Self-Paced Learning: an Implicit Regularization Perspective

Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting strategy that is determined by minimizer function. Existing methods usually pursue this by artificially designing the explicit form of SPL regularizer. In this paper, we focus on the minimizer function, and study a group of new regularizer, named self-paced implicit regularizer that is deduced from robust loss function. Based on the convex conjugacy theory, the minimizer function for self-paced implicit regularizer can be directly learned from the latent loss function, while the analytic form of the regularizer can be even known. A general framework (named SPL-IR) for SPL is developed accordingly. We demonstrate that the learning procedure of SPL-IR is associated with latent robust loss functions, thus can provide some theoretical inspirations for its working mechanism. We further analyze the relation between SPL-IR and half-quadratic optimization. Finally, we implement SPL-IR to both supervised and unsupervised tasks, and experimental results corroborate our ideas and demonstrate the correctness and effectiveness of implicit regularizers.Comment: 12 pages, 3 figure

Band Gap Structure of Two Dimensional Acoustic Metamaterials with Coated Double Hybrid Lattice

Acoustic metamaterials (phononic crystals) have received much recent attention. Over time, several efforts were proposed to improve the structure in order to enlarge the band gap, lower the band gap frequency, and/or generate greater attenuation of vibration. In this document, a novel two dimensional acoustic metamaterial with Coated Double Hybrid Lattice (CDHL) is proposed. The structure makes use of both the Bragg Scattering Theorem and the Local Resonance Theorem. In the simulation, both a lower frequency band gap and a higher frequency band gap are obtained. According to the modal analysis and phase spectrum analysis, it is proved that the lower frequency band gap is due to local resonance between the double lead cores and the rubber coating. At the same time, the higher frequency band gap is generated as a result of the interaction of Bragg scattering and local resonance effect. The variation in maximum vibration attenuation with respect to the position of the double central lead core is also investigated. Excellent vibration attenuation is demonstrated using the proposed method. Later on, experiments are carried out both to validate the simulation and to prove the effectiveness of the proposed structure. The output signal is picked up approximately in the middle of the structure and the results have coincides with the simulation quite well. At the end of this document, several outlooks are stated

Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks

Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack success rates in white-box setting, more focus is shifted to black-box attacks, of which the transferability of adversarial examples has gained significant attention. In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success. But little work pays attention to its possible limitation. In this work, we observe that the deviation between the original gradient and the generated noise may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability. To this end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM). Specifically, we use data rescaling to substitute the sign function without extra computational cost. We further propose a Depth First Sampling method to eliminate the fluctuation of rescaling and stabilize the gradient update. Our method could be used in any gradient-based attacks and is extensible to be integrated with various input transformation or ensemble methods to further improve the adversarial transferability. Extensive experiments on the standard ImageNet dataset show that our method could significantly boost the transferability of gradient-based attacks and outperform the state-of-the-art baselines.Comment: 10 pages, 6 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2204.0288

Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58