73 research outputs found
Compelling Intimacies: Domesticity, Sexuality, and Agency
This introduction highlights what we call Compelling Intimacies —the multiple desires, affects, and affinities that arise at the intersection of institutions, actors, technologies, and ethical discourses to exert persuasive pressures on subjects. Each article animates different facets of the intensities born of intimacy as they operate across social and relational fields. The authors separate agency from intention in their efforts to identify the vitality of human and non-human relations. Together, the articles demonstrate how domesticities arise through diverse sets of circumstances, emerging in multiple incarnations—often in the same household—in such a way as to generate a wide range of affects and affinities. Finally, each author turns attention to the so-called small events that come to affirm or deny life as given form in everyday household arrangements, kin relations, friendships, and institutional settings, thereby suggesting the political stakes evoked by differing forms of care
SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation
The testing of Deep Neural Networks (DNNs) has become increasingly important
as DNNs are widely adopted by safety critical systems. While many test adequacy
criteria have been suggested, automated test input generation for many types of
DNNs remains a challenge because the raw input space is too large to randomly
sample or to navigate and search for plausible inputs. Consequently, current
testing techniques for DNNs depend on small local perturbations to existing
inputs, based on the metamorphic testing principle. We propose new ways to
search not over the entire image space, but rather over a plausible input space
that resembles the true training distribution. This space is constructed using
Variational Autoencoders (VAEs), and navigated through their latent vector
space. We show that this space helps efficiently produce test inputs that can
reveal information about the robustness of DNNs when dealing with realistic
tests, opening the field to meaningful exploration through the space of highly
structured images
On the Challenges of Physical Implementations of RBMs
Restricted Boltzmann machines (RBMs) are powerful machine learning models,
but learning and some kinds of inference in the model require sampling-based
approximations, which, in classical digital computers, are implemented using
expensive MCMC. Physical computation offers the opportunity to reduce the cost
of sampling by building physical systems whose natural dynamics correspond to
drawing samples from the desired RBM distribution. Such a system avoids the
burn-in and mixing cost of a Markov chain. However, hardware implementations of
this variety usually entail limitations such as low-precision and limited range
of the parameters and restrictions on the size and topology of the RBM. We
conduct software simulations to determine how harmful each of these
restrictions is. Our simulations are designed to reproduce aspects of the
D-Wave quantum computer, but the issues we investigate arise in most forms of
physical computation
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Catastrophic forgetting is a problem faced by many machine learning models
and algorithms. When trained on one task, then trained on a second task, many
machine learning models "forget" how to perform the first task. This is widely
believed to be a serious problem for neural networks. Here, we investigate the
extent to which the catastrophic forgetting problem occurs for modern neural
networks, comparing both established and recent gradient-based training
algorithms and activation functions. We also examine the effect of the
relationship between the first task and the second task on catastrophic
forgetting. We find that it is always best to train using the dropout
algorithm--the dropout algorithm is consistently best at adapting to the new
task, remembering the old task, and has the best tradeoff curve between these
two extremes. We find that different tasks and relationships between tasks
result in very different rankings of activation function performance. This
suggests the choice of activation function should always be cross-validated
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on
three challenges: the black box learning challenge, the facial expression
recognition challenge, and the multimodal learning challenge. We describe the
datasets created for these challenges and summarize the results of the
competitions. We provide suggestions for organizers of future challenges and
some comments on what kind of knowledge can be gained from machine learning
competitions.Comment: 8 pages, 2 figure
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