1,022 research outputs found
Negative Differential Spin Conductance by Population Switching
An examination of the properties of many-electron conduction through
spin-degenerate systems can lead to situations where increasing the bias
voltage applied to the system is predicted to decrease the current flowing
through it, for the electrons of a particular spin. While this does not
necessarily constitute negative differential conductance (NDC) per se, it is an
example of negative differential conductance per spin (NDSC) which to our
knowledge is discussed here for the first time. Within a many-body master
equation approach which accounts for charging effects in the Coulomb Blockade
regime, we show how this might occur.Comment: 6 page, 2 figure
RoMA: a Method for Neural Network Robustness Measurement and Assessment
Neural network models have become the leading solution for a large variety of
tasks, such as classification, language processing, protein folding, and
others. However, their reliability is heavily plagued by adversarial inputs:
small input perturbations that cause the model to produce erroneous outputs.
Adversarial inputs can occur naturally when the system's environment behaves
randomly, even in the absence of a malicious adversary, and are a severe cause
for concern when attempting to deploy neural networks within critical systems.
In this paper, we present a new statistical method, called Robustness
Measurement and Assessment (RoMA), which can measure the expected robustness of
a neural network model. Specifically, RoMA determines the probability that a
random input perturbation might cause misclassification. The method allows us
to provide formal guarantees regarding the expected frequency of errors that a
trained model will encounter after deployment. Our approach can be applied to
large-scale, black-box neural networks, which is a significant advantage
compared to recently proposed verification methods. We apply our approach in
two ways: comparing the robustness of different models, and measuring how a
model's robustness is affected by the magnitude of input perturbation. One
interesting insight obtained through this work is that, in a classification
network, different output labels can exhibit very different robustness levels.
We term this phenomenon categorial robustness. Our ability to perform risk and
robustness assessments on a categorial basis opens the door to risk mitigation,
which may prove to be a significant step towards neural network certification
in safety-critical applications
On Reducing Undesirable Behavior in Deep Reinforcement Learning Models
Deep reinforcement learning (DRL) has proven extremely useful in a large
variety of application domains. However, even successful DRL-based software can
exhibit highly undesirable behavior. This is due to DRL training being based on
maximizing a reward function, which typically captures general trends but
cannot precisely capture, or rule out, certain behaviors of the system. In this
paper, we propose a novel framework aimed at drastically reducing the
undesirable behavior of DRL-based software, while maintaining its excellent
performance. In addition, our framework can assist in providing engineers with
a comprehensible characterization of such undesirable behavior. Under the hood,
our approach is based on extracting decision tree classifiers from erroneous
state-action pairs, and then integrating these trees into the DRL training
loop, penalizing the system whenever it performs an error. We provide a
proof-of-concept implementation of our approach, and use it to evaluate the
technique on three significant case studies. We find that our approach can
extend existing frameworks in a straightforward manner, and incurs only a
slight overhead in training time. Further, it incurs only a very slight hit to
performance, or even in some cases - improves it, while significantly reducing
the frequency of undesirable behavior
Developing Models to Visualize & Analyze User Interaction for Financial Technology Websites
Vestigo Ventures manually processes website traffic data to analyze the business performance of financial technology companies. By analyzing how people navigate through company websites, Vestigo aims to understand different customer activity patterns. Our team designed and implemented a tool that automatically processes clickstream data to visualize different customer activity within a website and compute statistics about user activity. This tool will provide Vestigo insight on the effectiveness of their clients’ website structures and help them make recommendations to their clients
DelBugV: Delta-Debugging Neural Network Verifiers
Deep neural networks (DNNs) are becoming a key component in diverse systems
across the board. However, despite their success, they often err miserably; and
this has triggered significant interest in formally verifying them.
Unfortunately, DNN verifiers are intricate tools, and are themselves
susceptible to soundness bugs. Due to the complexity of DNN verifiers, as well
as the sizes of the DNNs being verified, debugging such errors is a daunting
task. Here, we present a novel tool, named DelBugV, that uses automated delta
debugging techniques on DNN verifiers. Given a malfunctioning DNN verifier and
a correct verifier as a point of reference (or, in some cases, just a single,
malfunctioning verifier), DelBugV can produce much simpler DNN verification
instances that still trigger undesired behavior -- greatly facilitating the
task of debugging the faulty verifier. Our tool is modular and extensible, and
can easily be enhanced with additional network simplification methods and
strategies. For evaluation purposes, we ran DelBugV on 4 DNN verification
engines, which were observed to produce incorrect results at the 2021 neural
network verification competition (VNN-COMP'21). We were able to simplify many
of the verification queries that trigger these faulty behaviors, by as much as
99%. We regard our work as a step towards the ultimate goal of producing
reliable and trustworthy DNN-based software
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