2,133 research outputs found
Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network
The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively
Cooperative Retriever and Ranker in Deep Recommenders
Deep recommender systems (DRS) are intensively applied in modern web
services. To deal with the massive web contents, DRS employs a two-stage
workflow: retrieval and ranking, to generate its recommendation results. The
retriever aims to select a small set of relevant candidates from the entire
items with high efficiency; while the ranker, usually more precise but
time-consuming, is supposed to further refine the best items from the retrieved
candidates. Traditionally, the two components are trained either independently
or within a simple cascading pipeline, which is prone to poor collaboration
effect. Though some latest works suggested to train retriever and ranker
jointly, there still exist many severe limitations: item distribution shift
between training and inference, false negative, and misalignment of ranking
order. As such, it remains to explore effective collaborations between
retriever and ranker.Comment: 12pages, 4 figures, WWW'2
Interactive Graph Convolutional Filtering
Interactive Recommender Systems (IRS) have been increasingly used in various
domains, including personalized article recommendation, social media, and
online advertising. However, IRS faces significant challenges in providing
accurate recommendations under limited observations, especially in the context
of interactive collaborative filtering. These problems are exacerbated by the
cold start problem and data sparsity problem. Existing Multi-Armed Bandit
methods, despite their carefully designed exploration strategies, often
struggle to provide satisfactory results in the early stages due to the lack of
interaction data. Furthermore, these methods are computationally intractable
when applied to non-linear models, limiting their applicability. To address
these challenges, we propose a novel method, the Interactive Graph
Convolutional Filtering model. Our proposed method extends interactive
collaborative filtering into the graph model to enhance the performance of
collaborative filtering between users and items. We incorporate variational
inference techniques to overcome the computational hurdles posed by non-linear
models. Furthermore, we employ Bayesian meta-learning methods to effectively
address the cold-start problem and derive theoretical regret bounds for our
proposed method, ensuring a robust performance guarantee. Extensive
experimental results on three real-world datasets validate our method and
demonstrate its superiority over existing baselines
Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure
Ad creatives are one of the prominent mediums for online e-commerce
advertisements. Ad creatives with enjoyable visual appearance may increase the
click-through rate (CTR) of products. Ad creatives are typically handcrafted by
advertisers and then delivered to the advertising platforms for advertisement.
In recent years, advertising platforms are capable of instantly compositing ad
creatives with arbitrarily designated elements of each ingredient, so
advertisers are only required to provide basic materials. While facilitating
the advertisers, a great number of potential ad creatives can be composited,
making it difficult to accurately estimate CTR for them given limited real-time
feedback. To this end, we propose an Adaptive and Efficient ad creative
Selection (AES) framework based on a tree structure. The tree structure on
compositing ingredients enables dynamic programming for efficient ad creative
selection on the basis of CTR. Due to limited feedback, the CTR estimator is
usually of high variance. Exploration techniques based on Thompson sampling are
widely used for reducing variances of the CTR estimator, alleviating feedback
sparsity. Based on the tree structure, Thompson sampling is adapted with
dynamic programming, leading to efficient exploration for potential ad
creatives with the largest CTR. We finally evaluate the proposed algorithm on
the synthetic dataset and the real-world dataset. The results show that our
approach can outperform competing baselines in terms of convergence rate and
overall CTR
Dynamic property investigation of optical burst injection locking lasers
The dynamic properties of DFB- and DML-based injection lock lasers are investigated by analyzing the instantaneous frequency of the master and slave lasers' beat signal. Around 10~20 ns locking time is measured by burst signal injection and a large injection ratio leads to longer unlocking time
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