We further contribute a novel hierarchical neural network for the perceptual parsing of 3-D surfaces, named PicassoNet++, by leveraging its modular operations. Its performance in shape analysis and scene segmentation on prominent 3-D benchmarks is highly competitive. At https://github.com/EnyaHermite/Picasso, you'll find the code, data, and trained models for the Picasso project.
An adaptive neurodynamic method, tailored for multi-agent systems, is presented in this article for addressing nonsmooth distributed resource allocation problems (DRAPs) with affine-coupled equality constraints, coupled inequality constraints, and individually-held private information sets. Essentially, agents concentrate on optimizing resource assignment to reduce team expenditures, given the presence of broader limitations. The considered constraints, including multiple coupled constraints, are resolved through the addition of auxiliary variables, which guide the Lagrange multipliers towards agreement. Additionally, an adaptive controller, backed by the penalty method, is developed to address the limitations imposed by private set constraints, ensuring that global information remains undisclosed. Employing Lyapunov stability theory, the convergence of the neurodynamic approach is scrutinized. FK866 in vitro To mitigate the communicative burden borne by systems, the suggested neurodynamic approach is strengthened by implementing an event-triggered mechanism. Not only is the convergence property considered, but the Zeno phenomenon is also absent in this case. In a virtual 5G system, a simplified problem and a numerical example are executed to exemplify the efficacy of the proposed neurodynamic approaches, in conclusion.
The k-winner-take-all (WTA) model, driven by a dual neural network (DNN), possesses the capability to ascertain the k largest numbers among its m inputs. Realizations incorporating non-ideal step functions and Gaussian input noise as imperfections can yield incorrect model output. The influence of imperfections on the model's operational integrity is evaluated in this brief. The imperfections inherent in the original DNN-k WTA dynamics make them inefficient for influence analysis. In this connection, this initial compact model generates a comparable model to portray the model's functional behavior under imperfect conditions. non-infective endocarditis From the analogous model, a criterion ensuring correct output is established. Using the sufficient condition, we devise an efficient estimation process for the probability of the model producing the correct output. In addition, regarding the uniformly distributed inputs, a closed-form expression for the probability is calculated. Our analysis is ultimately extended to address the issue of non-Gaussian input noise. The simulation results are instrumental in verifying the accuracy of our theoretical findings.
Lightweight model design benefits significantly from the application of deep learning technology, with pruning as a key technique for reducing both model parameters and floating-point operations (FLOPs). The existing approaches to neural network pruning generally start by determining the importance of model parameters and using iterative evaluation metrics to eliminate parameters. Without examining the network model topology, the efficacy of these methods remains uncertain, potentially sacrificing efficiency while necessitating different pruning strategies for each dataset. Within this article, we analyze the graph configuration of neural networks and formulate a one-step neural network pruning procedure, regular graph pruning (RGP). We generate a regular graph as a preliminary step, and then adjust node degrees to conform with the pre-set pruning rate. Following this, we adjust the graph's edge connections to reduce the average shortest path length (ASPL) and attain the most optimal edge distribution. Lastly, the resultant graph is mapped to a neural network configuration to achieve pruning. The graph's ASPL has a negative impact on the accuracy of neural network classifications, as our tests reveal. RGP, however, retains a high level of precision while simultaneously reducing parameters by more than 90% and FLOPs by more than 90%. The necessary code is available for your convenience at https://github.com/Holidays1999/Neural-Network-Pruning-through-its-RegularGraph-Structure.
Privacy-preserving collaborative learning is facilitated by the burgeoning multiparty learning (MPL) methodology. Devices can collaboratively build a knowledge model, with local storage ensuring sensitive data privacy. However, the constant growth in the number of users creates a wider disparity in the characteristics of data and equipment, thereby exacerbating the challenge of model heterogeneity. Concerning practical application, this article examines two issues: data heterogeneity and model heterogeneity. A novel personal MPL method, dubbed device-performance-driven heterogeneous MPL (HMPL), is presented. Due to the inconsistency in the data formats from different devices, our primary concern is the variability in data sizes held by these devices. A novel approach to the adaptive unification of diverse feature maps is presented, using a heterogeneous feature-map integration method. Given the need for adaptable models across varying computing performances, a layer-wise strategy for generating and aggregating models is presented to tackle the heterogeneous model problem. The method's capacity to generate customized models is dependent on the device's performance. During aggregation, the common model parameters are adjusted using the principle that network layers with identical semantic values are united. Four prominent datasets were rigorously tested, and the outcomes showcase that our proposed framework's efficacy exceeds that of the leading contemporary methods.
Independent analyses of linguistic evidence from claim-table subgraphs and logical evidence from program-table subgraphs are common in existing table-based fact verification studies. Despite this, there is a paucity of interaction between the two kinds of evidence, which impedes the extraction of valuable consistent characteristics. We propose H2GRN, heuristic heterogeneous graph reasoning networks, in this work to capture consistent evidence shared between linguistic and logical data, employing innovative strategies in both graph construction and reasoning procedures. Rather than merely linking subgraphs by nodes with similar content, which leads to a sparse graph, we create a heuristically-guided heterogeneous graph to improve the close connectivity between the two subgraphs. This graph uses claim semantics to guide connections in the program-table subgraph, and in turn expands the connectivity of the claim-table subgraph through the logical relationships within programs as heuristic knowledge. Additionally, we devise multiview reasoning networks to create the appropriate association between linguistic and logical evidence. To enhance contextual understanding, we propose local-view multi-hop knowledge reasoning (MKR) networks, enabling current nodes to associate not only with immediate neighbors but also with those across multiple hops, thereby gleaning richer evidence. MKR leverages heuristic claim-table and program-table subgraphs to acquire more contextually rich linguistic and logical evidence, respectively. Simultaneously, we craft global-view graph dual-attention networks (DAN) to operate across the complete heuristic heterogeneous graph, strengthening the consistency of significant global-level evidence. For the purpose of claim verification, a consistency fusion layer is developed to alleviate inconsistencies between the three evidentiary types, thereby facilitating the identification of compatible shared evidence. The efficacy of H2GRN is shown by experiments conducted on TABFACT and FEVEROUS.
Recently, the significance of image segmentation for human-robot interaction has garnered substantial attention due to its vast potential. Networks that accurately determine the referenced location require a deep understanding of the interplay between image and language semantics. A range of mechanisms, including tiling, concatenation, and fundamental non-local transformations, are frequently utilized by existing works to accomplish cross-modality fusion. Nevertheless, the straightforward fusion process frequently exhibits either a lack of precision or is hampered by the excessive computational burden, ultimately leading to an insufficient grasp of the referent. Employing a fine-grained semantic funneling infusion (FSFI) method, we aim to solve the presented problem in this work. The FSFI's persistent spatial confinement of querying entities from varied encoding stages dynamically injects the gleaned language semantics into the vision branch. Moreover, the system decomposes features obtained from diverse data types into intricate components, enabling fusion in multiple, lower-dimensional spaces. The fusion's advantage lies in its potential to efficiently incorporate a higher quantity of representative information along the channel dimension, giving it a marked superiority over single-dimensional high-space fusion. A further obstacle in completing this task is the imposition of abstract semantic frameworks, which tend to diminish the precision of the referent's characteristics. With a focus on resolution, we present a multiscale attention-enhanced decoder (MAED) to resolve this problem. We implement a detail enhancement operator (DeEh), utilizing a multiscale and progressive approach. multi-media environment Higher-level features inform attention mechanisms, guiding lower-level features to prioritize detailed regions. Extensive evaluation on the demanding benchmarks reveals our network's performance is competitive with the current state-of-the-art systems.
Policy transfer via Bayesian policy reuse (BPR) leverages an offline policy library, selecting the most suitable source policy by inferring task-specific beliefs from observations, using a pre-trained observation model. For more effective policy transfer within deep reinforcement learning (DRL), we suggest a refined BPR methodology in this article. Typically, many BPR algorithms leverage the episodic return as the observation signal, a signal inherently limited in information and only accessible at the conclusion of each episode.