A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.

When multiple predictions agree, a higher level capsule becomes active.

A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. We use the length of the activity vector to represent the probability that the entity exists and its orientation to represent the instantiation parameters.

Deformable models are an attractive approach to recognizing non(cid:173) rigid objects which have considerable within class variability. How(cid:173) ever, there are severe search problems associated with fitting the models to data. We show that by using neural networks to provide better starting points, the search time can be significantly reduced. The method is demonstrated on a character recognition task. In previous work we have developed an approach to handwritten character recogni(cid:173) tion based on the use of deformable models (Hinton, Williams and Revow, 1992a; Revow, Williams and Hinton, 1993).

We first show how to represent sharp posterior probability distribu(cid:173) tions using real valued coefficients on broadly-tuned basis functions. Then we show how the precise times of spikes can be used to con(cid:173) vey the real-valued coefficients on the basis functions quickly and accurately. Finally we describe a simple simulation in which spik(cid:173) ing neurons learn to model an image sequence by fitting a dynamic generative model. A perceived object is represented in the brain by the activities of many neurons, but there is no general consensus on how the activities of individual neurons combine to represent the multiple properties of an object. We start by focussing on the case of a single object that has multiple instantiation parameters such as position, velocity, size and orientation. We assume that each neuron has an ideal stimulus in the space of instantiation parameters and that its activation rate or probability of activation falls off monotonically in all directions as the actual stimulus departs from this ideal.

We have put together a list of 10 most cited and discussed research papers in machine learning that published over the past 10 years, from AlexNet to GPT-3. These are great readings for researchers new to this field and freshers for experienced researchers. For each paper, we provide links to the short overview, author presentations and detailed paper walkthrough for readers with different levels of expertise. Abstract: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art.

I'm writing this to better understand an awesome idea I came across in the Computer Vision space. For my Bachelor's Dissertation I was fortunate enough to take a deep dive into the field's state of the art Object Recognition and Localisation. Papers like YOLO (and her latest v2 and v3 iterations -- the last of which is an entertaining read to say the least), as well as RCNN, Fast-RCN and Faster-RCNN were some of the most talked about at the time. Object Detection is an interesting, and difficult problem in Computer Vision. This is because it really combines two smaller underlying problems; that of object localisation and classification.

Electrocardiogram (ECG) signal analysis plays a vital role in medical diagnosis since ECG signal can provide vital information that can help to diagnose various health conditions. For example, ECG beat classification; e.g classifying ECG signal portions in to classes such as normal beats or different arrhythmia types such as atrial fibrillation, premature contraction, or ventricular fibrillation allows to identify different cardiovascular diseases. Similarly, ECG signal compression and reconstruction have a variety of applications such as remote cardiac monitoring in body sensor nodes (Mamaghanian et al., 2011) and achieving low power consumption when sending and processing data through IoT -gateways (Al Disi et al., 2018). ECG signal analysis and classification was predominantly done using signal processing methods such as wavelet transformation or independent component analysis or feature driven classical machine learning methods (Y u and Chou, 2008; Martis et al., 2013; Kim et al., 2009; Li and Zhou, 2016). However such methods have left room for further improvements in terms of accuracy and the manual feature curation is a daunting task. Recently 1D Convolutions have been tried on ECG classification producing some promising results (Li et al., 2017; Acharya et al., 2017), Nonetheless, these methods do not perform well for the classes with less volumes of training data.

This study develops an unsupervised learning algorithm for products of expert capsules with dynamic routing. Analogous to binary-valued neurons in Restricted Boltzmann Machines, the magnitude of a squashed capsule firing takes values between zero and one, representing the probability of the capsule being on. This analogy motivates the design of an energy function for capsule networks. In order to have an efficient sampling procedure where hidden layer nodes are not connected, the energy function is made consistent with dynamic routing in the sense of the probability of a capsule firing, and inference on the capsule network is computed with the dynamic routing between capsules procedure. In order to optimize the log-likelihood of the visible layer capsules, the gradient is found in terms of this energy function. The developed unsupervised learning algorithm is used to train a capsule network on standard vision datasets, and is able to generate realistic looking images from its learned distribution.