Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
Here, MX knockoff was used as it preserves the correlation structure of the original dataset (Extended Data Fig. 1)25. For low or intermediate correlation structures, StablEN and StablSGL selected fewer features with improved JI and FDR and similar predictivity compared to EN or SGL (Fig. 3c...
Sparsity (d), reliability (FDR,e; JI,f) and predictivity (RMSE,g) performances of StablL(red box plots) and Lasso (gray box plots) with increasing number of samples (n,xaxis) for 10 (left panels), 25 (middle panels) or 50 (right panels) informative features.h–k, Sparsity (h), ...
Note that current MOEAs designed for LSMOPs usually undergo a perfor- mance decrease on sparse LSMOPs [17, 18]. The primary reason is their neglect of the sparse structure of solutions. When optimizing sparse LSMOPs, they often start with a uni- formly sampled population and have to evolve...
Notably, ABN axons form a bundle-like structure (i.e., mossy fiber pathway) that takes an orthogonal path toward the CA3, passing through the illuminated region. Statistically speaking, the Ca2+ transients of ABNs during REM sleep are not randomly distributed, and the total REM duration ...
the strong hierarchical part-whole structure of natural entities and natural dynamics, e.g., operation of natural physical law, will, with probability close to 1, never bring the system into such states. These states likely do not need to be explicitly represented in order for the model to ...
In S2TLS, model structure determination is formulated as an optimization problem considering both the estimation/fitting error, and the complexity arising from models and regressors. The key aspects that distinguish the S2TLS from the native STLS algorithm include: first, in addition to the ...
We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. S
where,\(B(x,\delta )\)is the open ball of radius\(\delta \)centered asxand\(\sigma (\{x_1,...,x_{n-1}\})\)is the\(\varSigma -\)field generated by the random vector\(\{x_1,...,x_{n-1}\}\). The\(x_n^*\)series below is the best value of the decision vector...
The Lasso model, for instance, has a linear form but uses far more features. Similarly, the C5.0 models allow users to make predictions by hand, but use a hierarchical structure that makes it difficult to assess the influence of each input variable with respect to the others (see Freitas ...