A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
The study presents Vabs-Net, a new approach in protein modeling that considers both residue and atom levels. Traditional methods focus mainly on the residue level, ignoring the importance of atom…
Continue readingThe research delves into the challenge of sample-based inference (SBI) for Bayesian neural networks (BNNs) due to the size and structure of the network’s parameter space. The authors propose that…
Continue readingIn response to concerns over reliability and robustness in machine learning, the authors propose a multiverse analysis approach with their PRESTO framework. This framework maps the ‘multiverse’ of machine learning…
Continue readingThis article presents a unified framework to address the challenging convergence analysis under non-convex conditions in stochastic gradient descent (SGD). It interprets the updated direction as the sum of the…
Continue readingThe paper presents a new approach to predicting with missing data by viewing it as a two-stage adaptive optimization problem. Traditionally, missing data points are imputed before predictions are made….
Continue readingThe article discusses the development of a Secure-Aggregation (SecAgg) algorithm for residential short-term load forecasting, which maintains data privacy while also being robust against attacks. The algorithm minimizes communication complexity…
Continue readingThis paper offers a different understanding of the Adam optimizer, a popular choice in deep neural network training. Despite its practical success, the theoretical understanding of Adam’s algorithmic components has…
Continue readingThis academic article explores the potential of electric vehicles (EVs) in managing the primary frequency control of an industrial microgrid. The article emphasizes the advantages of EVs in the transport…
Continue readingThe paper proposes L2G2G, a Local2Global method to enhance the accuracy of Graph Autoencoders (GAEs) without compromising scalability. GAEs are tools that offer low-dimensional representations of networks, but they suffer…
Continue readingThis academic article discusses the development of a Stochastic Two-Point (S2P) approach for zeroth-order optimization in deep models. Zeroth-order methods are becoming increasingly popular due to their ability to optimize…
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