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Found 17 papers, 6 papers with code
Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review
Conclusions: The reporting quality of the PSM analysis is suboptimal in some COVID-19 epidemiological studies.
A Distributed Algorithm for Personal Sound Zones Systems
A Personal Sound Zones (PSZ) system aims to generate two or more independent listening zones that allow multiple users to listen to different music/audio content in a shared space without the need for wearing headphones.
Exact Diffusion Inversion via Bi-directional Integration Approximation
In our work, it is found that applying BDIA to the EDM sampling procedure produces consistently better performance over four pre-trained models.
Lookahead Diffusion Probabilistic Models for Refining Mean Estimation
We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process.
On Accelerating Diffusion-Based Sampling Process via Improved Integration Approximation
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE).
On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance
A number of recent adaptive optimizers improve the generalisation performance of Adam by essentially reducing the variance of adaptive stepsizes to get closer to SGD with momentum.
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range
Firstly, we show that the particular placement of the parameter epsilon within the update expressions of AdaBelief reduces the range of the adaptive stepsizes, making AdaBelief closer to SGD with momentum.
Extending AdamW by Leveraging Its Second Moment and Magnitude
Aida is designed to compute the qth power of the magnitude in the form of |m_{t+1}|^q/(r_{t+1}+epsilon)^(q/p) (or |m_{t+1}|^q/((r_{t+1})^(q/p)+epsilon)), which reduces to that of AdamW when (p, q)=(2, 1).
Coarsening Optimization for Differentiable Programming
This paper presents a novel optimization for differentiable programming named coarsening optimization.
A Stacking Ensemble Approach for Supervised Video Summarization
This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization.
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance.
Higher-order vector Peregrine solitons and asymptotic estimates for the multi-component nonlinear Schrödinger equations
We first report the first- and higher-order vector Peregrine solitons (alias rational rogue waves) for the any multi-component NLS equations based on the loop group theory, an explicit (n + 1)-multiple eigenvalue of a characteristic polynomial of degree (n + 1) related to the condition of Benjamin-Feir instability, and inverse functions.
Exactly Solvable and Integrable Systems Mathematical Physics Analysis of PDEs Mathematical Physics Pattern Formation and Solitons Computational Physics
Parity-time-symmetric vector rational rogue wave solutions in any n-component nonlinear Schrödinger models
The extreme events are investigated for an $n$-component nonlinear Schr\"odinger ($n$-NLS) system in the focusing Kerr-like nonlinear media, which appears in many physical fields.
Exactly Solvable and Integrable Systems Mathematical Physics Analysis of PDEs Mathematical Physics Pattern Formation and Solitons Optics
Intra-Correlation Encoding for Chinese Sentence Intention Matching
Sentence intention matching is vital for natural language understanding.
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
GENERALIZED ADAPTIVE MOMENT ESTIMATION
Empirical studies for training four convolutional neural networks over MNIST and CIFAR10 show that under proper parameter selection, Game produces promising validation performance as compared to AMSGrad and PAdam.
