12,424 research outputs found
Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning
While machine learning is rapidly being developed and deployed in health
settings such as influenza prediction, there are critical challenges in using
data from one environment in another due to variability in features; even
within disease labels there can be differences (e.g. "fever" may mean something
different reported in a doctor's office versus in an online app). Moreover,
models are often built on passive, observational data which contain different
distributions of population subgroups (e.g. men or women). Thus, there are two
forms of instability between environments in this observational transport
problem. We first harness knowledge from health to conceptualize the underlying
causal structure of this problem in a health outcome prediction task. Based on
sources of stability in the model, we posit that for human-sourced data and
health prediction tasks we can combine environment and population information
in a novel population-aware hierarchical Bayesian domain adaptation framework
that harnesses multiple invariant components through population attributes when
needed. We study the conditions under which invariant learning fails, leading
to reliance on the environment-specific attributes. Experimental results for an
influenza prediction task on four datasets gathered from different contexts
show the model can improve prediction in the case of largely unlabelled target
data from a new environment and different constituent population, by harnessing
both environment and population invariant information. This work represents a
novel, principled way to address a critical challenge by blending domain
(health) knowledge and algorithmic innovation. The proposed approach will have
a significant impact in many social settings wherein who and where the data
comes from matters
Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
The gap between our ability to collect interesting data and our ability to
analyze these data is growing at an unprecedented rate. Recent algorithmic
attempts to fill this gap have employed unsupervised tools to discover
structure in data. Some of the most successful approaches have used
probabilistic models to uncover latent thematic structure in discrete data.
Despite the success of these models on textual data, they have not generalized
as well to image data, in part because of the spatial and temporal structure
that may exist in an image stream.
We introduce a novel unsupervised machine learning framework that
incorporates the ability of convolutional autoencoders to discover features
from images that directly encode spatial information, within a Bayesian
nonparametric topic model that discovers meaningful latent patterns within
discrete data. By using this hybrid framework, we overcome the fundamental
dependency of traditional topic models on rigidly hand-coded data
representations, while simultaneously encoding spatial dependency in our topics
without adding model complexity. We apply this model to the motivating
application of high-level scene understanding and mission summarization for
exploratory marine robots. Our experiments on a seafloor dataset collected by a
marine robot show that the proposed hybrid framework outperforms current
state-of-the-art approaches on the task of unsupervised seafloor terrain
characterization.Comment: 8 page
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based methods to degrade significantly in their
recommendation performance. To address this sparsity problem, auxiliary
information such as item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking this approach which
tightly couples the two components that learn from two different sources of
information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse. To address this
problem, we generalize recent advances in deep learning from i.i.d. input to
non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian
model called collaborative deep learning (CDL), which jointly performs deep
representation learning for the content information and collaborative filtering
for the ratings (feedback) matrix. Extensive experiments on three real-world
datasets from different domains show that CDL can significantly advance the
state of the art
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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