1,059 research outputs found
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
Reinforcement learning approaches have long appealed to the data management
community due to their ability to learn to control dynamic behavior from raw
system performance. Recent successes in combining deep neural networks with
reinforcement learning have sparked significant new interest in this domain.
However, practical solutions remain elusive due to large training data
requirements, algorithmic instability, and lack of standard tools. In this
work, we introduce LIFT, an end-to-end software stack for applying deep
reinforcement learning to data management tasks. While prior work has
frequently explored applications in simulations, LIFT centers on utilizing
human expertise to learn from demonstrations, thus lowering online training
times. We further introduce TensorForce, a TensorFlow library for applied deep
reinforcement learning exposing a unified declarative interface to common RL
algorithms, thus providing a backend to LIFT. We demonstrate the utility of
LIFT in two case studies in database compound indexing and resource management
in stream processing. Results show LIFT controllers initialized from
demonstrations can outperform human baselines and heuristics across latency
metrics and space usage by up to 70%
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler
Domain specific accelerators present new challenges and opportunities for
code generation onto novel instruction sets, communication fabrics, and memory
architectures.
In this paper we introduce an intermediate representation (IR) which enables
both deep learning computational kernels and hardware capabilities to be
described in the same IR. We then formulate and apply instruction mapping to
determine the possible ways a computation can be performed on a hardware
system. Next, our scheduler chooses a specific mapping and determines the data
movement and computation order. In order to manage the large search space of
mappings and schedules, we developed a flexible framework that allows
heuristics, cost models, and potentially machine learning to facilitate this
search problem.
With this system, we demonstrate the automated extraction of matrix
multiplication kernels out of recent deep learning kernels such as
depthwise-separable convolution. In addition, we demonstrate two to five times
better performance on DeepBench sized GEMMs and GRU RNN execution when compared
to state-of-the-art (SOTA) implementations on new hardware and up to 85% of the
performance for SOTA implementations on existing hardware
Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection
Cardiac auscultation involves expert interpretation of abnormalities in heart
sounds using stethoscope. Deep learning based cardiac auscultation is of
significant interest to the healthcare community as it can help reducing the
burden of manual auscultation with automated detection of abnormal heartbeats.
However, the problem of automatic cardiac auscultation is complicated due to
the requirement of reliability and high accuracy, and due to the presence of
background noise in the heartbeat sound. In this work, we propose a Recurrent
Neural Networks (RNNs) based automated cardiac auscultation solution. Our
choice of RNNs is motivated by the great success of deep learning in medical
applications and by the observation that RNNs represent the deep learning
configuration most suitable for dealing with sequential or temporal data even
in the presence of noise. We explore the use of various RNN models, and
demonstrate that these models deliver the abnormal heartbeat classification
score with significant improvement. Our proposed approach using RNNs can be
potentially be used for real-time abnormal heartbeat detection in the Internet
of Medical Things for remote monitoring applications
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
Machine Learning for Networking: Workflow, Advances and Opportunities
Recently, machine learning has been used in every possible field to leverage
its amazing power. For a long time, the net-working and distributed computing
system is the key infrastructure to provide efficient computational resource
for machine learning. Networking itself can also benefit from this promising
technology. This article focuses on the application of Machine Learning
techniques for Networking (MLN), which can not only help solve the intractable
old network questions but also stimulate new network applications. In this
article, we summarize the basic workflow to explain how to apply the machine
learning technology in the networking domain. Then we provide a selective
survey of the latest representative advances with explanations on their design
principles and benefits. These advances are divided into several network design
objectives and the detailed information of how they perform in each step of MLN
workflow is presented. Finally, we shed light on the new opportunities on
networking design and community building of this new inter-discipline. Our goal
is to provide a broad research guideline on networking with machine learning to
help and motivate researchers to develop innovative algorithms, standards and
frameworks.Comment: 8 pages, 2 figure
Yum-me: A Personalized Nutrient-based Meal Recommender System
Nutrient-based meal recommendations have the potential to help individuals
prevent or manage conditions such as diabetes and obesity. However, learning
people's food preferences and making recommendations that simultaneously appeal
to their palate and satisfy nutritional expectations are challenging. Existing
approaches either only learn high-level preferences or require a prolonged
learning period. We propose Yum-me, a personalized nutrient-based meal
recommender system designed to meet individuals' nutritional expectations,
dietary restrictions, and fine-grained food preferences. Yum-me enables a
simple and accurate food preference profiling procedure via a visual quiz-based
user interface, and projects the learned profile into the domain of
nutritionally appropriate food options to find ones that will appeal to the
user. We present the design and implementation of Yum-me, and further describe
and evaluate two innovative contributions. The first contriution is an open
source state-of-the-art food image analysis model, named FoodDist. We
demonstrate FoodDist's superior performance through careful benchmarking and
discuss its applicability across a wide array of dietary applications. The
second contribution is a novel online learning framework that learns food
preference from item-wise and pairwise image comparisons. We evaluate the
framework in a field study of 227 anonymous users and demonstrate that it
outperforms other baselines by a significant margin. We further conducted an
end-to-end validation of the feasibility and effectiveness of Yum-me through a
60-person user study, in which Yum-me improves the recommendation acceptance
rate by 42.63%
Lale: Consistent Automated Machine Learning
Automated machine learning makes it easier for data scientists to develop
pipelines by searching over possible choices for hyperparameters, algorithms,
and even pipeline topologies. Unfortunately, the syntax for automated machine
learning tools is inconsistent with manual machine learning, with each other,
and with error checks. Furthermore, few tools support advanced features such as
topology search or higher-order operators. This paper introduces Lale, a
library of high-level Python interfaces that simplifies and unifies automated
machine learning in a consistent way.Comment: KDD Workshop on Automation in Machine Learning (AutoML@KDD), August
202
Construction and Quality Evaluation of Heterogeneous Hierarchical Topic Models
In our work, we propose to represent HTM as a set of flat models, or layers,
and a set of topical hierarchies, or edges. We suggest several quality measures
for edges of hierarchical models, resembling those proposed for flat models. We
conduct an assessment experimentation and show strong correlation between the
proposed measures and human judgement on topical edge quality. We also
introduce heterogeneous algorithm to build hierarchical topic models for
heterogeneous data sources. We show how making certain adjustments to learning
process helps to retain original structure of customized models while allowing
for slight coherent modifications for new documents. We evaluate this approach
using the proposed measures and show that the proposed heterogeneous algorithm
significantly outperforms the baseline concat approach. Finally, we implement
our own ESE called Rysearch, which demonstrates the potential of ARTM approach
for visualizing large heterogeneous document collections
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
A number of popular systems, most notably Google's TensorFlow, have been
implemented from the ground up to support machine learning tasks. We consider
how to make a very small set of changes to a modern relational database
management system (RDBMS) to make it suitable for distributed learning
computations. Changes include adding better support for recursion, and
optimization and execution of very large compute plans. We also show that there
are key advantages to using an RDBMS as a machine learning platform. In
particular, learning based on a database management system allows for trivial
scaling to large data sets and especially large models, where different
computational units operate on different parts of a model that may be too large
to fit into RAM
A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays
This paper introduces a paradigm of smartphone application based disease
diagnostics that may completely revolutionise the way healthcare services are
being provided. Although primarily aimed to assist the problems in rendering
the healthcare services during the coronavirus pandemic, the model can also be
extended to identify the exact disease that the patient is caught with from a
broad spectrum of pulmonary diseases. The app inputs Chest X-Ray images
captured from the mobile camera which is then relayed to the AI architecture in
a cloud platform, and diagnoses the disease with state of the art accuracy.
Doctors with a smartphone can leverage the application to save the considerable
time that standard COVID-19 tests take for preliminary diagnosis. The scarcity
of training data and class imbalance issues were effectively tackled in our
approach by the use of Data Augmentation Generative Adversarial Network (DAGAN)
and model architecture based as a Convolutional Siamese Network with attention
mechanism. The backend model was tested for robustness us-ing publicly
available datasets under two different classification
scenarios(Binary/Multiclass) with minimal and noisy data. The model achieved
pinnacle testing accuracy of 99.30% and 98.40% on the two respective scenarios,
making it completely reliable for its users. On top of that a semi-live
training scenario was introduced, which helps improve the app performance over
time as data accumulates. Overall, the problems of generalisability of complex
models and data inefficiency is tackled through the model architecture. The app
based setting with semi live training helps in ease of access to reliable
healthcare in the society, as well as help ineffective research of rare
diseases in a minimal data setting
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