1,508 research outputs found
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
There have been several attempts at modeling context in robots. However,
either these attempts assume a fixed number of contexts or use a rule-based
approach to determine when to increment the number of contexts. In this paper,
we pose the task of when to increment as a learning problem, which we solve
using a Recurrent Neural Network. We show that the network successfully (with
98\% testing accuracy) learns to predict when to increment, and demonstrate, in
a scene modeling problem (where the correct number of contexts is not known),
that the robot increments the number of contexts in an expected manner (i.e.,
the entropy of the system is reduced). We also present how the incremental
model can be used for various scene reasoning tasks.Comment: The first two authors have contributed equally, 6 pages, 8 figures,
International Conference on Intelligent Robots (IROS 2018
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
This paper presents a novel deep learning-based method for learning a
functional representation of mammalian neural images. The method uses a deep
convolutional denoising autoencoder (CDAE) for generating an invariant, compact
representation of in situ hybridization (ISH) images. While most existing
methods for bio-imaging analysis were not developed to handle images with
highly complex anatomical structures, the results presented in this paper show
that functional representation extracted by CDAE can help learn features of
functional gene ontology categories for their classification in a highly
accurate manner. Using this CDAE representation, our method outperforms the
previous state-of-the-art classification rate, by improving the average AUC
from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates
on input images that were downsampled significantly with respect to the
original ones to make it computationally feasible
MUSE: Modularizing Unsupervised Sense Embeddings
This paper proposes to address the word sense ambiguity issue in an
unsupervised manner, where word sense representations are learned along a word
sense selection mechanism given contexts. Prior work focused on designing a
single model to deliver both mechanisms, and thus suffered from either
coarse-grained representation learning or inefficient sense selection. The
proposed modular approach, MUSE, implements flexible modules to optimize
distinct mechanisms, achieving the first purely sense-level representation
learning system with linear-time sense selection. We leverage reinforcement
learning to enable joint training on the proposed modules, and introduce
various exploration techniques on sense selection for better robustness. The
experiments on benchmark data show that the proposed approach achieves the
state-of-the-art performance on synonym selection as well as on contextual word
similarities in terms of MaxSimC
Preserving Differential Privacy in Convolutional Deep Belief Networks
The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions
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