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
What is VideoBERT?
VideoBERT is a machine learning model that is used to learn a joint visual-linguistic representation for video. It is adapted from the powerful BERT model, which was originally developed for natural language processing. VideoBERT is capable of performing a variety of tasks related to video, including action classification and video captioning.
How does VideoBERT work?
VideoBERT works by encoding both video frames and textual descriptions of those frames into a joint embedd
Vision-and-Language BERT, also known as ViLBERT, is an innovative model that combines both natural language and image content to learn task-agnostic joint representations. This model is based on the popular BERT architecture and expands it into a multi-modal two-stream model that processes both visual and textual inputs. What sets ViLBERT apart from other models is its ability to interact through co-attentional transformer layers, making it highly versatile and useful for various applications.
XLM is an innovative language model architecture that has been attracting a lot of attention in recent years. It is based on the Transformer model and is pre-trained using one of three language modeling techniques.
The Three Language Modeling Objectives
There are three objectives that are used to pre-train the XLM language model:
Causal Language Modeling
This approach models the probability of a particular word given the previous words in a sentence. This helps to capture the contextual in
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