3,851 research outputs found
Exploring Connections Between Active Learning and Model Extraction
Machine learning is being increasingly used by individuals, research
institutions, and corporations. This has resulted in the surge of Machine
Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and
resources to learn the model, and (b) a user-friendly query interface to access
the model. However, such MLaaS systems raise privacy concerns such as model
extraction. In model extraction attacks, adversaries maliciously exploit the
query interface to steal the model. More precisely, in a model extraction
attack, a good approximation of a sensitive or proprietary model held by the
server is extracted (i.e. learned) by a dishonest user who interacts with the
server only via the query interface. This attack was introduced by Tramer et
al. at the 2016 USENIX Security Symposium, where practical attacks for various
models were shown. We believe that better understanding the efficacy of model
extraction attacks is paramount to designing secure MLaaS systems. To that end,
we take the first step by (a) formalizing model extraction and discussing
possible defense strategies, and (b) drawing parallels between model extraction
and established area of active learning. In particular, we show that recent
advancements in the active learning domain can be used to implement powerful
model extraction attacks, and investigate possible defense strategies
Long and Short Memory Balancing in Visual Co-Tracking using Q-Learning
Employing one or more additional classifiers to break the self-learning loop
in tracing-by-detection has gained considerable attention. Most of such
trackers merely utilize the redundancy to address the accumulating label error
in the tracking loop, and suffer from high computational complexity as well as
tracking challenges that may interrupt all classifiers (e.g. temporal
occlusions). We propose the active co-tracking framework, in which the main
classifier of the tracker labels samples of the video sequence, and only
consults auxiliary classifier when it is uncertain. Based on the source of the
uncertainty and the differences of two classifiers (e.g. accuracy, speed,
update frequency, etc.), different policies should be taken to exchange the
information between two classifiers. Here, we introduce a reinforcement
learning approach to find the appropriate policy by considering the state of
the tracker in a specific sequence. The proposed method yields promising
results in comparison to the best tracking-by-detection approaches.Comment: Submitted to ICIP 201
Rapid Adaptation with Conditionally Shifted Neurons
We describe a mechanism by which artificial neural networks can learn rapid
adaptation - the ability to adapt on the fly, with little data, to new tasks -
that we call conditionally shifted neurons. We apply this mechanism in the
framework of metalearning, where the aim is to replicate some of the
flexibility of human learning in machines. Conditionally shifted neurons modify
their activation values with task-specific shifts retrieved from a memory
module, which is populated rapidly based on limited task experience. On
metalearning benchmarks from the vision and language domains, models augmented
with conditionally shifted neurons achieve state-of-the-art results.Comment: ICML 2018; Added: additional ablation and speed comparison with
MetaNe
A New Ensemble Learning Framework for 3D Biomedical Image Segmentation
3D image segmentation plays an important role in biomedical image analysis.
Many 2D and 3D deep learning models have achieved state-of-the-art segmentation
performance on 3D biomedical image datasets. Yet, 2D and 3D models have their
own strengths and weaknesses, and by unifying them together, one may be able to
achieve more accurate results. In this paper, we propose a new ensemble
learning framework for 3D biomedical image segmentation that combines the
merits of 2D and 3D models. First, we develop a fully convolutional network
based meta-learner to learn how to improve the results from 2D and 3D models
(base-learners). Then, to minimize over-fitting for our sophisticated
meta-learner, we devise a new training method that uses the results of the
base-learners as multiple versions of "ground truths". Furthermore, since our
new meta-learner training scheme does not depend on manual annotation, it can
utilize abundant unlabeled 3D image data to further improve the model.
Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset
and the mouse piriform cortex dataset) show that our approach is effective
under fully-supervised, semi-supervised, and transductive settings, and attains
superior performance over state-of-the-art image segmentation methods.Comment: To appear in AAAI-2019. The first three authors contributed equally
to the pape
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Active learning holds promise of significantly reducing data annotation costs
while maintaining reasonable model performance. However, it requires sending
data to annotators for labeling. This presents a possible privacy leak when the
training set includes sensitive user data. In this paper, we describe an
approach for carrying out privacy preserving active learning with quantifiable
guarantees. We evaluate our approach by showing the tradeoff between privacy,
utility and annotation budget on a binary classification task in a active
learning setting.Comment: To appear at PAL: Privacy-Enhancing Artificial Intelligence and
Language Technologies as part of the AAAI Spring Symposium Series (AAAI-SSS
2019
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
Active learning (AL) aims to enable training high performance classifiers
with low annotation cost by predicting which subset of unlabelled instances
would be most beneficial to label. The importance of AL has motivated extensive
research, proposing a wide variety of manually designed AL algorithms with
diverse theoretical and intuitive motivations. In contrast to this body of
research, we propose to treat active learning algorithm design as a
meta-learning problem and learn the best criterion from data. We model an
active learning algorithm as a deep neural network that inputs the base learner
state and the unlabelled point set and predicts the best point to annotate
next. Training this active query policy network with reinforcement learning,
produces the best non-myopic policy for a given dataset. The key challenge in
achieving a general solution to AL then becomes that of learner generalisation,
particularly across heterogeneous datasets. We propose a multi-task
dataset-embedding approach that allows dataset-agnostic active learners to be
trained. Our evaluation shows that AL algorithms trained in this way can
directly generalise across diverse problems
Learning and Optimization with Submodular Functions
In many naturally occurring optimization problems one needs to ensure that
the definition of the optimization problem lends itself to solutions that are
tractable to compute. In cases where exact solutions cannot be computed
tractably, it is beneficial to have strong guarantees on the tractable
approximate solutions. In order operate under these criterion most optimization
problems are cast under the umbrella of convexity or submodularity. In this
report we will study design and optimization over a common class of functions
called submodular functions. Set functions, and specifically submodular set
functions, characterize a wide variety of naturally occurring optimization
problems, and the property of submodularity of set functions has deep
theoretical consequences with wide ranging applications. Informally, the
property of submodularity of set functions concerns the intuitive "principle of
diminishing returns. This property states that adding an element to a smaller
set has more value than adding it to a larger set. Common examples of
submodular monotone functions are entropies, concave functions of cardinality,
and matroid rank functions; non-monotone examples include graph cuts, network
flows, and mutual information.
In this paper we will review the formal definition of submodularity; the
optimization of submodular functions, both maximization and minimization; and
finally discuss some applications in relation to learning and reasoning using
submodular functions.Comment: Tech Report - USC Computer Science CS-599, Convex and Combinatorial
Optimizatio
Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
Deep neural networks are highly effective when a large number of labeled
samples are available but fail with few-shot classification tasks. Recently,
meta-learning methods have received much attention, which train a meta-learner
on massive additional tasks to gain the knowledge to instruct the few-shot
classification. Usually, the training tasks are randomly sampled and performed
indiscriminately, often making the meta-learner stuck into a bad local optimum.
Some works in the optimization of deep neural networks have shown that a better
arrangement of training data can make the classifier converge faster and
perform better. Inspired by this idea, we propose an easy-to-hard expert
meta-training strategy to arrange the training tasks properly, where easy tasks
are preferred in the first phase, then, hard tasks are emphasized in the second
phase. A task hardness aware module is designed and integrated into the
training procedure to estimate the hardness of a task based on the
distinguishability of its categories. In addition, we explore multiple hardness
measurements including the semantic relation, the pairwise Euclidean distance,
the Hausdorff distance, and the Hilbert-Schmidt independence criterion.
Experimental results on the miniImageNet and tieredImageNetSketch datasets show
that the meta-learners can obtain better results with our expert training
strategy.Comment: 9 pages, 6 figure
Learning to Optimize
Algorithm design is a laborious process and often requires many iterations of
ideation and validation. In this paper, we explore automating algorithm design
and present a method to learn an optimization algorithm, which we believe to be
the first method that can automatically discover a better algorithm. We
approach this problem from a reinforcement learning perspective and represent
any particular optimization algorithm as a policy. We learn an optimization
algorithm using guided policy search and demonstrate that the resulting
algorithm outperforms existing hand-engineered algorithms in terms of
convergence speed and/or the final objective value.Comment: 9 pages, 3 figure
Multi-Task Learning for Argumentation Mining
Multi-task learning has recently become a very active field in deep learning
research. In contrast to learning a single task in isolation, multiple tasks
are learned at the same time, thereby utilizing the training signal of related
tasks to improve the performance on the respective machine learning tasks.
Related work shows various successes in different domains when applying this
paradigm and this thesis extends the existing empirical results by evaluating
multi-task learning in four different scenarios: argumentation mining,
epistemic segmentation, argumentation component segmentation, and
grapheme-to-phoneme conversion. We show that multi-task learning can, indeed,
improve the performance compared to single-task learning in all these
scenarios, but may also hurt the performance. Therefore, we investigate the
reasons for successful and less successful applications of this paradigm and
find that dataset properties such as entropy or the size of the label inventory
are good indicators for a potential multi-task learning success and that
multi-task learning is particularly useful if the task at hand suffers from
data sparsity, i.e. a lack of training data. Moreover, multi-task learning is
particularly effective for long input sequences in our experiments. We have
observed this trend in all evaluated scenarios. Finally, we develop a highly
configurable and extensible sequence tagging framework which supports
multi-task learning to conduct our empirical experiments and to aid future
research regarding the multi-task learning paradigm and natural language
processing.Comment: Thesis for the M. Sc. Internet and Webbased Systems degree at
Technische Universit\"at Darmstadt (Germany
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