The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model consists of N...

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle...

The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM, each state is considered to...

Telescoping Markov chain Markov condition Causal Markov condition Markov model Hidden Markov model Hidden semi-Markov model Layered hidden Markov model Hierarchical...

been modeled using Markov chains, also including modeling the two states of clear and cloudiness as a two-state Markov chain. Hidden Markov models have...

probability Law of total variance Law of truly large numbers Layered hidden Markov model Le Cam's theorem Lead time bias Least absolute deviations Least-angle...

network if it has at least two hidden layers. Artificial neural networks are used for various tasks, including predictive modeling, adaptive control, and solving...

analysis Latent variable Latent variable model Lattice Miner Layered hidden Markov model Learnable function class Least squares support vector machine...

feed-forward layers contain most of the parameters in a Transformer model. The feedforward network (FFN) modules in a Transformer are 2-layered multilayer...

Examples of such a hierarchical model are Layered Hidden Markov Models (LHMMs) and the hierarchical hidden Markov model (HHMM), which have been shown to...

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language...

is a type of binary pairwise Markov random field (undirected probabilistic graphical model) with multiple layers of hidden random variables. It is a network...

vision-language model composed of a language model (Vicuna-13B) and a vision model (ViT-L/14), connected by a linear layer. Only the linear layer is finetuned...

rules. In the latter case, a hidden Markov model can provide the probabilities for the surrounding context. A context model can also apply to the surrounding...

to recognize context-sensitive languages unlike previous models based on hidden Markov models (HMM) and similar concepts. Gated recurrent unit (GRU), introduced...

consists of an input layer, hidden layers and an output layer. In a convolutional neural network, the hidden layers include one or more layers that perform convolutions...

logical model of biological neural networks. In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer...

improving this choice by trying both directions over time. For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing...

A Markov-modulated denial-of-service attack occurs when the attacker disrupts control packets using a hidden Markov model. A setting in which Markov-model...

logical model of biological neural networks. In 1958, Frank Rosenblatt proposed the multilayered perceptron model, consisting of an input layer, a hidden layer...

preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning. In classical...

generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections...

Autoencoders are often trained with a single-layer encoder and a single-layer decoder, but using many-layered (deep) encoders and decoders offers many advantages...

Reddy's students James Baker and Janet M. Baker began using the hidden Markov model (HMM) for speech recognition. James Baker had learned about HMMs...

theorem applies to feedforward networks with a single hidden layer. It states that if the layer's activation function is non-polynomial (which is true...

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...

context-dependent output layers produced error rates dramatically lower than then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also...

Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light...

written in C++, supporting methods including neural networks, SVM, hidden Markov models, etc. It was improved to Torch7 in 2012. Development on Torch ceased...

distribution by a linear-softmax operation on the activations of the hidden neurons within the model. The original paper demonstrated its effectiveness for recurrent...