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Encoder-decoder Model
The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a vector via lookup from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism, allowing the signal for key tokens to be amplified and less important tokens to be diminished. Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM). Later variations have been widely adopted for training large language models (LLM) on large (language) datasets. The modern version of the transformer was proposed in the 2017 paper "Attention Is All You Need" by researchers at Google. Transformers were first developed as an improvement ove ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Vision Transformer
A vision transformer (ViT) is a Transformer (machine learning model), transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into Byte pair encoding, tokens), serializes each patch into a vector, and maps it to a smaller dimension with a single matrix multiplication. These vector Latent space, embeddings are then processed by a BERT (language model), transformer encoder as if they were token embeddings. ViTs were designed as alternatives to convolutional neural networks (CNNs) in computer vision applications. They have different inductive biases, training stability, and data efficiency. Compared to CNNs, ViTs are less data efficient, but have higher capacity. Some of the largest modern computer vision models are ViTs, such as one with 22B parameters. Subsequent to its publication, many variants were proposed, with hybrid architectures with both features of ViTs and CNNs. ViTs have found application in image recognition, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attention Mechanism
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence. In natural language processing, importance is represented b"soft"weights assigned to each word in a sentence. More generally, attention encodes vectors called token embeddings across a fixed-width sequence that can range from tens to millions of tokens in size. Unlike "hard" weights, which are computed during the backwards training pass, "soft" weights exist only in the forward pass and therefore change with every step of the input. Earlier designs implemented the attention mechanism in a serial recurrent neural network (RNN) language translation system, but a more recent design, namely the transformer, removed the slower sequential RNN and relied more heavily on the faster parallel attention scheme. Inspired by ideas about attention in humans, the attention mechanism was developed to address the weaknesses of leveraging ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gated Recurrent Units
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] |
Vanishing-gradient Problem
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial derivative of the loss function. As the number of forward propagation steps in a network increases, for instance due to greater network depth, the gradients of earlier weights are calculated with increasingly many multiplications. These multiplications shrink the gradient magnitude. Consequently, the gradients of earlier weights will be exponentially smaller than the gradients of later weights. This difference in gradient magnitude might introduce instability in the training process, slow it, or halt it entirely. For instance, consider the hyperbolic tangent activation function. The gradients of this function are in range . The product of repeated multiplication with such gradients decreases exponenti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Elman Network
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] |
BERT (language Model)
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the Transformer (machine learning model), encoder-only transformer architecture. BERT dramatically improved the State of the art, state-of-the-art for large language model, large language models. , BERT is a ubiquitous baseline in natural language processing (NLP) experiments. BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, Latent space, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It is an evolutionary step over ELMo, and spawned the study of "BERTology", which attempts to interpret what is learned by BERT. BERT wa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generative Pre-trained Transformer
A generative pre-trained transformer (GPT) is a type of large language model (LLM) and a prominent framework for generative artificial intelligence. It is an Neural network (machine learning), artificial neural network that is used in natural language processing by machines. It is based on the Transformer (deep learning architecture), transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content. As of 2023, most LLMs had these characteristics and are sometimes referred to broadly as GPTs. The first GPT was introduced in 2018 by OpenAI. OpenAI has released significant #Foundation models, GPT foundation models that have been sequentially numbered, to comprise its "GPT-''n''" series. Each of these was significantly more capable than the previous, due to increased size (number of trainable parameters) and training. The most recent of these, GPT-4o, was released in May 2024. Such models have been the basis fo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Transfer Learning
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing/transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning and multi-objective optimization. History In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report considered the application of transfer learni ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Computer Chess
Computer chess includes both hardware (dedicated computers) and software capable of playing chess. Computer chess provides opportunities for players to practice even in the absence of human opponents, and also provides opportunities for analysis, entertainment and training. Computer chess applications that play at the level of a Chess title, chess grandmaster or higher are available on hardware from supercomputers to Smartphone, smart phones. Standalone chess-playing machines are also available. Stockfish (chess), Stockfish, Leela Chess Zero, GNU Chess, Fruit (software), Fruit, and other free open source applications are available for various platforms. Computer chess applications, whether implemented in hardware or software, use different strategies than humans to choose their moves: they use Heuristic (computer science), heuristic methods to build, search and evaluate Tree (data structure), trees representing sequences of moves from the current position and attempt to execute ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robotics
Robotics is the interdisciplinary study and practice of the design, construction, operation, and use of robots. Within mechanical engineering, robotics is the design and construction of the physical structures of robots, while in computer science, robotics focuses on robotic automation algorithms. Other disciplines contributing to robotics include electrical engineering, electrical, control engineering, control, software engineering, software, Information engineering (field), information, electronics, electronic, telecommunications engineering, telecommunication, computer engineering, computer, mechatronic, and materials engineering, materials engineering. The goal of most robotics is to design machines that can help and assist humans. Many robots are built to do jobs that are hazardous to people, such as finding survivors in unstable ruins, and exploring space, mines and shipwrecks. Others replace people in jobs that are boring, repetitive, or unpleasant, such as cleaning, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
