15,879 research outputs found
Learning Logistic Circuits
This paper proposes a new classification model called logistic circuits. On
MNIST and Fashion datasets, our learning algorithm outperforms neural networks
that have an order of magnitude more parameters. Yet, logistic circuits have a
distinct origin in symbolic AI, forming a discriminative counterpart to
probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that
parameter learning for logistic circuits is convex optimization, and that a
simple local search algorithm can induce strong model structures from data.Comment: Published in the Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (AAAI19
Bivariate Beta-LSTM
Long Short-Term Memory (LSTM) infers the long term dependency through a cell
state maintained by the input and the forget gate structures, which models a
gate output as a value in [0,1] through a sigmoid function. However, due to the
graduality of the sigmoid function, the sigmoid gate is not flexible in
representing multi-modality or skewness. Besides, the previous models lack
modeling on the correlation between the gates, which would be a new method to
adopt inductive bias for a relationship between previous and current input.
This paper proposes a new gate structure with the bivariate Beta distribution.
The proposed gate structure enables probabilistic modeling on the gates within
the LSTM cell so that the modelers can customize the cell state flow with
priors and distributions. Moreover, we theoretically show the higher upper
bound of the gradient compared to the sigmoid function, and we empirically
observed that the bivariate Beta distribution gate structure provides higher
gradient values in training. We demonstrate the effectiveness of bivariate Beta
gate structure on the sentence classification, image classification, polyphonic
music modeling, and image caption generation.Comment: AAAI 202
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Recurrent neural networks (RNNs) stand at the forefront of many recent
developments in deep learning. Yet a major difficulty with these models is
their tendency to overfit, with dropout shown to fail when applied to recurrent
layers. Recent results at the intersection of Bayesian modelling and deep
learning offer a Bayesian interpretation of common deep learning techniques
such as dropout. This grounding of dropout in approximate Bayesian inference
suggests an extension of the theoretical results, offering insights into the
use of dropout with RNN models. We apply this new variational inference based
dropout technique in LSTM and GRU models, assessing it on language modelling
and sentiment analysis tasks. The new approach outperforms existing techniques,
and to the best of our knowledge improves on the single model state-of-the-art
in language modelling with the Penn Treebank (73.4 test perplexity). This
extends our arsenal of variational tools in deep learning.Comment: Added clarifications; Published in NIPS 201
- …