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An addressing mode specifies how to calculate the effective memory address of an operand by using information held in registers and/or constants contained within a machine instruction or elsewhere. In computer programming, addressing modes are primarily of interest to those who write in assembly languages and to compiler writers.
On graph traversal and sequence-processing tasks with supervised learning, DNCs performed better than alternatives such as long short-term memory or a neural turing machine. [5] With a reinforcement learning approach to a block puzzle problem inspired by SHRDLU, DNC was trained via curriculum learning, and learned to make a plan.
In a computer using virtual memory, accessing the location corresponding to a memory address may involve many levels. In computing, a memory address is a reference to a specific memory location in memory used by both software and hardware. [1] These addresses are fixed-length sequences of digits, typically displayed and handled as unsigned ...
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the family of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) using a limited amount of computer memory. [1] It is a popular algorithm for parameter estimation in machine learning.
In machine learning, the Highway Network was the first working very deep feedforward neural network with hundreds of layers, much deeper than previous neural networks. [1] [2] [3] It uses skip connections modulated by learned gating mechanisms to regulate information flow, inspired by long short-term memory (LSTM) recurrent neural networks.
In machine learning, instance-based learning (sometimes called memory-based learning [1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed ...
In computer science, a tagged pointer is a pointer (concretely a memory address) with additional data associated with it, such as an indirection bit or reference count.This additional data is often "folded" into the pointer, meaning stored inline in the data representing the address, taking advantage of certain properties of memory addressing.
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] 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.