1,496 research outputs found
Estimating Uncertainty Online Against an Adversary
Assessing uncertainty is an important step towards ensuring the safety and
reliability of machine learning systems. Existing uncertainty estimation
techniques may fail when their modeling assumptions are not met, e.g. when the
data distribution differs from the one seen at training time. Here, we propose
techniques that assess a classification algorithm's uncertainty via calibrated
probabilities (i.e. probabilities that match empirical outcome frequencies in
the long run) and which are guaranteed to be reliable (i.e. accurate and
calibrated) on out-of-distribution input, including input generated by an
adversary. This represents an extension of classical online learning that
handles uncertainty in addition to guaranteeing accuracy under adversarial
assumptions. We establish formal guarantees for our methods, and we validate
them on two real-world problems: question answering and medical diagnosis from
genomic data
Concurrent collaborative captioning
Captioned text transcriptions of the spoken word can benefit hearing impaired people, non native speakers, anyone if no audio is available (e.g. watching TV at an airport) and also anyone who needs to review recordings of what has been said (e.g. at lectures, presentations, meetings etc.) In this paper, a tool is described that facilitates concurrent collaborative captioning by correction of speech recognition errors to provide a sustainable method of making videos accessible to people who find it difficult to understand speech through hearing alone. The tool stores all the edits of all the users and uses a matching algorithm to compare users’ edits to check if they are in agreement
Marginal and simultaneous predictive classification using stratified graphical models
An inductive probabilistic classification rule must generally obey the
principles of Bayesian predictive inference, such that all observed and
unobserved stochastic quantities are jointly modeled and the parameter
uncertainty is fully acknowledged through the posterior predictive
distribution. Several such rules have been recently considered and their
asymptotic behavior has been characterized under the assumption that the
observed features or variables used for building a classifier are conditionally
independent given a simultaneous labeling of both the training samples and
those from an unknown origin. Here we extend the theoretical results to
predictive classifiers acknowledging feature dependencies either through
graphical models or sparser alternatives defined as stratified graphical
models. We also show through experimentation with both synthetic and real data
that the predictive classifiers based on stratified graphical models have
consistently best accuracy compared with the predictive classifiers based on
either conditionally independent features or on ordinary graphical models.Comment: 18 pages, 5 figure
Harnessing AI for Speech Reconstruction using Multi-view Silent Video Feed
Speechreading or lipreading is the technique of understanding and getting
phonetic features from a speaker's visual features such as movement of lips,
face, teeth and tongue. It has a wide range of multimedia applications such as
in surveillance, Internet telephony, and as an aid to a person with hearing
impairments. However, most of the work in speechreading has been limited to
text generation from silent videos. Recently, research has started venturing
into generating (audio) speech from silent video sequences but there have been
no developments thus far in dealing with divergent views and poses of a
speaker. Thus although, we have multiple camera feeds for the speech of a user,
but we have failed in using these multiple video feeds for dealing with the
different poses. To this end, this paper presents the world's first ever
multi-view speech reading and reconstruction system. This work encompasses the
boundaries of multimedia research by putting forth a model which leverages
silent video feeds from multiple cameras recording the same subject to generate
intelligent speech for a speaker. Initial results confirm the usefulness of
exploiting multiple camera views in building an efficient speech reading and
reconstruction system. It further shows the optimal placement of cameras which
would lead to the maximum intelligibility of speech. Next, it lays out various
innovative applications for the proposed system focusing on its potential
prodigious impact in not just security arena but in many other multimedia
analytics problems.Comment: 2018 ACM Multimedia Conference (MM '18), October 22--26, 2018, Seoul,
Republic of Kore
Probabilistic Independence Networks for Hidden Markov Probability Models
Graphical techniques for modeling the dependencies of randomvariables have been explored in a variety of different areas includingstatistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics.Formalisms for manipulating these models have been developedrelatively independently in these research communities. In this paper weexplore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independencenetworks (PINs). The paper contains a self-contained review of the basic principles of PINs.It is shown that the well-known forward-backward (F-B) and Viterbialgorithms for HMMs are special cases of more general inference algorithms forarbitrary PINs. Furthermore, the existence of inference and estimationalgorithms for more general graphical models provides a set of analysistools for HMM practitioners who wish to explore a richer class of HMMstructures.Examples of relatively complex models to handle sensorfusion and coarticulationin speech recognitionare introduced and treated within the graphical model framework toillustrate the advantages of the general approach
Effective Feature Representation for Clinical Text Concept Extraction
Crucial information about the practice of healthcare is recorded only in
free-form text, which creates an enormous opportunity for high-impact NLP.
However, annotated healthcare datasets tend to be small and expensive to
obtain, which raises the question of how to make maximally efficient uses of
the available data. To this end, we develop an LSTM-CRF model for combining
unsupervised word representations and hand-built feature representations
derived from publicly available healthcare ontologies. We show that this
combined model yields superior performance on five datasets of diverse kinds of
healthcare text (clinical, social, scientific, commercial). Each involves the
labeling of complex, multi-word spans that pick out different healthcare
concepts. We also introduce a new labeled dataset for identifying the treatment
relations between drugs and diseases
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