|
LeNet Architecture
LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATM for reading Cheque, cheques. Convolutional neural networks are a kind of Feedforward neural network, feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing. LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep learning. In general, when "LeNet" is referred to without a number, it refers to the 1998 version, the most well-known version. It is also sometimes called "LeNet-5" or "LeNet5". Development history In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New J ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Yoshua Bengio
Yoshua Bengio (born March 5, 1964) is a Canadian-French computer scientist, and a pioneer of artificial neural networks and deep learning. He is a professor at the Université de Montréal and scientific director of the AI institute Montreal Institute for Learning Algorithms, MILA. Bengio received the 2018 Turing Award, ACM A.M. Turing Award, often referred to as the "List of prizes known as the Nobel of a field or the highest honors of a field, Nobel Prize of Computing", together with Geoffrey Hinton and Yann LeCun, for their foundational work on deep learning. Bengio, Geoffrey Hinton, Hinton, and Yann LeCun, LeCun are sometimes referred to as the "Godfathers of AI". Bengio is the most-cited computer scientist globally (by both total citations and by h-index, ''h''-index), and the most-cited living scientist across all fields (by total citations). In 2024, Time (magazine), ''TIME'' Magazine included Bengio in its Time 100, yearly list of the world's 100 most influential people. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
LeNet Architecture
LeNet is a series of convolutional neural network architectures created by a research group in AT&T Bell Laboratories during the 1988 to 1998 period, centered around Yann LeCun. They were designed for reading small grayscale images of handwritten digits and letters, and were used in ATM for reading Cheque, cheques. Convolutional neural networks are a kind of Feedforward neural network, feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing. LeNet-5 was one of the earliest convolutional neural networks and was historically important during the development of deep learning. In general, when "LeNet" is referred to without a number, it refers to the 1998 version, the most well-known version. It is also sometimes called "LeNet-5" or "LeNet5". Development history In 1988, LeCun joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New J ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SPARCstation 10
The SPARCstation 10 (codenamed ''Campus-2'') is a workstation computer made by Sun Microsystems. Announced on May 19, 1992, the SPARCstation sold for between 18,495 at the low end to US$57,995 at the high end (equivalent to $– in ). Housed in pizza-box form factor, pizza-box case, the SPARCstation 10 was the first desktop computer capable of symmetric multiprocessing from the factory. Sun later replaced it with the SPARCstation 20. The 40-MHz SPARCstation 10 without external cache was the reference for the Standard Performance Evaluation Corporation, SPEC CPU95 benchmark. Sales Volume production commenced on September 1992. By the end of December 1992, the company had shipped over 19,000 SPARCstation 10s, across all models. In 1993, Sun shipped an additional 80,000 units. Between it and its successor the SPARCstation 20, Sun had sold a combined 250,000 units by February 1995. Specifications CPU support The SPARCstation 10 (SS10) contains two MBus (SPARC), MBus slots running ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Distillation
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have more knowledge capacity than small models, this capacity might not be fully utilized. It can be just as computationally expensive to evaluate a model even if it utilizes little of its knowledge capacity. Knowledge distillation transfers knowledge from a large model to a smaller one without loss of validity. As smaller models are less expensive to evaluate, they can be deployed on less powerful hardware (such as a mobile device). Model distillation is not to be confused with model compression, which describes methods to decrease the size of a large model itself, without training a new model. Model compression generally preserves the architecture and the nominal parameter count of the model, while decreasing the bits-per-parameter. Knowledge distilla ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sun-4
Sun-4 is a series of Unix workstations and servers produced by Sun Microsystems, first appearing in July 1987, with the launch of the Sun 4/260. The original Sun-4 series were VMEbus-based systems similar to the earlier Sun-3 series, but employing microprocessors based on Sun's own SPARC V7 RISC architecture in place of the 68k family processors of previous Sun models. Sun 4/280 was a base system used for building an early RAID prototype. Models Models are listed in approximately chronological order. : In 1989, Sun dropped the "Sun-4" name for marketing purposes in favor of the SPARCstation and SPARCserver brands for new models, although early SPARCstation/server models were also assigned Sun-4-series model numbers. For example, the SPARCstation 1 was also known as the Sun 4/60. This practice was phased out with the introduction of the SPARCserver 600MP series in 1991. The term ''Sun-4'' continued to be used in an engineering context to identify the basic hardware archi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Buffalo, New York
Buffalo is a Administrative divisions of New York (state), city in the U.S. state of New York (state), New York and county seat of Erie County, New York, Erie County. It lies in Western New York at the eastern end of Lake Erie, at the head of the Niagara River on the Canada–United States border, Canadian border. With a population of 278,349 according to the 2020 census, Buffalo is the List of municipalities in New York, second-most populous city in New York State after New York City, and the List of United States cities by population, 82nd-most populous city in the U.S. Buffalo is the primary city of the Buffalo–Niagara Falls metropolitan area, which had an estimated population of 1.1 million in 2020, making it the List of metropolitan statistical areas, 49th-largest metro area in the U.S. Before the 17th century, the region was inhabited by nomadic Paleo-Indians who were succeeded by the Neutral Confederacy, Neutral, Erie people, Erie, and Iroquois nations. In the early 1 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Layer
In artificial neural networks, a convolutional layer is a type of network layer that applies a convolution operation to the input. Convolutional layers are some of the primary building blocks of convolutional neural networks (CNNs), a class of neural network most commonly applied to images, video, audio, and other data that have the property of uniform translational symmetry. The convolution operation in a convolutional layer involves sliding a small window (called a kernel or filter) across the input data and computing the dot product between the values in the kernel and the input at each position. This process creates a feature map that represents detected features in the input. Concepts Kernel Kernels, also known as filters, are small matrices of weights that are learned during the training process. Each kernel is responsible for detecting a specific feature in the input data. The size of the kernel is a hyperparameter that affects the network's behavior. Convolution ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Topological Skeleton
In shape analysis, skeleton (or topological skeleton) of a shape is a thin version of that shape that is equidistant to its boundaries. The skeleton usually emphasizes geometrical and topological properties of the shape, such as its connectivity, topology, length, direction, and width. Together with the distance of its points to the shape boundary, the skeleton can also serve as a representation of the shape (they contain all the information necessary to reconstruct the shape). Skeletons have several different mathematical definitions in the technical literature, and there are many different algorithms for computing them. Various different variants of skeleton can also be found, including straight skeletons, morphological skeletons, etc. In the technical literature, the concepts of skeleton and medial axis are used interchangeably by some authors,, Section 11.1.5, p. 650 while some other authors, Section 9.9, p. 382., Section 17.5.2, p. 234. regard them as re ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
