavioral changes, especially when dealing with a public health crises such as epidemic outbreaks. Here we develop an algorithm which builds on past advertising data to train a sequence-to-sequence Deep Neural Network which "translates" advertisements into optimized ads that are more likely to be clicked. The network is trained using more than 114 thousands ads shown on Microsoft Advertising. We apply this translator to two health related domains: Medical Symptoms (MS) and Preventative Healthcare (PH) and measure the improvements in click-through rates (CTR). Our experiments show that the generated ads are predicted to have higher CTR in 81% of MS ads and 76% of PH ads. To understand the differences between the generated ads and the original ones we develop estimators for the affective attributes of the ads. We show that the generated ads contain more calls-to-action and that they reflect higher valence (36% increase) and higher arousal (87%) on a sample of 1000 ads. Finally, we run an advertising campaign where 10 random ads and their rephrased versions from each of the domains are run in parallel. We show an average improvement in CTR of 68% for the generated ads compared to the original ads. Our results demonstrate the ability to automatically optimize advertisement for the health domain. We believe that our work offers health authorities an improved ability to help nudge people towards healthier behaviors while saving the time and cost needed to optimize advertising campaigns.

Model interpretation, or explanation of a machine learning classifier, aims to extract generalizable knowledge from a trained classifier into a human-understandable format, for various purposes such as model assessment, debugging and trust. From a computaional viewpoint, it is formulated as approximating the target classifier using a simpler interpretable model, such as rule models like a decision set/list/tree. Often, this approximation is handled as standard supervised learning and the only difference is that the labels are provided by the target classifier instead of ground truth. This paradigm is particularly popular because there exists a variety of well-studied supervised algorithms for learning an interpretable classifier. However, we argue that this paradigm is suboptimal for it does not utilize the unique property of the model interpretation problem, that is, the ability to generate synthetic instances and query the target classifier for their labels. We call this the active-query property, suggesting that we should consider model interpretation from an active learning perspective. Following this insight, we argue that the active-query property should be employed when designing a model interpretation algorithm, and that the generation of synthetic instances should be integrated seamlessly with the algorithm that learns the model interpretation. In this paper, we demonstrate that by doing so, it is possible to achieve more faithful interpretation with simpler model complexity. As a technical contribution, we present an active algorithm Active Decision Set Induction (ADS) to learn a decision set, a set of if-else rules, for model interpretation. ADS performs a local search over the space of all decision sets. In every iteration, ADS computes confidence intervals for the value of the objective function of all local actions and utilizes active-query to determine the best one.

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.