pwc-archive/papers-with-abstracts huggingface dataset:
TACO: Learning Task Decomposition via Temporal Alignment for Control
Authors: Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner
Date: 2018-03-02 00:00:00
Proceeding: ICML 2018 7

Many advanced Learning from Demonstration (LfD) methods consider the
decomposition of complex, real-world tasks into simpler sub-tasks. By reusing
the corresponding sub-policies within and between tasks, they provide training
data for each policy from different high-level tasks and compose them to
perform novel ones. Existing approaches to modular LfD focus either on learning
a single high-level task or depend on domain knowledge and temporal
segmentation. In contrast, we propose a weakly supervised, domain-agnostic
approach based on task sketches, which include only the sequence of sub-tasks
performed in each demonstration. Our approach simultaneously aligns the
sketches with the observed demonstrations and learns the required sub-policies.
This improves generalisation in comparison to separate optimisation procedures.
We evaluate the approach on multiple domains, including a simulated 3D robot
arm control task using purely image-based observations. The results show that
our approach performs commensurately with fully supervised approaches, while
requiring significantly less annotation effort.
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis
Authors: Zi Wang, Dali Wang, Chengcheng Li, Yichi Xu, Husheng Li, Zhirong Bao
Date: 2018-01-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Cell movement in the early phase of C. elegans development is regulated by a
highly complex process in which a set of rules and connections are formulated
at distinct scales. Previous efforts have shown that agent-based, multi-scale
modeling systems can integrate physical and biological rules and provide new
avenues to study developmental systems. However, the application of these
systems to model cell movement is still challenging and requires a
comprehensive understanding of regulation networks at the right scales. Recent
developments in deep learning and reinforcement learning provide an
unprecedented opportunity to explore cell movement using 3D time-lapse images.
We present a deep reinforcement learning approach within an ABM system to
characterize cell movement in C. elegans embryogenesis. Our modeling system
captures the complexity of cell movement patterns in the embryo and overcomes
the local optimization problem encountered by traditional rule-based, ABM that
uses greedy algorithms. We tested our model with two real developmental
processes: the anterior movement of the Cpaaa cell via intercalation and the
rearrangement of the left-right asymmetry. In the first case, model results
showed that Cpaaa's intercalation is an active directional cell movement caused
by the continuous effects from a longer distance, as opposed to a passive
movement caused by neighbor cell movements. This is because the learning-based
simulation found that a passive movement model could not lead Cpaaa to the
predefined destination. In the second case, a leader-follower mechanism well
explained the collective cell movement pattern. These results showed that our
approach to introduce deep reinforcement learning into ABM can test regulatory
mechanisms by exploring cell migration paths in a reverse engineering
perspective. This model opens new doors to explore large datasets generated by
live imaging.
An Overview of Robust Subspace Recovery
Authors: Gilad Lerman, Tyler Maunu
Date: 2018-03-02 00:00:00

This paper will serve as an introduction to the body of work on robust
subspace recovery. Robust subspace recovery involves finding an underlying
low-dimensional subspace in a dataset that is possibly corrupted with outliers.
While this problem is easy to state, it has been difficult to develop optimal
algorithms due to its underlying nonconvexity. This work emphasizes advantages
and disadvantages of proposed approaches and unsolved problems in the area.
DroidStar: Callback Typestates for Android Classes
Authors: Arjun Radhakrishna, Nicholas V. Lewchenko, Shawn Meier, Sergio Mover, Krishna Chaitanya Sripada, Damien Zufferey, Bor-Yuh Evan Chang, Pavol Černý
Date: 2017-01-26 00:00:00
Tasks: Active Learning

Event-driven programming frameworks, such as Android, are based on components
with asynchronous interfaces. The protocols for interacting with these
components can often be described by finite-state machines we dub *callback
typestates*. Callback typestates are akin to classical typestates, with the
difference that their outputs (callbacks) are produced asynchronously. While
useful, these specifications are not commonly available, because writing them
is difficult and error-prone.
  Our goal is to make the task of producing callback typestates significantly
easier. We present a callback typestate assistant tool, DroidStar, that
requires only limited user interaction to produce a callback typestate. Our
approach is based on an active learning algorithm, L*. We improved the
scalability of equivalence queries (a key component of L*), thus making active
learning tractable on the Android system.
  We use DroidStar to learn callback typestates for Android classes both for
cases where one is already provided by the documentation, and for cases where
the documentation is unclear. The results show that DroidStar learns callback
typestates accurately and efficiently. Moreover, in several cases, the
synthesized callback typestates uncovered surprising and undocumented
behaviors.
Label Sanitization against Label Flipping Poisoning Attacks
Authors: Andrea Paudice, Luis Muñoz-González, Emil C. Lupu
Date: 2018-03-02 00:00:00
Tasks: Data Poisoning

Many machine learning systems rely on data collected in the wild from
untrusted sources, exposing the learning algorithms to data poisoning.
Attackers can inject malicious data in the training dataset to subvert the
learning process, compromising the performance of the algorithm producing
errors in a targeted or an indiscriminate way. Label flipping attacks are a
special case of data poisoning, where the attacker can control the labels
assigned to a fraction of the training points. Even if the capabilities of the
attacker are constrained, these attacks have been shown to be effective to
significantly degrade the performance of the system. In this paper we propose
an efficient algorithm to perform optimal label flipping poisoning attacks and
a mechanism to detect and relabel suspicious data points, mitigating the effect
of such poisoning attacks.
Hybrid Model For Word Prediction Using Naive Bayes and Latent Information
Authors: Henrique X. Goulart, Mauro D. L. Tosi, Daniel Soares Gonçalves, Rodrigo F. Maia, Guilherme A. Wachs-Lopes
Date: 2018-03-02 00:00:00
Tasks: Sentence, Sentence Completion

Historically, the Natural Language Processing area has been given too much
attention by many researchers. One of the main motivation beyond this interest
is related to the word prediction problem, which states that given a set words
in a sentence, one can recommend the next word. In literature, this problem is
solved by methods based on syntactic or semantic analysis. Solely, each of
these analysis cannot achieve practical results for end-user applications. For
instance, the Latent Semantic Analysis can handle semantic features of text,
but cannot suggest words considering syntactical rules. On the other hand,
there are models that treat both methods together and achieve state-of-the-art
results, e.g. Deep Learning. These models can demand high computational effort,
which can make the model infeasible for certain types of applications. With the
advance of the technology and mathematical models, it is possible to develop
faster systems with more accuracy. This work proposes a hybrid word suggestion
model, based on Naive Bayes and Latent Semantic Analysis, considering
neighbouring words around unfilled gaps. Results show that this model could
achieve 44.2% of accuracy in the MSR Sentence Completion Challenge.
Baselines and test data for cross-lingual inference
Authors: Željko Agić, Natalie Schluter
Date: 2017-04-18 00:00:00
Proceeding: LREC 2018 5
Tasks: Cross-Lingual Word Embeddings, Machine Translation, Natural Language Inference, Translation, Word Embeddings

The recent years have seen a revival of interest in textual entailment,
sparked by i) the emergence of powerful deep neural network learners for
natural language processing and ii) the timely development of large-scale
evaluation datasets such as SNLI. Recast as natural language inference, the
problem now amounts to detecting the relation between pairs of statements: they
either contradict or entail one another, or they are mutually neutral. Current
research in natural language inference is effectively exclusive to English. In
this paper, we propose to advance the research in SNLI-style natural language
inference toward multilingual evaluation. To that end, we provide test data for
four major languages: Arabic, French, Spanish, and Russian. We experiment with
a set of baselines. Our systems are based on cross-lingual word embeddings and
machine translation. While our best system scores an average accuracy of just
over 75%, we focus largely on enabling further research in multilingual
inference.
Hashing with Mutual Information
Authors: Fatih Cakir, Kun He, Sarah Adel Bargal, Stan Sclaroff
Date: 2018-03-02 00:00:00
Tasks: Image Retrieval, Retrieval, Video Retrieval

Binary vector embeddings enable fast nearest neighbor retrieval in large
databases of high-dimensional objects, and play an important role in many
practical applications, such as image and video retrieval. We study the problem
of learning binary vector embeddings under a supervised setting, also known as
hashing. We propose a novel supervised hashing method based on optimizing an
information-theoretic quantity: mutual information. We show that optimizing
mutual information can reduce ambiguity in the induced neighborhood structure
in the learned Hamming space, which is essential in obtaining high retrieval
performance. To this end, we optimize mutual information in deep neural
networks with minibatch stochastic gradient descent, with a formulation that
maximally and efficiently utilizes available supervision. Experiments on four
image retrieval benchmarks, including ImageNet, confirm the effectiveness of
our method in learning high-quality binary embeddings for nearest neighbor
retrieval.
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
Authors: Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, Michael D. Ernst
Date: 2018-02-25 00:00:00
Proceeding: LREC 2018 5
Tasks: Semantic Parsing

We present new data and semantic parsing methods for the problem of mapping
English sentences to Bash commands (NL2Bash). Our long-term goal is to enable
any user to perform operations such as file manipulation, search, and
application-specific scripting by simply stating their goals in English. We
take a first step in this domain, by providing a new dataset of challenging but
commonly used Bash commands and expert-written English descriptions, along with
baseline methods to establish performance levels on this task.
CINet: A Learning Based Approach to Incremental Context Modeling in Robots
Authors: Fethiye Irmak Doğan, İlker Bozcan, Mehmet Çelik, Sinan Kalkan
Date: 2017-10-13 00:00:00

There have been several attempts at modeling context in robots. However,
either these attempts assume a fixed number of contexts or use a rule-based
approach to determine when to increment the number of contexts. In this paper,
we pose the task of when to increment as a learning problem, which we solve
using a Recurrent Neural Network. We show that the network successfully (with
98\% testing accuracy) learns to predict when to increment, and demonstrate, in
a scene modeling problem (where the correct number of contexts is not known),
that the robot increments the number of contexts in an expected manner (i.e.,
the entropy of the system is reduced). We also present how the incremental
model can be used for various scene reasoning tasks.
Lazifying Conditional Gradient Algorithms
Authors: Gábor Braun, Sebastian Pokutta, Daniel Zink
Date: 2016-10-17 00:00:00
Proceeding: ICML 2017 8

Conditional gradient algorithms (also often called Frank-Wolfe algorithms)
are popular due to their simplicity of only requiring a linear optimization
oracle and more recently they also gained significant traction for online
learning. While simple in principle, in many cases the actual implementation of
the linear optimization oracle is costly. We show a general method to lazify
various conditional gradient algorithms, which in actual computations leads to
several orders of magnitude of speedup in wall-clock time. This is achieved by
using a faster separation oracle instead of a linear optimization oracle,
relying only on few linear optimization oracle calls.
Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
Authors: Felix Berkenkamp, Andreas Krause, Angela P. Schoellig
Date: 2016-02-14 00:00:00
Tasks: Bayesian Optimization

Robotic algorithms typically depend on various parameters, the choice of which significantly affects the robot's performance. While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance. Optimization algorithms, such as Bayesian optimization, have been used to automate this process. However, these methods may evaluate unsafe parameters during the optimization process that lead to safety-critical system failures. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in robotics. For example, high-gain controllers might achieve low average tracking error (performance), but can overshoot and violate input constraints. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.
Stochastic Video Generation with a Learned Prior
Authors: Emily Denton, Rob Fergus
Date: 2018-02-21 00:00:00
Proceeding: ICML 2018 7
Tasks: Video Generation, Video Prediction

Generating video frames that accurately predict future world states is
challenging. Existing approaches either fail to capture the full distribution
of outcomes, or yield blurry generations, or both. In this paper we introduce
an unsupervised video generation model that learns a prior model of uncertainty
in a given environment. Video frames are generated by drawing samples from this
prior and combining them with a deterministic estimate of the future frame. The
approach is simple and easily trained end-to-end on a variety of datasets.
Sample generations are both varied and sharp, even many frames into the future,
and compare favorably to those from existing approaches.
A brain signature highly predictive of future progression to Alzheimer's dementia
Authors: Christian Dansereau, Angela Tam, AmanPreet Badhwar, Sebastian Urchs, Pierre Orban, Pedro Rosa-Neto, Pierre Bellec
Date: 2017-12-21 00:00:00
Tasks: Prognosis

Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment
(MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI
individuals will progress to dementia, even when screened using biomarkers. We
propose here to identify a subset of individuals who share a common brain
signature highly predictive of oncoming dementia. This signature was composed
of brain atrophy and functional dysconnectivity and discovered using a machine
learning model in patients suffering from dementia. The model recognized the
same brain signature in MCI individuals, 90% of which progressed to dementia
within three years. This result is a marked improvement on the state-of-the-art
in prognostic precision, while the brain signature still identified 47% of all
MCI progressors. We thus discovered a sizable MCI subpopulation which
represents an excellent recruitment target for clinical trials at the prodromal
stage of Alzheimer's disease.
Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees Embedded
Authors: Miten Mistry, Dimitrios Letsios, Gerhard Krennrich, Robert M. Lee, Ruth Misener
Date: 2018-03-02 00:00:00
Tasks: Chemical Process, Decision Making

Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process catalyst. We study a large-scale, industrially-relevant mixed-integer nonlinear nonconvex optimization problem involving both gradient-boosted trees and penalty functions mitigating risk. This mixed-integer optimization problem with convex penalty terms broadly applies to optimizing pre-trained regression tree models. Decision makers may wish to optimize discrete models to repurpose legacy predictive models, or they may wish to optimize a discrete model that particularly well-represents a data set. We develop several heuristic methods to find feasible solutions, and an exact, branch-and-bound algorithm leveraging structural properties of the gradient-boosted trees and penalty functions. We computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance.
Tree Species Identification from Bark Images Using Convolutional Neural Networks
Authors: Mathieu Carpentier, Philippe Giguère, Jonathan Gaudreault
Date: 2018-03-02 00:00:00
Tasks: Deep Learning

Tree species identification using bark images is a challenging problem that
could prove useful for many forestry related tasks. However, while the recent
progress in deep learning showed impressive results on standard vision
problems, a lack of datasets prevented its use on tree bark species
classification. In this work, we present, and make publicly available, a novel
dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark
images from 23 different tree species over a wide range of tree diameters. With
it, we demonstrate the feasibility of species recognition through bark images,
using deep learning. More specifically, we obtain an accuracy of 93.88% on
single crop, and an accuracy of 97.81% using a majority voting approach on all
of the images of a tree. We also empirically demonstrate that, for a fixed
number of images, it is better to maximize the number of tree individuals in
the training database, thus directing future data collection efforts.
End-to-end Driving via Conditional Imitation Learning
Authors: Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, Alexey Dosovitskiy
Date: 2017-10-06 00:00:00
Tasks: Imitation Learning

Deep networks trained on demonstrations of human driving have learned to
follow roads and avoid obstacles. However, driving policies trained via
imitation learning cannot be controlled at test time. A vehicle trained
end-to-end to imitate an expert cannot be guided to take a specific turn at an
upcoming intersection. This limits the utility of such systems. We propose to
condition imitation learning on high-level command input. At test time, the
learned driving policy functions as a chauffeur that handles sensorimotor
coordination but continues to respond to navigational commands. We evaluate
different architectures for conditional imitation learning in vision-based
driving. We conduct experiments in realistic three-dimensional simulations of
urban driving and on a 1/5 scale robotic truck that is trained to drive in a
residential area. Both systems drive based on visual input yet remain
responsive to high-level navigational commands. The supplementary video can be
viewed at https://youtu.be/cFtnflNe5fM
Protecting JPEG Images Against Adversarial Attacks
Authors: Aaditya Prakash, Nick Moran, Solomon Garber, Antonella DiLillo, James Storer
Date: 2018-03-02 00:00:00
Tasks: Decoder

As deep neural networks (DNNs) have been integrated into critical systems,
several methods to attack these systems have been developed. These adversarial
attacks make imperceptible modifications to an image that fool DNN classifiers.
We present an adaptive JPEG encoder which defends against many of these
attacks. Experimentally, we show that our method produces images with high
visual quality while greatly reducing the potency of state-of-the-art attacks.
Our algorithm requires only a modest increase in encoding time, produces a
compressed image which can be decompressed by an off-the-shelf JPEG decoder,
and classified by an unmodified classifier
A Deep Incremental Boltzmann Machine for Modeling Context in Robots
Authors: Fethiye Irmak Doğan, Hande Çelikkanat, Sinan Kalkan
Date: 2017-10-13 00:00:00
Tasks: General Classification, Scene Classification

Context is an essential capability for robots that are to be as adaptive as
possible in challenging environments. Although there are many context modeling
efforts, they assume a fixed structure and number of contexts. In this paper,
we propose an incremental deep model that extends Restricted Boltzmann
Machines. Our model gets one scene at a time, and gradually extends the
contextual model when necessary, either by adding a new context or a new
context layer to form a hierarchy. We show on a scene classification benchmark
that our method converges to a good estimate of the contexts of the scenes, and
performs better or on-par on several tasks compared to other incremental models
or non-incremental models.
Distributed Prioritized Experience Replay
Authors: Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver
Date: 2018-03-02 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a distributed architecture for deep reinforcement learning at
scale, that enables agents to learn effectively from orders of magnitude more
data than previously possible. The algorithm decouples acting from learning:
the actors interact with their own instances of the environment by selecting
actions according to a shared neural network, and accumulate the resulting
experience in a shared experience replay memory; the learner replays samples of
experience and updates the neural network. The architecture relies on
prioritized experience replay to focus only on the most significant data
generated by the actors. Our architecture substantially improves the state of
the art on the Arcade Learning Environment, achieving better final performance
in a fraction of the wall-clock training time.