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Relational database management systems (RDBMS) are widely used for the storage and retrieval of structured data. To derive insights beyond statistical aggregation, we typically have to extract specific subdatasets from the database using conventional database operations, and then apply deep neural networks (DNN) training and inference on these respective subdatasets in a separate machine learning system. The process can be prohibitively expensive, especially when there are a combinatorial number of subdatasets extracted for different analytical purposes. This calls for efficient in-database support of advanced analytical methods In this paper, we introduce LEADS, a novel SQL-aware dynamic model slicing technique to customize models for subdatasets specified by SQL queries. LEADS improves the predictive modeling of structured data via the mixture of experts (MoE) technique and maintains inference efficiency by a SQL-aware gating network. At the core of LEADS is the construction of a general model with multiple expert sub-models via MoE trained over the entire database. This SQL-aware MoE technique scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating only necessary experts via the gating network during inference. Additionally, we introduce two regularization terms during the training process of LEADS to strike a balance between effectiveness and efficiency. We also design and build an in-database inference system, called INDICES, to support end-to-end advanced structured data analytics by non-intrusively incorporating LEADS onto PostgreSQL. Our extensive experiments on real-world datasets demonstrate that LEADS consistently outperforms baseline models, and INDICES delivers effective in-database analytics with a considerable reduction in inference latency compared to traditional solutions.

The design of communication systems dedicated to machine learning tasks is one key aspect of goal-oriented communications. In this framework, this article investigates the interplay between data reconstruction and learning from the same compressed observations, particularly focusing on the regression problem. We establish achievable rate-generalization error regions for both parametric and non-parametric regression, where the generalization error measures the regression performance on previously unseen data. The analysis covers both asymptotic and finite block-length regimes, providing fundamental results and practical insights for the design of coding schemes dedicated to regression. The asymptotic analysis relies on conventional Wyner-Ziv coding schemes which we extend to study the convergence of the generalization error. The finite-length analysis uses the notions of information density and dispersion with additional term for the generalization error. We further investigate the trade-off between reconstruction and regression in both asymptotic and non-asymptotic regimes. Contrary to the existing literature which focused on other learning tasks, our results state that in the case of regression, there is no trade-off between data reconstruction and regression in the asymptotic regime. We also observe the same absence of trade-off for the considered achievable scheme in the finite-length regime, by analyzing correlation between distortion and generalization error.

We propose a scheme leveraging reinforcement learning to engineer control fields for generating non-classical states. It is exemplified by the application to prepare spin-squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performanceof the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement the control proposal. The extension to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.

We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.

This work aims to develop an integrated control strategy for Brushless Direct Current Motors for a wide range of applications in robotics systems. The controller is suited for both high torque - low speed and high-speed control of the motors. Hardware validation is done by developing a custom BLDC drive system, and the circuit elements are optimised for power efficiency.

Deep learning approaches are increasingly used to tackle forecasting tasks. A key factor in the successful application of these methods is a large enough training sample size, which is not always available. In these scenarios, synthetic data generation techniques are usually applied to augment the dataset. Data augmentation is typically applied before fitting a model. However, these approaches create a single augmented dataset, potentially limiting their effectiveness. This work introduces OnDAT (On-the-fly Data Augmentation for Time series) to address this issue by applying data augmentation during training and validation. Contrary to traditional methods that create a single, static augmented dataset beforehand, OnDAT performs augmentation on-the-fly. By generating a new augmented dataset on each iteration, the model is exposed to a constantly changing augmented data variations. We hypothesize this process enables a better exploration of the data space, which reduces the potential for overfitting and improves forecasting performance. We validated the proposed approach using a state-of-the-art deep learning forecasting method and 8 benchmark datasets containing a total of 75797 time series. The experiments suggest that OnDAT leads to better forecasting performance than a strategy that applies data augmentation before training as well as a strategy that does not involve data augmentation. The method and experiments are publicly available.

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data. User heterogeneity has imposed significant challenges to FL, which can incur drifted global models that are slow to converge. Knowledge Distillation has recently emerged to tackle this issue, by refining the server model using aggregated knowledge from heterogeneous users, other than directly averaging their model parameters. This approach, however, depends on a proxy dataset, making it impractical unless such a prerequisite is satisfied. Moreover, the ensemble knowledge is not fully utilized to guide local model learning, which may in turn affect the quality of the aggregated model. Inspired by the prior art, we propose a data-free knowledge distillation} approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then broadcasted to users, regulating local training using the learned knowledge as an inductive bias. Empirical studies powered by theoretical implications show that, our approach facilitates FL with better generalization performance using fewer communication rounds, compared with the state-of-the-art.

Benefit from the quick development of deep learning techniques, salient object detection has achieved remarkable progresses recently. However, there still exists following two major challenges that hinder its application in embedded devices, low resolution output and heavy model weight. To this end, this paper presents an accurate yet compact deep network for efficient salient object detection. More specifically, given a coarse saliency prediction in the deepest layer, we first employ residual learning to learn side-output residual features for saliency refinement, which can be achieved with very limited convolutional parameters while keep accuracy. Secondly, we further propose reverse attention to guide such side-output residual learning in a top-down manner. By erasing the current predicted salient regions from side-output features, the network can eventually explore the missing object parts and details which results in high resolution and accuracy. Experiments on six benchmark datasets demonstrate that the proposed approach compares favorably against state-of-the-art methods, and with advantages in terms of simplicity, efficiency (45 FPS) and model size (81 MB).

It is important to detect anomalous inputs when deploying machine learning systems. The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples. At the same time, diverse image and text data are available in enormous quantities. We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE). This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance. We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue. We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.