80,742 research outputs found
Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics
This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository
DeepStory: Video Story QA by Deep Embedded Memory Networks
Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-story learning model, i.e. Deep Embedded Memory Networks
(DEMN), to reconstruct stories from a joint scene-dialogue video stream using a
latent embedding space of observed data. The video stories are stored in a
long-term memory component. For a given question, an LSTM-based attention model
uses the long-term memory to recall the best question-story-answer triplet by
focusing on specific words containing key information. We trained the DEMN on a
novel QA dataset of children's cartoon video series, Pororo. The dataset
contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained
sentences for scene description, and 8,913 story-related QA pairs. Our
experimental results show that the DEMN outperforms other QA models. This is
mainly due to 1) the reconstruction of video stories in a scene-dialogue
combined form that utilize the latent embedding and 2) attention. DEMN also
achieved state-of-the-art results on the MovieQA benchmark.Comment: 7 pages, accepted for IJCAI 201
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
Designing a logo is a long, complicated, and expensive process for any
designer. However, recent advancements in generative algorithms provide models
that could offer a possible solution. Logos are multi-modal, have very few
categorical properties, and do not have a continuous latent space. Yet,
conditional generative adversarial networks can be used to generate logos that
could help designers in their creative process. We propose LoGAN: an improved
auxiliary classifier Wasserstein generative adversarial neural network (with
gradient penalty) that is able to generate logos conditioned on twelve
different colors. In 768 generated instances (12 classes and 64 logos per
class), when looking at the most prominent color, the conditional generation
part of the model has an overall precision and recall of 0.8 and 0.7
respectively. LoGAN's results offer a first glance at how artificial
intelligence can be used to assist designers in their creative process and open
promising future directions, such as including more descriptive labels which
will provide a more exhaustive and easy-to-use system.Comment: 6 page, ICMLA1
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