To evaluate the model, we built a large-scale road-vehicle dataset containing over 40 000 labeled road images captured by three cameras placed on our self-driving car. Moreover, human driving activities and vehicle states were recorded at the same time. 展开 ...
Each dataset comprises two sets of labels obtained from two different observers, with the first observer considered as the ground truth (GT). The DRIVE dataset is a commonly used dataset for retinal vessel segmentation, consisting of a total of 40 labeled retinal vessel images, each with a ...
Human cerebral organoids undergo vascularization and maturation in the mouse brain. Differentiation of human pluripotent stem cells to small brain-like structures known as brain organoids offers an unprecedented opportunity to model human brain developme
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus....
In an uninjured brain, MANF protein is expressed mainly in neurons [14, 40] and its expression levels are increased upon acute ischemia [7, 48, 80]. Interestingly, at 24 h after ischemia, MANF protein expression has been reported also in microglia/macrophages of the ischemic region [60, ...
Blood–brain barrier damage is a critical pathological feature of ischemic stroke. Oligodendrocyte precursor cells are involved in maintaining blood–brain barrier integrity during the development. However, whether oligodendrocyte precursor cell could sustain blood–brain barrier permeability during ischemic brai...
labeled populations from both datasets (Fig.3b). MultiVI achieves this while also correcting batch effects within the Satpathy data and technology-specific effects within the Ding data (Fig.3c,dand Supplementary Fig.5a–c). To study the correctness of the integration, we examined the set of ...
In supervised and semi-supervised learning, this training data must be thoughtfully labeled by data scientists to optimize results. Given proper feature extraction, supervised learning requires a lower quantity of training data overall than unsupervised learning. ...
The solution is to train the smaller model on a large amount of generated data that is labeled by the larger model. The smaller model learns the soft output of the larger model, instead of actual labels on real data. This is a simpler problem that can be learned by the smaller model. ...
In practice, many LLMs use a combination of both unsupervised and supervised learning. The model might first undergo unsupervised pre-training on large text datasets to learn general language patterns, followed by supervised fine-tuning on task-specific labeled data. ...