Neural architecture search (NAS) is a promising method for automatically design neural architectures. NAS adopts a search strategy to explore the predefined search space to find outstanding performance architecture with the minimum searching costs. Bayesian optimization and evolutionary algorithms are two commonly used search strategies, but they suffer from computationally expensive, challenge to implement or inefficient exploration ability. In this paper, we propose a neural predictor guided evolutionary algorithm to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is defined from Bayesian optimization and we propose a graph-based uncertainty estimation network as a surrogate model that is easy to implement and computationally efficient. The second predictor is a graph-based neural network that directly outputs the performance prediction of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Extensive experiments demonstrate the superiority of NPENAS. Quantitative results on three NAS search spaces indicate that both NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-BO achieving state-of-the-art performance on NASBench-201 and NPENAS-NP on NASBench-101 and DARTS, respectively.
Texas A&M University（德州农工大学）；Microsoft Corporation（微软公司）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Neural Architecture Search (NAS) often trains and evaluates a large number of architectures. Recent predictor-based NAS approaches attempt to alleviate such heavy computation costs with two key steps: sampling some architecture-performance pairs and fitting a proxy accuracy predictor. Given limited samples, these predictors, however, are far from accurate to locate top architectures due to the difficulty of fitting the huge search space. This paper reflects on a simple yet crucial question: if our final goal is to find the best architecture, do we really need to model the whole space well?. We…
University Parthenope（帕特诺佩大学）；University Federico II（费德里科二世大学）
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the pro…
We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables. Our model makes use of a factorization mechanism for representing the regression coefficients of interactions among the predictors, while the interaction selection is guided by a prior distribution on random hypergraphs, a construction which generalizes the Finite Feature Model. We present a posterior inference algorithm based on Gibbs sampling, and establish posterior consistency of our regression model. Our method is evaluated with extensive experiments on simul…
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
Kyushu University（九州大学）；German Research Center for Artificial Intelligence (DFKI GmbH)（德国人工智能研究中心）；RPTU University Kaiserslautern-Landau（科布伦茨-劳恩堡大学）；The University of Osaka（大阪大学）；IntelligentX GmbH；Osaka Metropolitan University（大阪 Metropolitan 大学）
SpO$_2$ Predictor-Guided Stage-Wise Time-Frequency Reconstruction of Low-Quality Dual-Wavelength PPG for Oxygen Saturation Estimation
Department of Computer Science, University of California, Davis, Davis, CA, U.S.A.（加州大学戴维斯分校计算机科学系）；Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, U.S.A.（加州大学戴维斯分校电气与计算机工程系）
Johns Hopkins University（约翰霍普金斯大学）；University of Nebraska（内布拉斯加大学）；U.S. Army DEVCOM Army Research Laboratory (ARL)（美国陆军DEVCOM陆军研究实验室）；Booz Allen Hamilton（博思艾伦汉密尔顿公司）
Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or learn domain-invariant robust features from these modalities for cross-modal matching. In this paper, we use attributes extracted from visible images to synthesize attribute-preserved visible images from thermal imagery for cross-modal matching. A pre-trained attribute predictor network is used to extract the attributes from the visible image. Then, a novel multi-scale generator is proposed…
Clova AI Research, NAVER Corp.（NAVER公司Clova AI研究中心）
We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the priors of future samples can be generated in an independently and identically distributed (i.i.d.) manner using an efficient predictor. Given the past samples and future priors, the mother AR model can post-process the priors while the accompanied confidence predictor decides whether the current sample needs a resampling or not. Thanks to the i.i.d. assumption, the post-processing can update each sample in a parallel …
HeatACO: A Heatmap-Guided Max--Min Ant System for Large-Scale Travelling Salesman Problems
Centre for Data Science；Artificial Intelligence \& the School of Engineering；Computer Science, Victoria University of Wellington, Wellington, 6140, New Zealand
Transferable Mass Spectrum Prediction via Reference-Guided Test-time Specialization
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Hong Kong（香港大学）；South China University of Technology（华南理工大学）；Hong Kong Polytechnic University（香港理工大学）；Jinan University（暨南大学）；The Hong Kong University of Science and Technology（香港科技大学）
PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations
University of Chinese Academy of Sciences（中国科学院大学）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；National University of Singapore（新加坡国立大学）；Zhongyuan University of Technology（中原工学院）
Carl von Ossietzky University Oldenburg（卡尔·冯·奥西茨基大学奥尔登堡分校）；Institute for Mathematics（数学研究所）
In modern data analysis, sparse model selection becomes inevitable once the number of predictors variables is very high. It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. The celebrated Stability Selection overcomes these weaknesses by aggregating models, based on subsamples of the training data, followed by choosing a stable predictor set which is usually much sparser than the predictor sets from the raw models. The standard Stability Selection is based on a global criterion, namely the per-family error rate, while additionally requiring…
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event Data
Harvard Pilgrim Health Care Institute（哈佛朝圣者医疗保健研究所）；Harvard Medical School（哈佛医学院）；Harvard School of Public Health（哈佛公共卫生学院）；North Carolina State University（北卡罗来纳州立大学）
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
Lero Research Ireland Centre for Software Research, University of Limerick, Castletroy, Limerick, Ireland（勒罗爱尔兰软件研究中心，利默里克大学，卡斯莱特里，利默里克，爱尔兰）；University of Ottawa, Canada（渥太华大学，加拿大）；Lero Research Ireland Centre for Software Research, University of Limerick, Ireland（勒罗爱尔兰软件研究中心，利默里克大学，爱尔兰）
Unveiling Population Heterogeneity in Health Risks Posed by Environmental Hazards Using Regression-Guided Neural Network
University of Southern California（南加州大学）；Leonard Davis School of Gerontology, University of Southern California（南加州大学伦纳德·戴维斯老年医学院）；University of Minnesota（明尼苏达大学）
Georgia Institute of Technology（佐治亚理工学院）；Adobe Research（Adobe研究院）
How can we predict missing values in multi-dimensional data (or tensors) more accurately? The task of tensor completion is crucial in many applications such as personalized recommendation, image and video restoration, and link prediction in social networks. Many tensor factorization and neural network-based tensor completion algorithms have been developed to predict missing entries in partially observed tensors. However, they can produce inaccurate estimations as real-world tensors are very sparse, and these methods tend to overfit on the small amount of data. Here, we overcome these shortcomi…
