7,580 research outputs found
Label Efficient Learning by Exploiting Multi-class Output Codes
We present a new perspective on the popular multi-class algorithmic
techniques of one-vs-all and error correcting output codes. Rather than
studying the behavior of these techniques for supervised learning, we establish
a connection between the success of these methods and the existence of
label-efficient learning procedures. We show that in both the realizable and
agnostic cases, if output codes are successful at learning from labeled data,
they implicitly assume structure on how the classes are related. By making that
structure explicit, we design learning algorithms to recover the classes with
low label complexity. We provide results for the commonly studied cases of
one-vs-all learning and when the codewords of the classes are well separated.
We additionally consider the more challenging case where the codewords are not
well separated, but satisfy a boundary features condition that captures the
natural intuition that every bit of the codewords should be significant
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field
of medical imaging. While medical imaging datasets have been growing in size, a
challenge for supervised ML algorithms that is frequently mentioned is the lack
of annotated data. As a result, various methods which can learn with less/other
types of supervision, have been proposed. We review semi-supervised, multiple
instance, and transfer learning in medical imaging, both in diagnosis/detection
or segmentation tasks. We also discuss connections between these learning
scenarios, and opportunities for future research.Comment: Submitted to Medical Image Analysi
Text Generation from Knowledge Graphs with Graph Transformers
Generating texts which express complex ideas spanning multiple sentences
requires a structured representation of their content (document plan), but
these representations are prohibitively expensive to manually produce. In this
work, we address the problem of generating coherent multi-sentence texts from
the output of an information extraction system, and in particular a knowledge
graph. Graphical knowledge representations are ubiquitous in computing, but
pose a significant challenge for text generation techniques due to their
non-hierarchical nature, collapsing of long-distance dependencies, and
structural variety. We introduce a novel graph transforming encoder which can
leverage the relational structure of such knowledge graphs without imposing
linearization or hierarchical constraints. Incorporated into an encoder-decoder
setup, we provide an end-to-end trainable system for graph-to-text generation
that we apply to the domain of scientific text. Automatic and human evaluations
show that our technique produces more informative texts which exhibit better
document structure than competitive encoder-decoder methods.Comment: Accepted as a long paper in NAACL 201
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes
In this paper the problem of complex event detection in the continuous domain
(i.e. events with unknown starting and ending locations) is addressed. Existing
event detection methods are limited to features that are extracted from the
local spatial or spatio-temporal patches from the videos. However, this makes
the model vulnerable to the events with similar concepts e.g. "Open drawer" and
"Open cupboard". In this work, in order to address the aforementioned
limitations we present a novel model based on the combination of semantic and
temporal features extracted from video frames. We train a max-margin classifier
on top of the extracted features in an adaptive framework that is able to
detect the events with unknown starting and ending locations. Our model is
based on the Bidirectional Region Neural Network and large margin Structural
Output SVM. The generality of our model allows it to be simply applied to
different labeled and unlabeled datasets. We finally test our algorithm on
three challenging datasets, "UCF 101-Action Recognition", "MPII Cooking
Activities" and "Hollywood", and we report state-of-the-art performance.Comment: submit to journal of Computer Vision and Image Understandin
Context-Aware Query Selection for Active Learning in Event Recognition
Activity recognition is a challenging problem with many practical
applications. In addition to the visual features, recent approaches have
benefited from the use of context, e.g., inter-relationships among the
activities and objects. However, these approaches require data to be labeled,
entirely available beforehand, and not designed to be updated continuously,
which make them unsuitable for surveillance applications. In contrast, we
propose a continuous-learning framework for context-aware activity recognition
from unlabeled video, which has two distinct advantages over existing methods.
First, it employs a novel active-learning technique that not only exploits the
informativeness of the individual activities but also utilizes their contextual
information during query selection; this leads to significant reduction in
expensive manual annotation effort. Second, the learned models can be adapted
online as more data is available. We formulate a conditional random field model
that encodes the context and devise an information-theoretic approach that
utilizes entropy and mutual information of the nodes to compute the set of most
informative queries, which are labeled by a human. These labels are combined
with graphical inference techniques for incremental updates. We provide a
theoretical formulation of the active learning framework with an analytic
solution. Experiments on six challenging datasets demonstrate that our
framework achieves superior performance with significantly less manual
labeling.Comment: To appear in Transactions of Pattern Pattern Analysis and Machine
Intelligence (T-PAMI
Robust Visual Knowledge Transfer via EDA
We address the problem of visual knowledge adaptation by leveraging labeled
patterns from source domain and a very limited number of labeled instances in
target domain to learn a robust classifier for visual categorization. This
paper proposes a new extreme learning machine based cross-domain network
learning framework, that is called Extreme Learning Machine (ELM) based Domain
Adaptation (EDA). It allows us to learn a category transformation and an ELM
classifier with random projection by minimizing the l_(2,1)-norm of the network
output weights and the learning error simultaneously. The unlabeled target
data, as useful knowledge, is also integrated as a fidelity term to guarantee
the stability during cross domain learning. It minimizes the matching error
between the learned classifier and a base classifier, such that many existing
classifiers can be readily incorporated as base classifiers. The network output
weights cannot only be analytically determined, but also transferrable.
Additionally, a manifold regularization with Laplacian graph is incorporated,
such that it is beneficial to semi-supervised learning. Extensively, we also
propose a model of multiple views, referred as MvEDA. Experiments on benchmark
visual datasets for video event recognition and object recognition, demonstrate
that our EDA methods outperform existing cross-domain learning methods.Comment: This paper has been accepted for publication in IEEE Transactions on
Image Processin
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Modeling uncertainty in deep neural networks, despite recent important
advances, is still an open problem. Bayesian neural networks are a powerful
solution, where the prior over network weights is a design choice, often a
normal distribution or other distribution encouraging sparsity. However, this
prior is agnostic to the generative process of the input data, which might lead
to unwarranted generalization for out-of-distribution tested data. We suggest
the presence of a confounder for the relation between the input data and the
discriminative function given the target label. We propose an approach for
modeling this confounder by sharing neural connectivity patterns between the
generative and discriminative networks. This approach leads to a new deep
architecture, where networks are sampled from the posterior of local causal
structures, and coupled into a compact hierarchy. We demonstrate that sampling
networks from this hierarchy, proportionally to their posterior, is efficient
and enables estimating various types of uncertainties. Empirical evaluations of
our method demonstrate significant improvement compared to state-of-the-art
calibration and out-of-distribution detection methods
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
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Labeling training data is one of the most costly bottlenecks in developing
machine learning-based applications. We present a first-of-its-kind study
showing how existing knowledge resources from across an organization can be
used as weak supervision in order to bring development time and cost down by an
order of magnitude, and introduce Snorkel DryBell, a new weak supervision
management system for this setting. Snorkel DryBell builds on the Snorkel
framework, extending it in three critical aspects: flexible, template-based
ingestion of diverse organizational knowledge, cross-feature production
serving, and scalable, sampling-free execution. On three classification tasks
at Google, we find that Snorkel DryBell creates classifiers of comparable
quality to ones trained with tens of thousands of hand-labeled examples,
converts non-servable organizational resources to servable models for an
average 52% performance improvement, and executes over millions of data points
in tens of minutes
Active Learning for Network Intrusion Detection
Network operators are generally aware of common attack vectors that they
defend against. For most networks the vast majority of traffic is legitimate.
However new attack vectors are continually designed and attempted by bad actors
which bypass detection and go unnoticed due to low volume. One strategy for
finding such activity is to look for anomalous behavior. Investigating
anomalous behavior requires significant time and resources. Collecting a large
number of labeled examples for training supervised models is both prohibitively
expensive and subject to obsoletion as new attacks surface. A purely
unsupervised methodology is ideal; however, research has shown that even a very
small number of labeled examples can significantly improve the quality of
anomaly detection. A methodology that minimizes the number of required labels
while maximizing the quality of detection is desirable. False positives in this
context result in wasted effort or blockage of legitimate traffic and false
negatives translate to undetected attacks. We propose a general active learning
framework and experiment with different choices of learners and sampling
strategies
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