Forthcoming in Erkenntnis LEARNING AND POOLING, POOLING AND LEARNING
نویسنده
چکیده
We explore which types of probabilistic updating commute with convex IP pooling (Stewart and Ojea Quintana, 2017). Positive results are stated for Bayesian conditionalization (and a mild generalization of it), imaging, and a certain parameterization of Jeffrey conditioning. This last observation is obtained with the help of a slight generalization of a characterization of (precise) externally Bayesian pooling operators due to Wagner (2009). These results strengthen the case that pooling should go by imprecise probabilities since no precise pooling method is as versatile.
منابع مشابه
Early detection of MS in fMRI images using deep learning techniques
Introduction & Objective:MS is a disease of the central nervous system in which the body makes a defensive attack on its tissues. The disease can affect the brain and spinal cord, causing a wide range of potential symptoms, including balance, movement and vision problems. MRI and fMRI images are a very important tool in the diagnosis and treatment of MS. The aim of this study was to provide...
متن کاملA Convolutional Neural Network based on Adaptive Pooling for Classification of Noisy Images
Convolutional neural network is one of the effective methods for classifying images that performs learning using convolutional, pooling and fully-connected layers. All kinds of noise disrupt the operation of this network. Noise images reduce classification accuracy and increase convolutional neural network training time. Noise is an unwanted signal that destroys the original signal. Noise chang...
متن کاملA Learning-based Frame Pooling Model For Event Detection
Detecting complex events in a large video collection crawled from video websites is a challenging task. When applying directly good image-based feature representation, e.g., HOG, SIFT, to videos, we have to face the problem of how to pool multiple frame feature representations into one feature representation. In this paper, we propose a novel learning-based frame pooling method. We formulate th...
متن کاملGeneralizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a p...
متن کاملComparing the Max and Noisy-Or Pooling Functions in Multiple Instance Learning for Weakly Supervised Sequence Learning Tasks
Many sequence learning tasks require the localization of certain events in sequences. Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information. Multiple instance learning (MIL) is a popular framework for learning from weak labeling. In a common scenari...
متن کامل