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Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
If block A1 is accessed at time 1, its recency will be 0; this is the first-accessed block and the IRR will be 1, since it predicts that A1 will be accessed again in time 3. In time 2, since A4 is accessed, the recency will become 0 for A4 and 1 for A1; A4 is the most recently accessed object, and the IRR will become 4.
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1] In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text. If the input text is long, the fixed-length vector ...
A 1-bit saturating counter (essentially a flip-flop) records the last outcome of the branch. This is the most simple version of dynamic branch predictor possible, although it is not very accurate. A 2-bit saturating counter [1] is a state machine with four states: Figure 2: State diagram of 2-bit saturating counter. Strongly not taken; Weakly ...
Visualization of a software buffer overflow. Data is written into A, but is too large to fit within A, so it overflows into B.. In programming and information security, a buffer overflow or buffer overrun is an anomaly whereby a program writes data to a buffer beyond the buffer's allocated memory, overwriting adjacent memory locations.
In computer science, a data buffer (or just buffer) is a region of memory used to store data temporarily while it is being moved from one place to another. Typically, the data is stored in a buffer as it is retrieved from an input device (such as a microphone) or just before it is sent to an output device (such as speakers); however, a buffer may be used when data is moved between processes ...
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
This is mostly attributed to the spread of object-oriented programming techniques that favor large numbers of small functions, use of sophisticated data structures like trees and hash tables that tend to result in chaotic memory reference patterns, and the advent of garbage collection that drastically changed memory access behavior of applications.