Related papers: Bayesian Reasoning Enabled by Spin-Orbit Torque Ma…

Bayesian learning is a powerful learning framework which combines the external information of the data (background information) with the internal information (training data) in a logically consistent way in inference and prediction. By…

Spin-transfer torques (STTs) can be exploited in order to manipulate the magnetic moments of nanomagnets, thus allowing for new consumer-oriented devices to be designed. Of particular interest here are tuneable radio-frequency (RF)…

Artificial intelligence based on artificial neural networks, which are originally inspired by the biological architectures of human brain, has mostly been realized using software but executed on conventional von Neumann computers, where the…

Current-induced magnetization switching by spin-orbit torque (SOT) holds considerable promise for next generation ultralow-power memory and logic applications. In most cases, generation of spin-orbit torques has relied on an external…

Artificial intelligence applications in autonomous driving, medical diagnostics, and financial systems increasingly demand machine learning models that can provide robust uncertainty quantification, interpretability, and noise resilience.…

Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not…

Rapid random telegraph noise (RTN) in magnetic tunnel junctions (MTJs) is an important figure of merit for probabilistic computing applications. However, the interactions between the macrospin and spin waves with finite wave numbers reduce…

Three-terminal magnetic tunnel junction (MTJ), where non-volatile magnetization state can be switched via spin orbit torque (SOT), is attracting massive research interests since it is featured by high speed, low power, nearly unlimited…

Structure learning of Bayesian networks is an important problem that arises in numerous machine learning applications. In this work, we present a novel approach for learning the structure of Bayesian networks using the solution of an…

Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning applications, and its multi-marginal extension opens up for…

To learn (statistical) dependencies among random variables requires exponentially large sample size in the number of observed random variables if any arbitrary joint probability distribution can occur. We consider the case that sparse data…

Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become…

Quantum Bayesian networks provide a mathematical formalism to describe causal relations, to analyse correlations, and to predict the probabilities of measurement outcomes, in systems involving both classical and quantum data. They…

Spin-orbit torque (SOT) represents an energy efficient method to control magnetization in magnetic memory devices. However, deterministically switching perpendicular memory bits usually requires the application of an additional bias field…

Bifurcation theory is a powerful tool for studying how the dynamics of a neural network model depends on its underlying neurophysiological parameters. However, bifurcation theory has been developed mostly for smooth dynamical systems and…

Recently there has been increasing activity to build dedicated Ising Machines to accelerate the solution of combinatorial optimization problems by expressing these problems as a ground-state search of the Ising model. A common theme of such…

Using type-x spin-orbit torque (SOT) switching scheme, in which the easy axis (EA) of the ferromagnetic (FM) layer and the charge current flow direction are collinear, is possible to realize a lower-power-consumption, higher-density, and…

Superparamagnetic magnetic tunnel junctions (sMTJs) are promising components for true random number generation and probabilistic computing. Achieving high-frequency fluctuation while maintaining reliable control over output level is…

The increasing scale of neural networks and their growing application space have produced demand for more energy- and memory-efficient artificial-intelligence-specific hardware. Avenues to mitigate the main issue, the von Neumann…

interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the spatial heterogeneity in the underlying process of interest.…