12,528 research outputs found
Declarative Machine Learning - A Classification of Basic Properties and Types
Declarative machine learning (ML) aims at the high-level specification of ML
tasks or algorithms, and automatic generation of optimized execution plans from
these specifications. The fundamental goal is to simplify the usage and/or
development of ML algorithms, which is especially important in the context of
large-scale computations. However, ML systems at different abstraction levels
have emerged over time and accordingly there has been a controversy about the
meaning of this general definition of declarative ML. Specification
alternatives range from ML algorithms expressed in domain-specific languages
(DSLs) with optimization for performance, to ML task (learning problem)
specifications with optimization for performance and accuracy. We argue that
these different types of declarative ML complement each other as they address
different users (data scientists and end users). This paper makes an attempt to
create a taxonomy for declarative ML, including a definition of essential basic
properties and types of declarative ML. Along the way, we provide insights into
implications of these properties. We also use this taxonomy to classify
existing systems. Finally, we draw conclusions on defining appropriate
benchmarks and specification languages for declarative ML
Declarative Data Analytics: a Survey
The area of declarative data analytics explores the application of the
declarative paradigm on data science and machine learning. It proposes
declarative languages for expressing data analysis tasks and develops systems
which optimize programs written in those languages. The execution engine can be
either centralized or distributed, as the declarative paradigm advocates
independence from particular physical implementations. The survey explores a
wide range of declarative data analysis frameworks by examining both the
programming model and the optimization techniques used, in order to provide
conclusions on the current state of the art in the area and identify open
challenges.Comment: 36 pages, 2 figure
Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service
Recently, we have been witnessing huge advancements in the scale of data we
routinely generate and collect in pretty much everything we do, as well as our
ability to exploit modern technologies to process, analyze and understand this
data. The intersection of these trends is what is called, nowadays, as Big Data
Science. Cloud computing represents a practical and cost-effective solution for
supporting Big Data storage, processing and for sophisticated analytics
applications. We analyze in details the building blocks of the software stack
for supporting big data science as a commodity service for data scientists. We
provide various insights about the latest ongoing developments and open
challenges in this domain
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries
The complexity of the visual world creates significant challenges for
comprehensive visual understanding. In spite of recent successes in visual
recognition, today's vision systems would still struggle to deal with visual
queries that require a deeper reasoning. We propose a knowledge base (KB)
framework to handle an assortment of visual queries, without the need to train
new classifiers for new tasks. Building such a large-scale multimodal KB
presents a major challenge of scalability. We cast a large-scale MRF into a KB
representation, incorporating visual, textual and structured data, as well as
their diverse relations. We introduce a scalable knowledge base construction
system that is capable of building a KB with half billion variables and
millions of parameters in a few hours. Our system achieves competitive results
compared to purpose-built models on standard recognition and retrieval tasks,
while exhibiting greater flexibility in answering richer visual queries
Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?
Identifying whether an utterance is a statement, question, greeting, and so
forth is integral to effective automatic understanding of natural dialog.
Little is known, however, about how such dialog acts (DAs) can be automatically
classified in truly natural conversation. This study asks whether current
approaches, which use mainly word information, could be improved by adding
prosodic information. The study is based on more than 1000 conversations from
the Switchboard corpus. DAs were hand-annotated, and prosodic features
(duration, pause, F0, energy, and speaking rate) were automatically extracted
for each DA. In training, decision trees based on these features were inferred;
trees were then applied to unseen test data to evaluate performance.
Performance was evaluated for prosody models alone, and after combining the
prosody models with word information -- either from true words or from the
output of an automatic speech recognizer. For an overall classification task,
as well as three subtasks, prosody made significant contributions to
classification. Feature-specific analyses further revealed that although
canonical features (such as F0 for questions) were important, less obvious
features could compensate if canonical features were removed. Finally, in each
task, integrating the prosodic model with a DA-specific statistical language
model improved performance over that of the language model alone, especially
for the case of recognized words. Results suggest that DAs are redundantly
marked in natural conversation, and that a variety of automatically extractable
prosodic features could aid dialog processing in speech applications.Comment: 55 pages, 10 figure
MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data?
We conduct an empirical study of machine learning functionalities provided by
major cloud service providers, which we call machine learning clouds. Machine
learning clouds hold the promise of hiding all the sophistication of running
large-scale machine learning: Instead of specifying how to run a machine
learning task, users only specify what machine learning task to run and the
cloud figures out the rest. Raising the level of abstraction, however, rarely
comes free - a performance penalty is possible. How good, then, are current
machine learning clouds on real-world machine learning workloads?
We study this question with a focus on binary classication problems. We
present mlbench, a novel benchmark constructed by harvesting datasets from
Kaggle competitions. We then compare the performance of the top winning code
available from Kaggle with that of running machine learning clouds from both
Azure and Amazon on mlbench. Our comparative study reveals the strength and
weakness of existing machine learning clouds and points out potential future
directions for improvement
Harnessing Deep Neural Networks with Logic Rules
Combining deep neural networks with structured logic rules is desirable to
harness flexibility and reduce uninterpretability of the neural models. We
propose a general framework capable of enhancing various types of neural
networks (e.g., CNNs and RNNs) with declarative first-order logic rules.
Specifically, we develop an iterative distillation method that transfers the
structured information of logic rules into the weights of neural networks. We
deploy the framework on a CNN for sentiment analysis, and an RNN for named
entity recognition. With a few highly intuitive rules, we obtain substantial
improvements and achieve state-of-the-art or comparable results to previous
best-performing systems.Comment: Fix typos in appendix. ACL 201
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Machine learning workflow development is a process of trial-and-error:
developers iterate on workflows by testing out small modifications until the
desired accuracy is achieved. Unfortunately, existing machine learning systems
focus narrowly on model training---a small fraction of the overall development
time---and neglect to address iterative development. We propose Helix, a
machine learning system that optimizes the execution across
iterations---intelligently caching and reusing, or recomputing intermediates as
appropriate. Helix captures a wide variety of application needs within its
Scala DSL, with succinct syntax defining unified processes for data
preprocessing, model specification, and learning. We demonstrate that the reuse
problem can be cast as a Max-Flow problem, while the caching problem is
NP-Hard. We develop effective lightweight heuristics for the latter. Empirical
evaluation shows that Helix is not only able to handle a wide variety of use
cases in one unified workflow but also much faster, providing run time
reductions of up to 19x over state-of-the-art systems, such as DeepDive or
KeystoneML, on four real-world applications in natural language processing,
computer vision, social and natural sciences
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
Mapping to Declarative Knowledge for Word Problem Solving
Math word problems form a natural abstraction to a range of quantitative
reasoning problems, such as understanding financial news, sports results, and
casualties of war. Solving such problems requires the understanding of several
mathematical concepts such as dimensional analysis, subset relationships, etc.
In this paper, we develop declarative rules which govern the translation of
natural language description of these concepts to math expressions. We then
present a framework for incorporating such declarative knowledge into word
problem solving. Our method learns to map arithmetic word problem text to math
expressions, by learning to select the relevant declarative knowledge for each
operation of the solution expression. This provides a way to handle multiple
concepts in the same problem while, at the same time, support interpretability
of the answer expression. Our method models the mapping to declarative
knowledge as a latent variable, thus removing the need for expensive
annotations. Experimental evaluation suggests that our domain knowledge based
solver outperforms all other systems, and that it generalizes better in the
realistic case where the training data it is exposed to is biased in a
different way than the test data.Comment: Accepted at TACL 201
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