Document detail
ID
oai:arXiv.org:2406.10256
Topic
Computer Science - Computation and... Computer Science - Artificial Inte... Computer Science - Machine Learnin...Author
Andonov, Jovan Ganea, Octavian Grnarova, Paulina Bécigneul, Gary Hofmann, ThomasCategory
Computer Science
Year
2024
listing date
6/19/2024
Keywords
modellingAbstract
Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for commercial language modeling.
Recently, it has been shown that when looking at language modelling from a matrix factorization point of view, the final Softmax layer limits the expressiveness of the model, by putting an upper bound on the rank of the resulting matrix.
Additionally, a new family of neural networks based called NeuralODEs, has been introduced as a continuous alternative to Residual Networks.
Moreover, it has been shown that there is a connection between these models and Normalizing Flows.
In this work we propose a new family of language models based on NeuralODEs and the continuous analogue of Normalizing Flows and manage to improve on some of the baselines.
;Comment: Master's thesis
Andonov, Jovan,Ganea, Octavian,Grnarova, Paulina,Bécigneul, Gary,Hofmann, Thomas, 2024, Explicit Word Density Estimation for Language Modelling
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