The material in this supplement was not able to be included in the paper. It is meant for those who would like to know more details of the behavior of the two methods for fitting the diffusion model to data. It concerns some additional discussion of efficiency and accuracy of parameter estimates, a discussion of overfitting, presentation of parameter tradeoffs in the model (showing a very stron...

When do nonparametric Bayesian procedures “overfit?” To shed light on this question, we consider a binary regression problem in detail and establish frequentist consistency for a certain class of Bayes procedures based on hierarchical priors, called uniform mixture priors. These are defined as follows: let ν be any probability distribution on the nonnegative integers. To sample a function f fro...

This paper discusses the propagation of signals in generic densely connected multilayered feedforward neural networks. It is concluded that the dense connecting combined with the hyperbolic tangent activation functions of the neurons may cause a highly random, spurious generalization, that decreases the overall performance and reliability of a neural network and can be mistaken with overfitting...

AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because its objective is to minimize error on the training set. We show that with the introduction of a scoring function and the random selection of training data it is possible to create a smaller set of feature vectors. The selection of th...

We present a method for incrementally learning mixture models that avoids the necessity to keep all data points around. It contains a single user-settable parameter that controls via a novel statistical criterion the trade-off between the number of mixture components and the accuracy of representing the data. A key idea is that each component of the (non-overfitting) mixture is in turn represen...

The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often results in overfitting or singularity, which limits the generalizability of the results. In this paper, we propose a Doubly Regularized LOgistic Regression Algorithm (DR LORA) which penalizes the voxels of the brain t...

Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing prompts manually, Context Optimization (CoOp) has been recently proposed to learn continuous using task-specific training data. Despite the performance improvements on tasks, several studies reported that CoOp suffer...

This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of “overfitting” such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard decision procedure at every iterative step to overcome the ov...