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Gated Recurrent Unit
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, but lacks a context vector or output gate, resulting in fewer parameters than LSTM. GRU's performance on certain tasks of polyphonic music modeling, speech signal modeling and natural language processing was found to be similar to that of LSTM. GRUs showed that gating is indeed helpful in general, and Bengio's team came to no concrete conclusion on which of the two gating units was better. Architecture There are several variations on the full gated unit, with gating done using the previous hidden state and the bias in various combinations, and a simplified form called minimal gated unit. The operator \odot denotes the Hadamard product in the following. Fully gated unit Initially, for t = 0, the output vector is h_0 = 0. : \begin z_t &= \s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recurrent Neural Networks
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output of a neuron at one time step is fed back as input to the network at the next time step. This enables RNNs to capture temporal dependencies and patterns within sequences. The fundamental building block of RNNs is the ''recurrent unit'', which maintains a ''hidden state''—a form of memory that is updated at each time step based on the current input and the previous hidden state. This feedback mechanism allows the network to learn from past inputs and incorporate that knowledge into its current processing. RNNs have been successfully applied to tasks such as unsegmented, connected handwriting recognition, speech recognition, natural language processing, and neural ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Long Short-term Memory
Long short-term memory (LSTM) is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem commonly encountered by traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models, and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps (thus "''long'' short-term memory"). The name is made in analogy with long-term memory and short-term memory and their relationship, studied by cognitive psychologists since the early 20th century. An LSTM unit is typically composed of a cell and three gates: an input gate, an output gate, and a forget gate. The cell remembers values over arbitrary time intervals, and the gates regulate the flow of information into and out of the cell. Forget gates decide what information to discard from the previous state, by mapping the previous state and the current input to a value between 0 and 1. A (rounded) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gating Mechanism
In neural networks, the gating mechanism is an architectural motif for controlling the flow of activation and gradient signals. They are most prominently used in recurrent neural networks (RNNs), but have also found applications in other architectures. RNNs Gating mechanisms are the centerpiece of long short-term memory (LSTM). They were proposed to mitigate the vanishing gradient problem often encountered by regular RNNs. An LSTM unit contains three gates: * An input gate, which controls the flow of new information into the memory cell * A forget gate, which controls how much information is retained from the previous time step * An output gate, which controls how much information is passed to the next layer. The equations for LSTM are: \begin \mathbf_t &= \sigma(\mathbf_t \mathbf_ + \mathbf_ \mathbf_ + \mathbf_i) \\ \mathbf_t &= \sigma(\mathbf_t \mathbf_ + \mathbf_ \mathbf_ + \mathbf_f) \\ \mathbf_t &= \sigma(\mathbf_t \mathbf_ + \mathbf_ \mathbf_ + \mathbf_o) \\ \tilde ... [...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] |
Hadamard Product (matrices)
In mathematics, the Hadamard product (also known as the element-wise product, entrywise product or Schur product) is a binary operation that takes in two Matrix (mathematics), matrices of the same dimensions and returns a matrix of the multiplied corresponding elements. This operation can be thought as a "naive matrix multiplication" and is different from the Matrix multiplication, matrix product. It is attributed to, and named after, either French mathematician Jacques Hadamard or German mathematician Issai Schur. The Hadamard product is associative and Distributive property, distributive. Unlike the matrix product, it is also commutative. Definition For two matrices and of the same dimension , the Hadamard product A \odot B (sometimes A \circ B) is a matrix of the same dimension as the operands, with elements given by :(A \odot B)_ = (A)_ (B)_. For matrices of different dimensions ( and , where or ), the Hadamard product is undefined. An example of the Hadamard product for ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gradient Recurrent Unit
In vector calculus, the gradient of a scalar-valued differentiable function f of several variables is the vector field (or vector-valued function) \nabla f whose value at a point p gives the direction and the rate of fastest increase. The gradient transforms like a vector under change of basis of the space of variables of f. If the gradient of a function is non-zero at a point p, the direction of the gradient is the direction in which the function increases most quickly from p, and the magnitude Magnitude may refer to: Mathematics *Euclidean vector, a quantity defined by both its magnitude and its direction *Magnitude (mathematics), the relative size of an object *Norm (mathematics), a term for the size or length of a vector *Order of ... of the gradient is the rate of increase in that direction, the greatest absolute value, absolute directional derivative. Further, a point where the gradient is the zero vector is known as a stationary point. The gradient thus plays a fun ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
