Score-Based Change Detection for Gradient-Based Learning Machines

The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a continuous, possibly evolving, stream of data, it is desirable and often critical to supplement it with a companion change detection algorithm to facilitate its monitoring and control. We present a generic score-based change detection method that can detect a change in any number of components of a machine learning model trained via empirical risk minimization. This proposed statistical hypothesis test can be readily implemented for such models designed within a differentiable programming framework. We establish the consistency of the hypothesis test and show how to calibrate it to achieve a prescribed false alarm rate. We illustrate the versatility of the approach on synthetic and real data.

Autonomous Drone Racing with Deep Reinforcement Learning

In many robotic tasks, such as drone racing, the goal is to travel through a set of waypoints as fast as possible. A key challenge for this task is planning the minimum-time trajectory, which is typically solved by assuming perfect knowledge of the waypoints to pass in advance. The resulting solutions are either highly specialized for a single-track layout, or suboptimal due to simplifying assumptions about the platform dynamics. In this work, a new approach to minimum-time trajectory generation for quadrotors is presented. Leveraging deep reinforcement learning and relative gate observations, this approach can adaptively compute near-time-optimal trajectories for random track layouts. Our method exhibits a significant computational advantage over approaches based on trajectory optimization for non-trivial track configurations. The proposed approach is evaluated on a set of race tracks in simulation and the real world, achieving speeds of up to 17 m/s with a physical quadrotor.

Bio-inspired visual attention for silicon retinas based on spiking neural networks applied to pattern classification

Visual attention can be defined as the behavioral and cognitive process of selectively focusing on a discrete aspect of sensory cues while disregarding other perceivable information. This biological mechanism, more specifically saliency detection, has long been used in multimedia indexing to drive the analysis only on relevant parts of images or videos for further processing. The recent advent of silicon retinas (or event cameras -- sensors that measure pixel-wise changes in brightness and output asynchronous events accordingly) raises the question of how to adapt attention and saliency to the unconventional type of such sensors' output. Silicon retina aims to reproduce the biological retina behaviour. In that respect, they produce punctual events in time that can be construed as neural spikes and interpreted as such by a neural network. In particular, Spiking Neural Networks (SNNs) represent an asynchronous type of artificial neural network closer to biology than traditional artificial networks, mainly because they seek to mimic the dynamics of neural membrane and action potentials over time. SNNs receive and process information in the form of spike trains. Therefore, they make for a suitable candidate for the efficient processing and classification of incoming event patterns measured by silicon retinas. In this paper, we review the biological background behind the attentional mechanism, and introduce a case study of event videos classification with SNNs, using a biology-grounded low-level computational attention mechanism, with interesting preliminary results.

Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challenging problem. In order to alleviate the cold-start issue, most existing methods introduce content and contextual information as the auxiliary information. Nevertheless, these methods assume the recommended items behave steadily over time, while in a typical E-commerce scenario, items generally have very different performances throughout their life period. In such a situation, it would be beneficial to consider the long-term return from the item perspective, which is usually ignored in conventional methods. Reinforcement learning (RL) naturally fits such a long-term optimization problem, in which the recommender could identify high potential items, proactively allocate more user impressions to boost their growth, therefore improve the multi-period cumulative gains. Inspired by this idea, we model the process as a Partially Observable and Controllable Markov Decision Process (POC-MDP), and propose an actor-critic RL framework (RL-LTV) to incorporate the item lifetime values (LTV) into the recommendation. In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation. Scores suggested by the actor are then combined with classical ranking scores in a dual-rank framework, therefore the recommendation is balanced with the LTV consideration. Our method outperforms the strong live baseline with a relative improvement of 8.67% and 18.03% on IPV and GMV of cold-start items, on one of the largest E-commerce platform.

Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models

Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities. The critical component of such an MCMC algorithm is to construct good Metropolis-Hastings steps for updating the tree topology. However, such algorithms frequently suffering from local mode stickiness and poor mixing. As a result, the algorithms are slow to converge. Hitherto, authors have primarily used discrete-time birth/death mechanisms for Bayesian (sums of) regression tree models to explore the model space. These algorithms are efficient only if the acceptance rate is high which is not always the case. Here we overcome this issue by developing a new search algorithm which is based on a continuous-time birth-death Markov process. This search algorithm explores the model space by jumping between parameter spaces corresponding to different tree structures. In the proposed algorithm, the moves between models are always accepted which can dramatically improve the convergence and mixing properties of the MCMC algorithm. We provide theoretical support of the algorithm for Bayesian regression tree models and demonstrate its performance.

Comparison of Machine Learning Methods for Predicting Winter Wheat Yield in Germany

This study analyzed the performance of different machine learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. To address the seasonality, weekly features were used that explicitly take soil moisture conditions and meteorological events into account. Our results indicated that nonlinear models such as deep neural networks (DNN) and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models. The results also revealed that the deep neural networks often had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. As a result, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). The feature selection method estimated the individual effect of weather components, soil conditions, and phenology variables as well as the time that these variables become important. As such, our study indicates which variables have the most significant effect on winter wheat yield.

Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis

Most classical (non-spiking) neural network models disregard internal neuron dynamics and treat neurons as simple input integrators. However, biological neurons have an internal state governed by complex dynamics that plays a crucial role in learning, adaptation and the overall network activity and behaviour. This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model, which combines several biologically inspired mechanisms to efficiently simulate internal neuron dynamics with a single parameter analogous to the membrane time constant in biological neurons. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with fluctuating input. One consequence of the MPATH model is that it imbues neurons with a sense of time without recurrent connections, paving the way for modelling processes that depend on temporal aspects of neuron activity. Experiments demonstrate the model's ability to adapt to and continually learn from its input.

Time-aware Test Case Execution Scheduling for Cyber-Physical Systems

Testing cyber-physical systems involves the execution of test cases on target-machines equipped with the latest release of a software control system. When testing industrial robots, it is common that the target machines need to share some common resources, e.g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources. With a large number of such tests executed on a regular basis, this scheduling becomes difficult to manage manually. In fact, with manual test execution planning and scheduling, some robots may remain unoccupied for long periods of time and some test cases may not be executed. This paper introduces TC-Sched, a time-aware method for automated test case execution scheduling. TC-Sched uses Constraint Programming to schedule tests to run on multiple machines constrained by the tests' access to shared resources, such as measurement or networking devices. The CP model is written in SICStus Prolog and uses the Cumulatives global constraint. Given a set of test cases, a set of machines, and a set of shared resources, TC-Sched produces an execution schedule where each test is executed once with minimal time between when a source code change is committed and the test results are reported to the developer. Experiments reveal that TC-Sched can schedule 500 test cases over 100 machines in less than 4 minutes for 99.5% of the instances. In addition, TC-Sched largely outperforms simpler methods based on a greedy algorithm and is suitable for deployment on industrial robot testing.

A Time-Vertex Signal Processing Framework

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning.

AGGGEN: Ordering and Aggregating while Generating

We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation. In contrast to previous work using sentence planning, our model is still end-to-end: AGGGEN performs sentence planning at the same time as generating text by learning latent alignments (via semantic facts) between input representation and target text. Experiments on the WebNLG and E2E challenge data show that by using fact-based alignments our approach is more interpretable, expressive, robust to noise, and easier to control, while retaining the advantages of end-to-end systems in terms of fluency. Our code is available at https://github.com/XinnuoXu/AggGen.