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Michael J. Black
Michael J. Black is an American-born computer scientist working in Tübingen, Germany. He is a founding director at the Max Planck Institute for Intelligent Systems where he leads the Perceiving Systems Department in research focused on computer vision, machine learning, and computer graphics. He is also an Honorary Professor at the University of Tübingen. Black is the only researcher in the field to have won all three major test-of-time prizes in computer vision: the 2010 Koenderink Prize at the European Conference on Computer Vision (ECCV), the 2013 Helmholtz Prize at the International Conference on Computer Vision (ICCV), and the 2020 Longuet-Higgins Prize at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Research Optical flow Black's thesis reformulated optical flow estimation as a robust M-estimation problem. The main observation was that spatial discontinuities in image motion and violations of the standard brightness constancy assumption could be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
North Carolina
North Carolina () is a state in the Southeastern region of the United States. The state is the 28th largest and 9th-most populous of the United States. It is bordered by Virginia to the north, the Atlantic Ocean to the east, Georgia and South Carolina to the south, and Tennessee to the west. In the 2020 census, the state had a population of 10,439,388. Raleigh is the state's capital and Charlotte is its largest city. The Charlotte metropolitan area, with a population of 2,595,027 in 2020, is the most-populous metropolitan area in North Carolina, the 21st-most populous in the United States, and the largest banking center in the nation after New York City. The Raleigh-Durham-Cary combined statistical area is the second-largest metropolitan area in the state and 32nd-most populous in the United States, with a population of 2,043,867 in 2020, and is home to the largest research park in the United States, Research Triangle Park. The earliest evidence of human occu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
The Matrix Reloaded
''The Matrix Reloaded'' is a 2003 American science-fiction action film written and directed by the Wachowskis. It is a sequel to ''The Matrix'' (1999) and the second installment in the ''Matrix'' film series. The film stars Keanu Reeves, Laurence Fishburne, Carrie-Anne Moss, Hugo Weaving and Gloria Foster who reprise their roles from the previous film, while Jada Pinkett Smith was introduced in the cast. The film premiered on May 7, 2003, in Westwood, Los Angeles, California, and had its worldwide release by Warner Bros. Pictures on May 15, 2003, including a screening out of competition at the 2003 Cannes Film Festival. The video game '' Enter the Matrix'' and '' The Animatrix'', a collection of short animations, supported and expanded the film's story. The film received generally positive reviews from critics, although most felt it inferior to the first film. It grossed $741.8 million worldwide, breaking '' Terminator 2: Judgment Day''s record and becoming the highest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neuroprosthetics
Neuroprosthetics (also called neural prosthetics) is a discipline related to neuroscience and biomedical engineering concerned with developing neural prostheses. They are sometimes contrasted with a brain–computer interface, which connects the brain to a computer rather than a device meant to replace missing biological functionality. Neural prostheses are a series of devices that can substitute a motor, sensory or cognitive modality that might have been damaged as a result of an injury or a disease. Cochlear implants provide an example of such devices. These devices substitute the functions performed by the eardrum and stapes while simulating the frequency analysis performed in the cochlea. A microphone on an external unit gathers the sound and processes it; the processed signal is then transferred to an implanted unit that stimulates the auditory nerve through a microelectrode array. Through the replacement or augmentation of damaged senses, these devices are intended to imp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
BrainGate
BrainGate is a brain implant system built and previously owned by Cyberkinetics, currently under development and in clinical trials, designed to help those who have lost control of their limbs, or other bodily functions, such as patients with amyotrophic lateral sclerosis (ALS) or spinal cord injury. The Braingate technology and related Cyberkinetic’s assets are now owned by privately held Braingate, Co. The sensor, which is implanted into the brain, monitors brain activity in the patient and converts the intention of the user into computer commands. Technology In its current form, BrainGate consists of a sensor implanted in the brain and an external decoder device, which connects to some kind of prosthetic or other external object. The sensor is in the form of a microelectrode array, formerly known as the Utah Array, which consists of 100 hair-thin electrodes that sense the electromagnetic signature of neurons firing in specific areas of the brain, for example, the area ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Brown University
Brown University is a private research university in Providence, Rhode Island. Brown is the seventh-oldest institution of higher education in the United States, founded in 1764 as the College in the English Colony of Rhode Island and Providence Plantations. Brown is one of nine colonial colleges chartered before the American Revolution. Admissions at Brown is among the most selective in the United States. In 2022, the university reported a first year acceptance rate of 5%. It is a member of the Ivy League. Brown was the first college in the United States to codify in its charter that admission and instruction of students was to be equal regardless of their religious affiliation. The university is home to the oldest applied mathematics program in the United States, the oldest engineering program in the Ivy League, and the third-oldest medical program in New England. The university was one of the early doctoral-granting U.S. institutions in the late 19th century, adding ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
John Donoghue (neuroscientist)
__NOTOC__ John P. Donoghue (born 1949) is an American neuroscientist; he is currently the Henry Merritt Wriston Professor of Neuroscience and Professor of Engineering at Brown University, where he has taught since 1984. Donoghue founded Brown's Carney Institute for Brain Science and directed the institute from 2008 to 2015. He later served as the founding director of the Wyss Center for Bio and Neuroengineering at Campus Biotech in Geneva, Switzerland. Donoghue is best known for his work developing BrainGate and is recognized as a pioneer in neuroprosthetics and brain–computer interfaces. Early life and education John P. Donoghue was born in 1949 in Cambridge, Massachusetts. He earned a Bachelor of Arts in Biology from Boston University in 1971, a master's degree in anatomy from the University of Vermont in 1976, and a PhD from Brown University in 1979. Professional career Donoghue is a founder of the discipline of neuroprosthetics and coordinated the team that develo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expectation–maximization Algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the ''E'' step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. History The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin. They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. One of the earliest is the gene-counting method for estimating alle ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mixture Model
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with "mixture distributions" relate to deriving the properties of the overall population from those of the sub-populations, "mixture models" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information. Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). However, compositional models can be thought of as mixture models ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation- equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Product Of Experts
Product of experts (PoE) is a machine learning technique. It models a probability distribution by combining the output from several simpler distributions. It was proposed by Geoffrey Hinton, along with an algorithm for training the parameters of such a system. The core idea is to combine several probability distributions ("experts") by multiplying their density functions—making the PoE classification similar to an "and" operation. This allows each expert to make decisions on the basis of a few dimensions without having to cover the full dimensionality of a problem. This is related to (but quite different from) a mixture model In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observat ..., where several probability distributions are combined via an "or" operation, which is a weighted sum of th ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Principal-component Analysis
Principal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Many studies use the first two principal components in order to plot the data in two dimensions and to visually identify clusters of closely related data points. Principal component analysis has applications in many fields such as population genetics, microbiome studies, and atmospheric science. The principal components of a collection of points in a real coordinate space are a sequence of p unit vectors, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Random Field
In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Andrey Markov, Jr., Markov random field if it satisfies Markov properties. The concept originates from the Spin glass#Sherrington–Kirkpatrick model, Sherrington–Kirkpatrick model. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed acyclic graph, directed and acyclic, whereas Markov networks are undirected and may be cyclic. Thus, a Markov network can represent certain dependencies that a Bayesian network cannot (such as cyclic dependencies ); on the other hand, it can't represent certain dependencies that a Bayesian network can (such as induced dependencies ). The underlying graph of a Markov random field may be finite or infinite ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
