25,022 research outputs found
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
Concept-Oriented Deep Learning: Generative Concept Representations
Generative concept representations have three major advantages over
discriminative ones: they can represent uncertainty, they support integration
of learning and reasoning, and they are good for unsupervised and
semi-supervised learning. We discuss probabilistic and generative deep
learning, which generative concept representations are based on, and the use of
variational autoencoders and generative adversarial networks for learning
generative concept representations, particularly for concepts whose data are
sequences, structured data or graphs
A Probabilistic Generative Grammar for Semantic Parsing
We present a framework that couples the syntax and semantics of natural
language sentences in a generative model, in order to develop a semantic parser
that jointly infers the syntactic, morphological, and semantic representations
of a given sentence under the guidance of background knowledge. To generate a
sentence in our framework, a semantic statement is first sampled from a prior,
such as from a set of beliefs in a knowledge base. Given this semantic
statement, a grammar probabilistically generates the output sentence. A joint
semantic-syntactic parser is derived that returns the -best semantic and
syntactic parses for a given sentence. The semantic prior is flexible, and can
be used to incorporate background knowledge during parsing, in ways unlike
previous semantic parsing approaches. For example, semantic statements
corresponding to beliefs in a knowledge base can be given higher prior
probability, type-correct statements can be given somewhat lower probability,
and beliefs outside the knowledge base can be given lower probability. The
construction of our grammar invokes a novel application of hierarchical
Dirichlet processes (HDPs), which in turn, requires a novel and efficient
inference approach. We present experimental results showing, for a simple
grammar, that our parser outperforms a state-of-the-art CCG semantic parser and
scales to knowledge bases with millions of beliefs.Comment: [manuscript draft
Feature and Variable Selection in Classification
The amount of information in the form of features and variables avail- able
to machine learning algorithms is ever increasing. This can lead to classifiers
that are prone to overfitting in high dimensions, high di- mensional models do
not lend themselves to interpretable results, and the CPU and memory resources
necessary to run on high-dimensional datasets severly limit the applications of
the approaches. Variable and feature selection aim to remedy this by finding a
subset of features that in some way captures the information provided best. In
this paper we present the general methodology and highlight some specific
approaches.Comment: Part of master seminar in document analysis held by Marcus
Eichenberger-Liwick
Machine learning \& artificial intelligence in the quantum domain
Quantum information technologies, and intelligent learning systems, are both
emergent technologies that will likely have a transforming impact on our
society. The respective underlying fields of research -- quantum information
(QI) versus machine learning (ML) and artificial intelligence (AI) -- have
their own specific challenges, which have hitherto been investigated largely
independently. However, in a growing body of recent work, researchers have been
probing the question to what extent these fields can learn and benefit from
each other. QML explores the interaction between quantum computing and ML,
investigating how results and techniques from one field can be used to solve
the problems of the other. Recently, we have witnessed breakthroughs in both
directions of influence. For instance, quantum computing is finding a vital
application in providing speed-ups in ML, critical in our "big data" world.
Conversely, ML already permeates cutting-edge technologies, and may become
instrumental in advanced quantum technologies. Aside from quantum speed-up in
data analysis, or classical ML optimization used in quantum experiments,
quantum enhancements have also been demonstrated for interactive learning,
highlighting the potential of quantum-enhanced learning agents. Finally, works
exploring the use of AI for the very design of quantum experiments, and for
performing parts of genuine research autonomously, have reported their first
successes. Beyond the topics of mutual enhancement, researchers have also
broached the fundamental issue of quantum generalizations of ML/AI concepts.
This deals with questions of the very meaning of learning and intelligence in a
world that is described by quantum mechanics. In this review, we describe the
main ideas, recent developments, and progress in a broad spectrum of research
investigating machine learning and artificial intelligence in the quantum
domain.Comment: Review paper. 106 pages. 16 figure
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
PID control architectures are widely used in industrial applications. Despite
their low number of open parameters, tuning multiple, coupled PID controllers
can become tedious in practice. In this paper, we extend PILCO, a model-based
policy search framework, to automatically tune multivariate PID controllers
purely based on data observed on an otherwise unknown system. The system's
state is extended appropriately to frame the PID policy as a static state
feedback policy. This renders PID tuning possible as the solution of a finite
horizon optimal control problem without further a priori knowledge. The
framework is applied to the task of balancing an inverted pendulum on a seven
degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast
and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International
Conference on Robotics and Automation (ICRA
Probabilistic classifiers with low rank indefinite kernels
Indefinite similarity measures can be frequently found in bio-informatics by
means of alignment scores, but are also common in other fields like shape
measures in image retrieval. Lacking an underlying vector space, the data are
given as pairwise similarities only. The few algorithms available for such data
do not scale to larger datasets. Focusing on probabilistic batch classifiers,
the Indefinite Kernel Fisher Discriminant (iKFD) and the Probabilistic
Classification Vector Machine (PCVM) are both effective algorithms for this
type of data but, with cubic complexity. Here we propose an extension of iKFD
and PCVM such that linear runtime and memory complexity is achieved for low
rank indefinite kernels. Employing the Nystr\"om approximation for indefinite
kernels, we also propose a new almost parameter free approach to identify the
landmarks, restricted to a supervised learning problem. Evaluations at several
larger similarity data from various domains show that the proposed methods
provides similar generalization capabilities while being easier to parametrize
and substantially faster for large scale data
Inducing Probabilistic Grammars by Bayesian Model Merging
We describe a framework for inducing probabilistic grammars from corpora of
positive samples. First, samples are {\em incorporated} by adding ad-hoc rules
to a working grammar; subsequently, elements of the model (such as states or
nonterminals) are {\em merged} to achieve generalization and a more compact
representation. The choice of what to merge and when to stop is governed by the
Bayesian posterior probability of the grammar given the data, which formalizes
a trade-off between a close fit to the data and a default preference for
simpler models (`Occam's Razor'). The general scheme is illustrated using three
types of probabilistic grammars: Hidden Markov models, class-based -grams,
and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second
International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13
page
Robust and Scalable Models of Microbiome Dynamics
Microbes are everywhere, including in and on our bodies, and have been shown
to play key roles in a variety of prevalent human diseases. Consequently, there
has been intense interest in the design of bacteriotherapies or "bugs as
drugs," which are communities of bacteria administered to patients for specific
therapeutic applications. Central to the design of such therapeutics is an
understanding of the causal microbial interaction network and the population
dynamics of the organisms. In this work we present a Bayesian nonparametric
model and associated efficient inference algorithm that addresses the key
conceptual and practical challenges of learning microbial dynamics from time
series microbe abundance data. These challenges include high-dimensional (300+
strains of bacteria in the gut) but temporally sparse and non-uniformly sampled
data; high measurement noise; and, nonlinear and physically non-negative
dynamics. Our contributions include a new type of dynamical systems model for
microbial dynamics based on what we term interaction modules, or learned
clusters of latent variables with redundant interaction structure (reducing the
expected number of interaction coefficients from to );
a fully Bayesian formulation of the stochastic dynamical systems model that
propagates measurement and latent state uncertainty throughout the model; and
introduction of a temporally varying auxiliary variable technique to enable
efficient inference by relaxing the hard non-negativity constraint on states.
We apply our method to simulated and real data, and demonstrate the utility of
our technique for system identification from limited data and gaining new
biological insights into bacteriotherapy design.Comment: ICML 201
Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
Ontologies provide features like a common vocabulary, reusability,
machine-readable content, and also allows for semantic search, facilitate agent
interaction and ordering & structuring of knowledge for the Semantic Web (Web
3.0) application. However, the challenge in ontology engineering is automatic
learning, i.e., the there is still a lack of fully automatic approach from a
text corpus or dataset of various topics to form ontology using machine
learning techniques. In this paper, two topic modeling algorithms are explored,
namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to
determine the statistical relationship between document and terms to build a
topic ontology and ontology graph with minimum human intervention. Experimental
analysis on building a topic ontology and semantic retrieving corresponding
topic ontology for the user's query demonstrating the effectiveness of the
proposed approach
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