讲者: Yingbin Liang Professor at the Department of Electrical and Computer Engineering at the Ohio State University (OSU) 讲座题目:Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-...
However, as regression methods failed to give adequate predictions, for NFIQ 1, the machine-learning problem was restated in terms of classification into five levels of utility: excellent, very good, good, fair and poor [3]. The boundaries between the levels of utility were defined based on ...
Those conditions can be used as easy-to-check criteria when convergence (or not) of long-range predictions is desirable.LeoniUniversity of Southern DenmarkPatrickUniversity of Southern DenmarkNeural ComputationLong-Range Out-of-Sample Properties of Autoregressive Neural Networks.” Neural Computation - ...
The advantage of this method is that it can make predictions in real time, without having to wait for the completion of the entire stream. Therefore, we regard bandwidth and duration as distinct forecasting tasks rather than using them as input features as traditional traffic classification methods...
Despite the advent of machine learning frameworks such as the seminal SHAP (Shapley Additive exPlanations) [23], which allow for interpretation of the magnitude of a variable’s impact on model predictions, revealing the structure of relations is a formidable challenge. Network analysis is one metho...
MegaD: A package for metagenomic analysis to identify and predict disease sample accurately using deep neural networks.Machine learning has been utilized in many applications from biomedical imaging to business analytics. Machine learning is stipulated to be a strong method for diagnostics and even for...
Specifically, we first use two networks to make predictions on the same mini-batch data and calculate a joint loss with Co-Regularization for each training example. Then we select small-loss examples to update the parameters of both two networks simultaneously. Trained by the joint loss, these ...
Sample test images from both datasets that were misclassified by mixup-augmented models (a), when embedded in a 2D space for t-SNE visualization, show that they lie in the vicinity of training samples from classes different from the test images’ labels, leading to wrong predictions (b, d)...
We integrate the confidence given by multiple data enhancement models, and try to select samples with high confidence but not easy to learn (for example, the predictions of multiple models are not all consistent). Then, the multi-template Prompt Learning is reconstructed with the set of labeled...
From the results presented above, it is noticeable that annotated images are fundamental for obtaining sufficiently performant segmentation models, in the metallography context. Unsupervised methods. Unsupervised methods can only achieve around 20%–22% mean IoU, which may be useful if predictions are ...