Stochastic gradient descent (SGD) plays a critical and foundational role in the field of deep learning. Even though the method is basic, pinpointing its success rate proves an arduous task. The stochastic gradient noise (SGN) is frequently cited as a factor driving the success of SGD during the training phase. This shared understanding frequently positions SGD as an Euler-Maruyama discretization of stochastic differential equations (SDEs), driven by Brownian or Levy stable motion. We posit in this study that SGN deviates significantly from both Gaussian and Lévy stable distributions. Drawing inspiration from the short-range correlations within the SGN data series, we propose that stochastic gradient descent (SGD) can be understood as a discretization of a stochastic differential equation (SDE) governed by fractional Brownian motion (FBM). Therefore, the diverse convergence behaviors exhibited by SGD are firmly established. The first passage time of an SDE driven by FBM is, in essence, approximately derived. The finding indicates a lower escape rate corresponding to a larger Hurst parameter, thereby inducing SGD to stay longer in the flat minima. The occurrence of this event aligns with the widely recognized phenomenon that stochastic gradient descent tends to favor flat minima, which are associated with superior generalization performance. To confirm our hypothesis, extensive experiments were undertaken, showcasing the persistence of short-term memory effects across diverse model architectures, datasets, and training methods. This study provides a new lens through which to view SGD and potentially advances our understanding.

Hyperspectral tensor completion (HTC) in remote sensing, essential for progress in space exploration and satellite imaging, has experienced a surge in interest from the recent machine learning community. diazepine biosynthesis The intricate web of closely spaced spectral bands within hyperspectral imagery (HSI) produces distinctive electromagnetic signatures for each material, thereby making it an essential tool for remote material identification. Yet, hyperspectral images obtained remotely exhibit a low degree of data purity, and their observations are frequently incomplete or corrupted during the transmission process. Therefore, the 3-D hyperspectral tensor's completion, encompassing two spatial dimensions and one spectral dimension, is a fundamental signal processing challenge for facilitating subsequent applications. The methodologies of benchmarking HTC often depend on the application of either supervised learning or non-convex optimization techniques. Functional analysis, as discussed in recent machine learning publications, designates John ellipsoid (JE) as a crucial topological framework for proficient hyperspectral analysis. We accordingly seek to employ this critical topology in this study, but this leads to a predicament. Computing JE mandates access to the complete HSI tensor, which is unavailable within the parameters of the HTC problem. The HTC dilemma is tackled by creating convex subproblems that improve computational efficiency, and we present superior HTC performance in our algorithm. Subsequent land cover classification accuracy on the recovered hyperspectral tensor is shown to be improved by our method.

Deep learning models needing to operate at the edge of a network often pose a substantial computational and memory burden, thus proving unfeasible for low-powered, embedded platforms, including mobile units and remote security systems. For this challenge, this article introduces a real-time, hybrid neuromorphic framework for object tracking and classification by utilizing event-based cameras. These cameras possess advantageous properties: low-power consumption (5-14 milliwatts) and high dynamic range (120 decibels). Notwithstanding conventional methods of event-by-event processing, this work has adopted a blended frame-and-event system to improve energy efficiency and high performance. Foreground event density forms the basis of a frame-based region proposal method for object tracking. A hardware-optimized system is created that addresses occlusion by leveraging apparent object velocity. The energy-efficient deep network (EEDN) pipeline reverses frame-based object track input into spike data for TrueNorth (TN) classification. The TN model is trained on the hardware track outputs from our initial data sets, not the typical ground truth object locations, and exemplifies our system's proficiency in handling practical surveillance scenarios, contrasting with conventional practices. As an alternative tracker, a C++ implementation of a continuous-time tracker is presented. In this tracker, each event is processed independently, thus leveraging the asynchronous and low-latency properties of neuromorphic vision sensors. We then extensively contrast the proposed methodologies with leading event-based and frame-based techniques for object tracking and classification, demonstrating the viability of our neuromorphic approach for real-time, embedded application requirements without trade-offs in performance. Lastly, the proposed neuromorphic system's performance is evaluated and compared against a standard RGB camera, utilizing hours of traffic footage for comprehensive testing.

The capacity for variable impedance regulation in robots, offered by model-based impedance learning control, results from online learning without relying on interaction force sensing. Despite the existence of pertinent findings, the guaranteed uniform ultimate boundedness (UUB) of closed-loop control systems hinges on periodic, iteration-dependent, or slowly varying human impedance characteristics. This paper presents a repetitive impedance learning control technique for the purpose of physical human-robot interaction (PHRI) in repetitive actions. A repetitive impedance learning term, an adaptive control term, and a proportional-differential (PD) control term form the foundation of the proposed control system. Estimating the uncertainties in robotic parameters over time utilizes differential adaptation with modifications to the projection. Estimating the iteratively changing uncertainties in human impedance is tackled by employing fully saturated repetitive learning. Using a PD controller, along with projection and full saturation for uncertainty estimation, guarantees the uniform convergence of tracking errors, demonstrably proven via a Lyapunov-like analysis. An iteration-independent component and an iteration-dependent disturbance factor, contribute to the stiffness and damping properties of impedance profiles. Repetitive learning estimates the former, and PD control compresses the latter, respectively. In conclusion, the developed method can be employed in the PHRI setting, recognizing the stiffness and damping changes that occur with each iteration. The effectiveness and benefits of the control system, as demonstrated by simulations on a parallel robot performing repetitive tasks, are validated.

We introduce a fresh approach to evaluating the inherent properties of deep neural networks. Our convolutional network-centric framework, however, can be adapted to any network architecture. We investigate two network characteristics, namely capacity, linked to expressiveness, and compression, related to the ease of learning. Just the network's design, and no other factor, defines these two characteristics, which remain unchanged regardless of the network's parameters. For this endeavor, we introduce two metrics. The first, layer complexity, gauges the architectural intricacy of a network layer; and the second, layer intrinsic power, mirrors the compression of data within the network. one-step immunoassay Layer algebra, a concept introduced in this article, forms the basis of these metrics. The dependence of global properties on network topology is central to this concept. Local transfer functions can approximate the leaf nodes of any neural network, enabling a straightforward method for computing global metrics. Compared to the VC dimension, our global complexity metric offers a more manageable calculation and representation. read more By employing our metrics, we scrutinize the properties of various current state-of-the-art architectures to subsequently assess their performance on benchmark image classification datasets.

Recognition of emotions through brain signals has seen a rise in recent interest, given its strong potential for integration into human-computer interfaces. To better understand the emotional interaction between intelligent systems and humans, researchers have devoted considerable effort to interpreting human emotions from brain scans. Most current attempts to model emotion and brain activity hinge on utilizing parallels in emotional expressions (for instance, emotion graphs) or parallels in the functions of different brain areas (e.g., brain networks). Yet, the relationship between feelings and the associated brain areas is not explicitly part of the representation learning framework. Therefore, the representations learned might not hold sufficient detail for certain applications, such as deciphering emotions. This paper presents a novel method of graph-enhanced neural decoding for emotions. It employs a bipartite graph structure to integrate emotional and brain region associations into the decoding process, leading to improved learned representations. Theoretical analyses posit that the proposed emotion-brain bipartite graph encompasses and extends the established emotion graphs and brain networks. Comprehensive experiments on visually evoked emotion datasets showcase the superior effectiveness of our approach.

Quantitative magnetic resonance (MR) T1 mapping provides a promising method for the elucidation of intrinsic tissue-dependent information. However, the extended scanning time poses a significant obstacle to its widespread adoption. Low-rank tensor models have been adopted in recent times, exhibiting outstanding performance in accelerating the MR T1 mapping process.