15,753 research outputs found
Discovering Representations for Black-box Optimization
The encoding of solutions in black-box optimization is a delicate,
handcrafted balance between expressiveness and domain knowledge -- between
exploring a wide variety of solutions, and ensuring that those solutions are
useful. Our main insight is that this process can be automated by generating a
dataset of high-performing solutions with a quality diversity algorithm (here,
MAP-Elites), then learning a representation with a generative model (here, a
Variational Autoencoder) from that dataset. Our second insight is that this
representation can be used to scale quality diversity optimization to higher
dimensions -- but only if we carefully mix solutions generated with the learned
representation and those generated with traditional variation operators. We
demonstrate these capabilities by learning an low-dimensional encoding for the
inverse kinematics of a thousand joint planar arm. The results show that
learned representations make it possible to solve high-dimensional problems
with orders of magnitude fewer evaluations than the standard MAP-Elites, and
that, once solved, the produced encoding can be used for rapid optimization of
novel, but similar, tasks. The presented techniques not only scale up quality
diversity algorithms to high dimensions, but show that black-box optimization
encodings can be automatically learned, rather than hand designed.Comment: Presented at GECCO 2020 -- v2 (Previous title 'Automating
Representation Discovery with MAP-Elites'
Gradient-free activation maximization for identifying effective stimuli
A fundamental question for understanding brain function is what types of
stimuli drive neurons to fire. In visual neuroscience, this question has also
been posted as characterizing the receptive field of a neuron. The search for
effective stimuli has traditionally been based on a combination of insights
from previous studies, intuition, and luck. Recently, the same question has
emerged in the study of units in convolutional neural networks (ConvNets), and
together with this question a family of solutions were developed that are
generally referred to as "feature visualization by activation maximization."
We sought to bring in tools and techniques developed for studying ConvNets to
the study of biological neural networks. However, one key difference that
impedes direct translation of tools is that gradients can be obtained from
ConvNets using backpropagation, but such gradients are not available from the
brain. To circumvent this problem, we developed a method for gradient-free
activation maximization by combining a generative neural network with a genetic
algorithm. We termed this method XDream (EXtending DeepDream with real-time
evolution for activation maximization), and we have shown that this method can
reliably create strong stimuli for neurons in the macaque visual cortex (Ponce
et al., 2019). In this paper, we describe extensive experiments characterizing
the XDream method by using ConvNet units as in silico models of neurons. We
show that XDream is applicable across network layers, architectures, and
training sets; examine design choices in the algorithm; and provide practical
guides for choosing hyperparameters in the algorithm. XDream is an efficient
algorithm for uncovering neuronal tuning preferences in black-box networks
using a vast and diverse stimulus space.Comment: 16 pages, 8 figures, 3 table
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.Comment: 10 pages, 8 figures, 1 tabl
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