In this study, we present a novel method for modeling the canopy surface of an umbrella. Our approach involves representing the area between the ribs on the canopy as a trimmed bilinear patch. ...Furthermore, we conduct an in-depth exploration of various differential geometric properties of the umbrella surface. We introduce a method for unfolding the canopy surface onto a plane, which serves as a valuable technique for fabricating a cardboard template to accurately cut canopy fabrics. To validate the effectiveness of our geometric modeling method, we apply it to several umbrella models, showcasing its practical application and benefits.
•A canopy surface between ribs is represented as a trimmed bilinear patch.•A method to unfold the canopy surface onto a plane is proposed.•Parameters that determine the shape of the canopy surface are defined.•An umbrella was fabricated based on our methods.
Advances in Variational Inference Zhang, Cheng; Butepage, Judith; Kjellstrom, Hedvig ...
IEEE transactions on pattern analysis and machine intelligence,
08/2019, Letnik:
41, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational ...inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully applied to various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.
The most puzzling aspect of the ‘strange metal’ behavior of correlated electron compounds is that the linear in temperature resistivity often extends down to low temperatures, lower than natural ...microscopic energy scales. We consider recently proposed deconfined critical points (or phases) in models of electrons in large dimension lattices with random nearest-neighbor exchange interactions. The criticality is in the class of Sachdev–Ye–Kitaev models, and exhibits a time reparameterization soft mode representing gravity in dual holographic theories. We compute the low temperature resistivity in a large M limit of models with SU(M) spin symmetry, and find that the dominant temperature dependence arises from this soft mode. The resistivity is linear in temperature down to zero temperature at the critical point, with a co-efficient universally proportional to the product of the residual resistivity and the co-efficient of the linear in temperature specific heat. We argue that the time reparameterization soft mode offers a promising and generic mechanism for resolving the strange metal puzzle.
Recent research has successfully adapted vision-based convolutional neural network (CNN) architectures for audio recognition tasks using Mel-Spectrograms. However, these CNNs have high computational ...costs and memory requirements, limiting their deployment on low-end edge devices. Motivated by the success of efficient vision models like InceptionNeXt and ConvNeXt, we propose AudioRepInceptionNeXt, a single-stream architecture. Its basic building block breaks down the parallel multi-branch depth-wise convolutions with descending scales of k×k kernels into a cascade of two multi-branch depth-wise convolutions. The first multi-branch consists of parallel multi-scale 1×k depth-wise convolutional layers followed by a similar multi-branch employing parallel multi-scale k×1 depth-wise convolutional layers. This reduces computational and memory footprint while separating time and frequency processing of Mel-Spectrograms. The large kernels capture global frequencies and long activities, while small kernels get local frequencies and short activities. We also reparameterize the multi-branch design during inference to further boost speed without losing accuracy. Experiments show that AudioRepInceptionNeXt reduces parameters and computations by 50%+ and improves inference speed 1.28× over state-of-the-art CNNs like the Slow–Fast while maintaining comparable accuracy. It also learns robustly across a variety of audio recognition tasks.
•StocIP is converted into a pure network training paradigm for high-dimensional problems making full use of backpropagation and gradient-based optimization.•Affine transformation is embedded in the ...network to convert statistical parameters of physical random vector into learnable weights and biases.•Pre-embedded subnetwork implements the reparameterization for physical random vector by reformulating sampling as a differentiable transformation.•Maximum mean discrepancy is employed as a distribution-free loss to quantify distribution discrepancy.•StocIPNet has solid probabilistic interpretability due to theoretical equivalence to maximum likelihood estimation method.
The stochastic inverse problem (StocIP), which aims to align push-forward and observed output distributions by estimating probability distributions of unknown system inputs, often faces optimization challenges and the curse of dimensionality. A novel deep network called StocIPNet which comprises an affine-embedded reparameterization subnetwork (ReparNet) and a complex system metamodeling subnetwork (MetaNet) is proposed to alleviate these issues. The ReparNet subnetwork embeds the affine transformation to convert the statistical parameters of the physical random vector into learnable weights and biases, effectively implementing the reparameterization trick by separating random and deterministic elements in the stochastic sampling operation to preserve differentiability. In parallel, the MetaNet subnetwork offers a computationally efficient alternative to time-consuming forward solvers, facilitating the generation of push-forward distributions. The entire StocIPNet utilizes the kernel maximum mean discrepancy (MMD) as a distribution-free loss function, quantifying the discrepancy between push-forward and observed output distributions. By leveraging the ReparNet’s advantage of reformulating the sampling process as a differentiable transformation and combining two subnetworks seamlessly, the StocIP is reconfigured into a pure network training paradigm preserving differentiability perfectly, which allows for direct modeling and efficient inference of uncertainty within the network using automatic differentiation, backpropagation and gradient-based optimization methods, enabling ease of scaling to high-dimensional problems. The proposed framework has been theoretically demonstrated to be equivalent to the maximum likelihood method, ensuring its solid probabilistic interpretable foundation. The proposed framework is applied to perform stochastic model updating on a numerical and an experimental structure, which effectively demonstrates the framework’s remarkable effectiveness and high efficiency in treating high-dimensional StocIPs.
Generative Adversarial Network (GAN) is a thriving generative model and considerable efforts have been made to enhance the generation capabilities via designing a different adversarial framework of ...GAN (e.g., the discriminator and the generator) or redesigning the penalty function. Although existing models have been demonstrated to be very effective, their generation capabilities have limitations. Existing GAN variants either result in identical generated instances or generate simulation data with low quality when the training data are diverse and extremely limited (a dataset consists of a set of classes but each class holds several or even one single sample) or extremely imbalanced (a category holds a set of samples and other categories hold one single sample). In this paper, we present an innovative approach to tackle this issue, which jointly employs joint distribution and reparameterization method to reparameterize the randomized space as a mixture model and learn the parameters of this mixture model along with that of GAN. In this way, we term our approach Joint Distribution GAN (JDGAN). In our work, we show that the JDGAN can not only generate high quality simulation data with diversity, but also increase the overlapping area between the generating distribution and the raw data distribution. We proceed to conduct extensive experiments, utilizing MNIST, CIFAR10 and Mass Spectrometry datasets, all using extremely limited amounts of data, to demonstrate the significant performance of JDGAN in both achieving the smallest Fréchet Inception Distance (FID) score and producing diverse generated data.
In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous ...dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images.
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•Structural reparameterization dehazenet: outstanding performance, faster speed.•Replacing Tanh with ReLU leads to better results.•Transfer learning addresses the problem of insufficient samples.•Dual subnets method proves highly effective in datasets of different scales.
•Using a Bayesian classifier reparameterization for classifier uncertainty analysis.•A label augmentation strategy models the distribution for label uncertainty.•Our strategies significantly improve ...the backbones under different SGG tasks.
As a challenging task in computer vision, Scene graph generation (SGG) aims to model the underlying semantic relationships among objects in a given image for scene understanding. Due to the increasing scale and subjectivity, the annotations of existing SGG benchmarks inevitably suffer from some uncertainty issues, resulting in the models hardly learning the relationships comprehensively. In this work, we address the uncertainty from the perspectives of both classifier parameters and relationship labels. On one hand, we handle the classifier uncertainty via learning a Bayesian classifier reparameterization, of which the weights are sampled from a latent space spanned with a prior distribution. On the other hand, we assume that each relationship label is sampled from a latent label space and mitigate the label uncertainty via estimating the latent relationship distribution. As a result, the distribution of the classifier parameters are comprehensively learned under the supervision of the estimated relationship labels, thus improving the model’s generalization ability. Experimental results on the popular benchmark demonstrate that the proposed strategies significantly improve different baseline models on different SGG tasks.
The rapid development of deep learning has opened a new door to the exploration of topology optimization methods. The combination of deep learning and topology optimization has become one of the ...hottest research fields at the moment. Different from most existing work, this paper conducts an in-depth study on the method of directly using neural networks (NN) to carry out topology optimization. Inspired by the idea from the field of “Inverting Representation of Image” and “Physics-Informed Neural Network”, a topology optimization via neural reparameterization framework (TONR) that can solve various topology optimization problems is formed. The core idea of TONR is Reparameterization, which means the update of the design variables (pseudo-density) in the conventional topology optimization method is transformed into the update of the NN’s parameters. The sensitivity analysis in the conventional topology optimization method is realized by automatic differentiation technology. With the update of NN’s parameters, the density field is optimized. Some strategies for dealing with design constraints, determining NN’s initial parameters, and accelerating training are proposed in the paper. In addition, the solution of the multi-constrained topology optimization problem is also embedded in the TONR framework. Numerical examples show that TONR can stably obtain optimized structures for different optimization problems, including the stress-constrained problem, structural natural frequency optimization problems, compliant mechanism design problems, heat conduction system design problems, and the optimization problem of hyperelastic structures. Compared with the existing methods that combine deep learning with topology optimization, TONR does not need to construct a dataset in advance and does not suffer from structural disconnection. The structures obtained by TONR can be comparable to the conventional methods.
•This paper conducts an in-depth exploration of the method that directly executes TO using the NN itself.•In TONR, the update of the design variables in the conventional-TO is transformed into the update of the NN’s parameters.•TONR can solve various optimization problems.•The performance of the optimized structures obtained by TONR can be comparable to that of the conventional method.•TONR employs automatic differentiation to handle differential operators.
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