7,917 research outputs found
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Complex computational models are often designed to simulate real-world
physical phenomena in many scientific disciplines. However, these simulation
models tend to be computationally very expensive and involve a large number of
simulation input parameters which need to be analyzed and properly calibrated
before the models can be applied for real scientific studies. We propose a
visual analysis system to facilitate interactive exploratory analysis of
high-dimensional input parameter space for a complex yeast cell polarization
simulation. The proposed system can assist the computational biologists, who
designed the simulation model, to visually calibrate the input parameters by
modifying the parameter values and immediately visualizing the predicted
simulation outcome without having the need to run the original expensive
simulation for every instance. Our proposed visual analysis system is driven by
a trained neural network-based surrogate model as the backend analysis
framework. Surrogate models are widely used in the field of simulation sciences
to efficiently analyze computationally expensive simulation models. In this
work, we demonstrate the advantage of using neural networks as surrogate models
for visual analysis by incorporating some of the recent advances in the field
of uncertainty quantification, interpretability and explainability of neural
network-based models. We utilize the trained network to perform interactive
parameter sensitivity analysis of the original simulation at multiple
levels-of-detail as well as recommend optimal parameter configurations using
the activation maximization framework of neural networks. We also facilitate
detail analysis of the trained network to extract useful insights about the
simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic
Constraining Implicit Space with Minimum Description Length: An Unsupervised Attention Mechanism across Neural Network Layers
Inspired by the adaptation phenomenon of neuronal firing, we propose the
regularity normalization (RN) as an unsupervised attention mechanism (UAM)
which computes the statistical regularity in the implicit space of neural
networks under the Minimum Description Length (MDL) principle. Treating the
neural network optimization process as a partially observable model selection
problem, UAM constrains the implicit space by a normalization factor, the
universal code length. We compute this universal code incrementally across
neural network layers and demonstrated the flexibility to include data priors
such as top-down attention and other oracle information. Empirically, our
approach outperforms existing normalization methods in tackling limited,
imbalanced and non-stationary input distribution in image classification,
classic control, procedurally-generated reinforcement learning, generative
modeling, handwriting generation and question answering tasks with various
neural network architectures. Lastly, UAM tracks dependency and critical
learning stages across layers and recurrent time steps of deep networks
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.Comment: 14 pages, 10 figure
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Current learning machines have successfully solved hard application problems,
reaching high accuracy and displaying seemingly "intelligent" behavior. Here we
apply recent techniques for explaining decisions of state-of-the-art learning
machines and analyze various tasks from computer vision and arcade games. This
showcases a spectrum of problem-solving behaviors ranging from naive and
short-sighted, to well-informed and strategic. We observe that standard
performance evaluation metrics can be oblivious to distinguishing these diverse
problem solving behaviors. Furthermore, we propose our semi-automated Spectral
Relevance Analysis that provides a practically effective way of characterizing
and validating the behavior of nonlinear learning machines. This helps to
assess whether a learned model indeed delivers reliably for the problem that it
was conceived for. Furthermore, our work intends to add a voice of caution to
the ongoing excitement about machine intelligence and pledges to evaluate and
judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication
Linking Image and Text with 2-Way Nets
Linking two data sources is a basic building block in numerous computer
vision problems. Canonical Correlation Analysis (CCA) achieves this by
utilizing a linear optimizer in order to maximize the correlation between the
two views. Recent work makes use of non-linear models, including deep learning
techniques, that optimize the CCA loss in some feature space. In this paper, we
introduce a novel, bi-directional neural network architecture for the task of
matching vectors from two data sources. Our approach employs two tied neural
network channels that project the two views into a common, maximally correlated
space using the Euclidean loss. We show a direct link between the
correlation-based loss and Euclidean loss, enabling the use of Euclidean loss
for correlation maximization. To overcome common Euclidean regression
optimization problems, we modify well-known techniques to our problem,
including batch normalization and dropout. We show state of the art results on
a number of computer vision matching tasks including MNIST image matching and
sentence-image matching on the Flickr8k, Flickr30k and COCO datasets.Comment: 14 pages, 2 figures, 6 table
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