15,171 research outputs found
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
Reliability analysis of dynamic systems by translating temporal fault trees into Bayesian networks
Classical combinatorial fault trees can be used to assess combinations of failures but are unable to capture sequences of faults, which are important in complex dynamic systems. A number of proposed techniques extend fault tree analysis for dynamic systems. One of such technique, Pandora, introduces temporal gates to capture the sequencing of events and allows qualitative analysis of temporal fault trees. Pandora can be easily integrated in model-based design and analysis techniques. It is, therefore, useful to explore the possible avenues for quantitative analysis of Pandora temporal fault trees, and we identify Bayesian Networks as a possible framework for such analysis. We describe how Pandora fault trees can be translated to Bayesian Networks for dynamic dependability analysis and demonstrate the process on a simplified fuel system model. The conversion facilitates predictive reliability analysis of Pandora fault trees, but also opens the way for post-hoc diagnostic analysis of failures
What May Visualization Processes Optimize?
In this paper, we present an abstract model of visualization and inference
processes and describe an information-theoretic measure for optimizing such
processes. In order to obtain such an abstraction, we first examined six
classes of workflows in data analysis and visualization, and identified four
levels of typical visualization components, namely disseminative,
observational, analytical and model-developmental visualization. We noticed a
common phenomenon at different levels of visualization, that is, the
transformation of data spaces (referred to as alphabets) usually corresponds to
the reduction of maximal entropy along a workflow. Based on this observation,
we establish an information-theoretic measure of cost-benefit ratio that may be
used as a cost function for optimizing a data visualization process. To
demonstrate the validity of this measure, we examined a number of successful
visualization processes in the literature, and showed that the
information-theoretic measure can mathematically explain the advantages of such
processes over possible alternatives.Comment: 10 page
Abandon Statistical Significance
We discuss problems the null hypothesis significance testing (NHST) paradigm
poses for replication and more broadly in the biomedical and social sciences as
well as how these problems remain unresolved by proposals involving modified
p-value thresholds, confidence intervals, and Bayes factors. We then discuss
our own proposal, which is to abandon statistical significance. We recommend
dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the
default statistical paradigm for research, publication, and discovery in the
biomedical and social sciences. Specifically, we propose that the p-value be
demoted from its threshold screening role and instead, treated continuously, be
considered along with currently subordinate factors (e.g., related prior
evidence, plausibility of mechanism, study design and data quality, real world
costs and benefits, novelty of finding, and other factors that vary by research
domain) as just one among many pieces of evidence. We have no desire to "ban"
p-values or other purely statistical measures. Rather, we believe that such
measures should not be thresholded and that, thresholded or not, they should
not take priority over the currently subordinate factors. We also argue that it
seldom makes sense to calibrate evidence as a function of p-values or other
purely statistical measures. We offer recommendations for how our proposal can
be implemented in the scientific publication process as well as in statistical
decision making more broadly
Machine learning techniques for fault isolation and sensor placement
Fault isolation and sensor placement are vital for monitoring and diagnosis. A sensor conveys information about a system's state that guides troubleshooting if problems arise. We are using machine learning methods to uncover behavioral patterns over snapshots of system simulations that will aid fault isolation and sensor placement, with an eye towards minimality, fault coverage, and noise tolerance
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Sub-Optimal Allocation of Time in Sequential Movements
The allocation of limited resources such as time or energy is a core problem that organisms face when planning complex
actions. Most previous research concerning planning of movement has focused on the planning of single, isolated
movements. Here we investigated the allocation of time in a pointing task where human subjects attempted to touch two
targets in a specified order to earn monetary rewards. Subjects were required to complete both movements within a limited time but could freely allocate the available time between the movements. The time constraint presents an allocation
problem to the subjects: the more time spent on one movement, the less time is available for the other. In different
conditions we assigned different rewards to the two tokens. How the subject allocated time between movements affected
their expected gain on each trial. We also varied the angle between the first and second movements and the length of the
second movement. Based on our results, we developed and tested a model of speed-accuracy tradeoff for sequential
movements. Using this model we could predict the time allocation that would maximize the expected gain of each subject
in each experimental condition. We compared human performance with predicted optimal performance. We found that all
subjects allocated time sub-optimally, spending more time than they should on the first movement even when the reward
of the second target was five times larger than the first. We conclude that the movement planning system fails to maximize
expected reward in planning sequences of as few as two movements and discuss possible interpretations drawn from
economic theory
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