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Found 15 papers, 1 papers with code
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model
Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models.
Statistical Mechanical Analysis of Catastrophic Forgetting in Continual Learning with Teacher and Student Networks
Teacher-student learning is a framework in which we introduce two neural networks: one neural network is a target function in supervised learning, and the other is a learning neural network.
Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis
The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers.
Role of two learning rates in convergence of model-agnostic meta-learning
Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.
Localized Generations with Deep Neural Networks for Multi-Scale Structured Datasets
The local learning approach extracts semantic representations for these datasets by training the embedding model from scratch for each local neighborhood, respectively.
Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)
Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation.
Statistical mechanical analysis of sparse linear regression as a variable selection problem
The analysis is conducted through evaluation of the entropy, an exponential rate of the number of combinations of variables giving a specific value of fit error to given data which is assumed to be generated from a linear process using the design matrix.
Concept Formation and Dynamics of Repeated Inference in Deep Generative Models
Our study demonstrated that transient dynamics of inference first approaches a concept, and then moves close to a memory.
Exhaustive search for sparse variable selection in linear regression
By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states.
Statistical Mechanics of Node-perturbation Learning with Noisy Baseline
The value of the objective function for an unperturbed output is called a baseline.
Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution
The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter.
The topographic unsupervised learning of natural sounds in the auditory cortex
The computational modelling of the primary auditory cortex (A1) has been less fruitful than that of the primary visual cortex (V1) due to the less organized properties of A1.
Switching state space model for simultaneously estimating state transitions and nonstationary firing rates
Synthetic data analysis reveals the high performance of our algorithm in estimating state transitions, the number of neural states, and nonstationary firing rates compared to previous methods.
Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks
Loewenstein & Seung (2006) demonstrated that matching behavior is a steady state of learning in neural networks if the synaptic weights change proportionally to the covariance between reward and neural activities.
A general framework for investigating how far the decoding process in the brain can be simplified
Then, we compute how much information is lost when information is decoded using the simplified models, i. e., ``mismatched decoders''.
