The accurate extraction of interconnect parasitic capacitance is a critical issue for designing VLSI circuits. To improve the efficiency of parasitic capacitance extraction, we present in this paper an adaptive incremental learning (AIL) strategy to build the parasitic capacitance extraction pattern model. The proposed model is a deep neural network (DNN) trained using adaptive incremental learning, or AIL-DNN. The key ideas are as follows: Firstly, the parametric space is divided into several subspaces called regions. And then a small number of training samples and test data are collected by Latin hypercube sampling (LHS); Secondly, a DNN model is trained and tested. The regions with large prediction errors are determined based on the average relative errors of test, which are called the training ineffective regions; Then, according to the ineffective regions, the sampling density is adjusted, that is, a new training dataset is prepared by adaptive resampling; Finally, incremental learning (IL) is used to make the DNN train new samples and update the network. The procedure of adaptive resampling, training and testing iterates until the test error reaches the predefined prediction accuracy. The proposed AIL-DNN can improve the efficiency and accuracy of DNN training with a reduced number of samples, and the trained DNN model can be used for the rapid extraction of parasitic capacitance. In this work, the prediction results of AIL-DNN and of the traditional DNN for two given interconnect patterns are compared. The results show that the size of training dataset required by AIL-DNN is about 20% of that of the traditional DNN with the similar accuracy. This significantly reduces the computational cost and time of dataset preparation.

In video super-resolution, it is common to use a frame-wise alignment to support the propagation of information over time. The role of alignment is well-studied for low-level enhancement in video, but existing works overlook a critical step - resampling. We show through extensive experiments that for alignment to be effective, the resam-pIing should preserve the reference frequency spectrum while minimizing spatial distortions. However, most ex-isting works simply use a default choice of bilinear inter-polation for resampling even though bilinear interpolation has a smoothing effect and hinders super-resolution. From these observations, we propose an implicit resampling-based alignment. The sampling positions are encoded by a sinusoidal positional encoding, while the value is es-timated with a coordinate network and a window-based cross-attention. We show that bilinear interpolation inher-ently attenuates high-frequency information while an MLP-based coordinate network can approximate more frequen-cies. Experiments on synthetic and real-world datasets show that alignment with our proposed implicit resampling enhances the performance of state-of-the-art frameworks with minimal impact on both compute and parameters.

Direct mesh fitting for 3D hand shape reconstruction is highly accurate. However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes. Conversely, parametric models like MANO ensure plausible hand shapes but are not as accurate as the non-parametric methods. In this work, we introduce a novel weakly-supervised hand shape estimation framework that integrates non-parametric mesh fitting with MANO model in an end-to-end fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to yield well-aligned and high-quality 3D meshes, especially in challenging two-hand and hand-object interaction scenarios.

Wizard of Oz (WoZ) as a prototyping method has been used to simulate intelligent user interfaces, particularly for speech-based systems. However, as our societies' expectations on artificial intelligence (AI) grows, the question remains whether a single Wizard is sufficient for it to simulate smarter systems and more complex interactions. Optimistic visions of 'what artificial intelligence (AI) can do' places demands on WoZ platforms to simulate smarter systems and more complex interactions. This raises the question of whether the typical approach of employing a single Wizard is sufficient. Moreover, while existing work has employed multiple Wizards in WoZ studies, a multi-Wizard approach has not been systematically studied in terms of feasibility, effectiveness, and challenges. We offer Wizundry, a real-time, web-based WoZ platform that allows multiple Wizards to collaboratively operate a speech-to-text based system remotely. We outline the design and technical specifications of our open-source platform, which we iterated over two design phases. We report on two studies in which participant-Wizards were tasked with negotiating how to cooperatively simulate an interface that can handle natural speech for dictation and text editing as well as other intelligent text processing tasks. We offer qualitative findings on the Multi-Wizard experience for Dyads and Triads of Wizards. Our findings reveal the promises and challenges of the multi-Wizard approach and open up new research questions.

Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs, however, suffer from: 1) poor scalability for big data due to the full kernel matrix and 2) intractable inference due to the non-Gaussian likelihoods. Hence, various scalable GPCs have been proposed through: 1) the sparse approximation built upon a small inducing set to reduce the time complexity and 2) the approximate inference to derive analytical evidence lower bound (ELBO). However, these scalable GPCs equipped with analytical ELBO are limited to specific likelihoods or additional assumptions. In this work, we present a unifying framework that accommodates scalable GPCs using various likelihoods. Analogous to GP regression (GPR), we introduce additive noises to augment the probability space for: 1) the GPCs with step, (multinomial) probit, and logit likelihoods via the internal variables and 2) particularly, the GPC using softmax likelihood via the noise variables themselves. This leads to unified scalable GPCs with analytical ELBO by using variational inference. Empirically, our GPCs showcase superiority on extensive binary/multiclass classification tasks with up to two million data points.

For real-world applications, multilabel learning usually suffers from unsatisfactory training data. Typically, features may be corrupted or class labels may be noisy or both. Ignoring noise in the learning process tends to result in an unreasonable model and, thus, inaccurate prediction. Most existing methods only consider either feature noise or label noise in multilabel learning. In this paper, we propose a unified robust multilabel learning framework for data with hybrid noise, that is, both feature noise and label noise. The proposed method, hybrid noise-oriented multilabel learning (HNOML), is simple but rather robust for noisy data. HNOML simultaneously addresses feature and label noise by bi-sparsity regularization bridged with label enrichment. Specifically, the label enrichment matrix explores the underlying correlation among different classes which improves the noisy labeling. Bridged with the enriching label matrix, the structured sparsity is imposed to jointly handle the corrupted features and noisy labeling. We utilize the alternating direction method (ADM) to efficiently solve our problem. Experimental results on several benchmark datasets demonstrate the advantages of our method over the state-of-the-art ones.

For real-world applications, data are often associated with multiple labels and represented with multiple views. Most existing multi-label learning methods do not sufficiently consider the complementary information among multiple views, leading to unsatisfying performance. To address this issue, we propose a novel approach for multi-view multi-label learning based on matrix factorization to exploit complementarity among different views. Specifically, under the assumption that there exists a common representation across different views, the uncovered latent patterns are enforced to be aligned across different views in kernel spaces. In this way, the latent semantic patterns underlying in data could be well uncovered and this enhances the reasonability of the common representation of multiple views. As a result, the consensus multi-view representation is obtained which encodes the complementarity and consistence of different views in latent semantic space. We provide theoretical guarantee for the strict convexity for our method by properly setting parameters. Empirical evidence shows the clear advantages of our method over the state-of-the-art ones.

In this paper, we focus on multi-label classification which associates one instance with multiple labels. The approach Label-specIfic FeaTures (LIFT) achieves state-of-the-art performance due to the label-specific features. However, the main limitation of LIFT is the poor local optima in k-means used at training stage. For this issue, in this paper, we propose to mitigate the limitation for high classification accuracy with ensemble way and term our approach as Ensemble of Label specIfic FeaTures (ELIFT). Specifically, our approach firstly constructs multiple LIFT classifiers by using multiple training sets generated by bagging strategy. Furthermore, different classifiers are weighted automatically according to the loss of each classifier. Finally, for each new instance, the predicted label vector is obtained by the weighted ensemble classifiers learned. Experiments conducted on five benchmark datasets demonstrate the performance of the proposed method outperforms the state-of-the-art approaches.

In this paper, we focus on promoting multi-label learning task with ensemble learning. Compared to traditional single algorithm methods, it has been recognized that ensemble methods could achieve much better performance than each constituent learned model, especially under the conditional independence of different classifiers. Existing multi-label ensemble algorithms mainly focus on creating diverse component learners by employing different mechanisms, mostly using randomization strategies by smart heuristics. Different from most existing methods, in this paper, we propose an ensemble method to learn the basic classifiers which considers the general independence of the different classifiers. Therefore, each learned multi-label classifier is guaranteed to be diverse and complementary. Furthermore, considering the different qualities of these classifiers, a weight vector is learned to balance these classifiers. Experiments on several benchmark datasets well demonstrate that the proposed method outperforms the state-of-the-art methods.