In variational continual learning, the posterior distribution of interest is frequently intractable and approximation is required. We summarize the meta-training process of our VC-BML in algorithm 1. Moreover,we evaluate FTML onthe unseen tasks (i.e., tasks sampled from meta-test set) instead ofthe training tasksthattheoriginalFTMLused. It would be unfair to adopt the original initialization procedure in OSML. BOMVI [10]: In our experiments, we use variational inference to approximate the posterior of meta-parameters. E.3.2 Settings As the latent variables in this paper are meta-parameters and task-specific parameters, the dimensionality ofthelatent space isactually determined bythenumber ofparameters inthedeep neural network. In particular, we define a CNN architecture and present its details in Table 1.

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The current version of Predify assumes that there is no gap between the encoders. One can easily override the default setting by providing all the details for a PCoder. A.3 ExecutionTime Since we used a variable number of GPUs for the different experiments, an exact execution time is hard to pinpoint. We expect that this could be further improved with a more extensive and systematic hyperparameter search. In other words, their training hyperparameters appeared to have been optimised for their predictive coding network, but not - or not as much - for their feedforward baseline.

This paper presents a systematic study of the application of convolutional neural networks (CNNs) as an efficient and versatile tool for the analysis of critical and low-temperature phase states in spin system models. The problem of calculating the dependence of the average energy on the spatial distribution of exchange integrals for the Edwards-Anderson model on a square lattice with frustrated interactions is considered. We further construct a single convolutional classifier of phase states of the ferromagnetic Ising model on square, triangular, honeycomb, and kagome lattices, trained on configurations generated by the Swendsen-Wang cluster algorithm. Computed temperature profiles of the averaged posterior probability of the high-temperature phase form clear S-shaped curves that intersect in the vicinity of the theoretical critical temperatures and allow one to determine the critical temperature for the kagome lattice without additional retraining. It is shown that convolutional models substantially reduce the root-mean-square error (RMSE) compared with fully connected architectures and efficiently capture complex correlations between thermodynamic characteristics and the structure of magnetic correlated systems.

Neural architecture search (NAS) automates the design process of high-performing architectures, but remains bottlenecked by expensive performance evaluation. Most existing studies that achieve faster evaluation are mostly tied to cell-based search spaces and graph encodings tailored to those individual search spaces, limiting their flexibility and scalability when applied to more expressive search spaces. In this work, we aim to close the gap of individual search space restrictions and search space dependent network representations. We present ONNX-Bench, a benchmark consisting of a collection of neural networks in a unified format based on ONNX files. ONNX-Bench includes all open-source NAS-bench-based neural networks, resulting in a total size of more than 600k {architecture, accuracy} pairs. This benchmark allows creating a shared neural network representation, ONNX-Net, able to represent any neural architecture using natural language descriptions acting as an input to a performance predictor. This text-based encoding can accommodate arbitrary layer types, operation parameters, and heterogeneous topologies, enabling a single surrogate to generalise across all neural architectures rather than being confined to cell-based search spaces. Experiments show strong zero-shot performance across disparate search spaces using only a small amount of pretraining samples, enabling the unprecedented ability to evaluate any neural network architecture instantly.

We first derive the (6). In this section, we provide implementation details regarding the proposed method. Every experiment we report can be trained and tested on a single card.