7,632 research outputs found
Generative and Discriminative Text Classification with Recurrent Neural Networks
We empirically characterize the performance of discriminative and generative
LSTM models for text classification. We find that although RNN-based generative
models are more powerful than their bag-of-words ancestors (e.g., they account
for conditional dependencies across words in a document), they have higher
asymptotic error rates than discriminatively trained RNN models. However we
also find that generative models approach their asymptotic error rate more
rapidly than their discriminative counterparts---the same pattern that Ng &
Jordan (2001) proved holds for linear classification models that make more
naive conditional independence assumptions. Building on this finding, we
hypothesize that RNN-based generative classification models will be more robust
to shifts in the data distribution. This hypothesis is confirmed in a series of
experiments in zero-shot and continual learning settings that show that
generative models substantially outperform discriminative models
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by
accommodating knowledge while retaining previously learned experiences. Neural
networks can learn multiple tasks when trained on them jointly, but cannot
maintain performance on previously learned tasks when tasks are presented one
at a time. This problem is called catastrophic forgetting. In this work, we
propose a classification model that learns continuously from sequentially
observed tasks, while preventing catastrophic forgetting. We build on the
lifelong generative capabilities of [10] and extend it to the classification
setting by deriving a new variational bound on the joint log likelihood, .Comment: 5 pages, 4 figures, under review in Continual learning Workshop NIPS
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