The Adaptive Span Transformer is a deep learning model that uses a self-attention mechanism to process long sequences of data. It is an improved version of the Transformer model that allows the network to choose its own context size by utilizing adaptive masking. This way, each attention layer can gather information on its own context, resulting in better scaling to input sequences with more than 8 thousand tokens.
What is the Adaptive Span Transformer?
The Adaptive Span Transformer is a neur
What is DeLighT?
DeLighT is a transformer architecture that aims to improve parameter efficiency by using DExTra, a light-weight transformation within each Transformer block, and block-wise scaling across blocks. This allows for more efficient use of single-headed attention and bottleneck FFN layers, and shallower and narrower DeLighT blocks near the input, and wider and deeper DeLighT blocks near the output.
What is a Transformer Architecture?
A transformer architecture is a type of neural
A Feedback Transformer is a type of sequential transformer that utilizes a feedback mechanism to expose all previous representations to all future representations. This unique architecture allows for recursive computation, building stronger representations by utilizing past representations.
What is a Feedback Transformer?
A Feedback Transformer is a type of neural network architecture that is used in natural language processing tasks, image recognition, and other artificial intelligence appli
Are you fascinated by how computers can understand and process human language? If you are, then you might be interested in the latest advancement in natural language processing technology called GPT.
What is GPT?
GPT stands for Generative Pre-trained Transformer. It is a type of neural network architecture that uses a transformer-based model for natural language processing tasks. With its advanced language processing capabilities, it is capable of understanding and generating human-like text.
The Levenshtein Transformer: Enhancing Flexibility in Language Decoding
The Levenshtein Transformer (LevT) is a type of transformer that addresses the limitations of previous decoding models by introducing two basic operations—insertion and deletion. These operations make decoding more flexible, allowing for the revision, replacement, revocation, or deletion of any part of the generated text. LevT is trained using imitation learning, making it a highly effective model for language decoding.
B
Introduction to Linformer
Linformer is a linear Transformer model that resolves the self-attention bottleneck associated with Transformer models. It utilizes a linear self-attention mechanism to improve performance and make the model more efficient. By decomposing self-attention into multiple smaller attentions through linear projection, Linformer effectively creates a low-rank factorization of the original attention, reducing the computational cost of processing the input sequence.
The Probl
Overview of Primer: A Transformer-Based Architecture with Multi-DConv-Head-Attention
Primer is a new transformer-based architecture built using two improvements found through neural architecture search. The architecture uses the squared RELU activations and depthwise convolutions in the attention multi-head projections, resulting in a new multi-dconv-head-attention module. The module helps improve the accuracy and speed of natural language processing (NLP) models by combining the traditional tr
The Routing Transformer: A New Approach to Self-Attention in Machine Learning
Self-attention is a crucial feature in modern machine learning that allows models to focus on specific information while ignoring irrelevant data. This has been particularly successful in natural language processing tasks such as language translation, but it has also found use in image recognition and speech processing. One of the most popular self-attention models is the Transformer, which has revolutionized the fiel
What is a Sandwich Transformer?
A Sandwich Transformer is a type of Transformer architecture that reorders the sublayers to achieve better performance. Transformers are a type of neural network that are commonly used in natural language processing and other tasks that require a sequence to sequence mapping. They work by processing the input data in parallel through a series of sublayers.
The Sandwich Transformer reorders the sublayers in a way that optimizes the model's performance. The author
The Sinkhorn Transformer is an advanced type of transformer that uses Sparse Sinkhorn Attention as one of its components. This new attention mechanism offers improved memory complexity and sparse attention, which is an essential feature when working with large datasets, deep learning models, and other complex machine learning scenarios.
Transformer Overview
The transformer is a type of neural network architecture that is widely used in natural language processing, image recognition, and other
A Sparse Transformer is a new and improved version of the Transformer architecture which is used in Natural Language Processing (NLP). It is designed to reduce memory and time usage while still producing accurate results. The main idea behind the Sparse Transformer is to utilize sparse factorizations of the attention matrix. This allows for faster computation by only looking at subsets of the attention matrix as needed.
What is the Transformer Architecture?
Before diving into the intricacies
What is Transformer-XL?
Transformer-XL is a type of Transformer architecture that incorporates the notion of recurrence to the deep self-attention network. It is designed to model long sequences of text by reusing hidden states from previous segments, which serve as a memory for the current segment. This enables the model to establish connections between different segments and thus model long-term dependency more efficiently.
How does it work?
The Transformer-XL uses a new form of attention
Transformers are a significant advancement in the field of artificial intelligence and machine learning. They are model architectures that rely on an attention mechanism instead of recurrence, unlike previous models based on recurrent or convolutional neural networks. The attention mechanism allows for global dependencies between input and output, resulting in better performance and more parallelization.
What is a Transformer?
A Transformer is a type of neural network architecture used for se
The Universal Transformer is an advanced neural network architecture that improves on the already powerful Transformer model.
What is the Transformer architecture?
The Transformer architecture is a type of neural network model widely used in natural language processing tasks such as language translation, text summarization, and sentiment analysis. Transformer models are known for their high performance and efficiency in processing sequential data. They use self-attention mechanisms and parall
XLNet is a type of language model that uses a technique called autoregressive modeling to predict the likelihood of a sequence of words. Unlike other language models, XLNet does not rely on a fixed order to predict the likelihood of a sequence, but instead uses all possible factorization order permutations to learn bidirectional context. This allows each position in the sequence to learn from both the left and the right, maximizing the context for each position.
What is Autoregressive Language
