pwc-archive/papers-with-abstracts huggingface dataset:
Semantic embeddings for program behavior patterns
Authors: Alexander Chistyakov, Ekaterina Lobacheva, Arseny Kuznetsov, Alexey Romanenko
Date: 2018-04-10 00:00:00

In this paper, we propose a new feature extraction technique for program
execution logs. First, we automatically extract complex patterns from a
program's behavior graph. Then, we embed these patterns into a continuous space
by training an autoencoder. We evaluate the proposed features on a real-world
malicious software detection task. We also find that the embedding space
captures interpretable structures in the space of pattern parts.
Probabilistic Prediction of Vehicle Semantic Intention and Motion
Authors: Yeping Hu, Wei Zhan, Masayoshi Tomizuka
Date: 2018-04-10 00:00:00
Tasks: Autonomous Vehicles, motion prediction, Prediction

Accurately predicting the possible behaviors of traffic participants is an
essential capability for future autonomous vehicles. The majority of current
researches fix the number of driving intentions by considering only a specific
scenario. However, distinct driving environments usually contain various
possible driving maneuvers. Therefore, a intention prediction method that can
adapt to different traffic scenarios is needed. To further improve the overall
vehicle prediction performance, motion information is usually incorporated with
classified intentions. As suggested in some literature, the methods that
directly predict possible goal locations can achieve better performance for
long-term motion prediction than other approaches due to their automatic
incorporation of environment constraints. Moreover, by obtaining the temporal
information of the predicted destinations, the optimal trajectories for
predicted vehicles as well as the desirable path for ego autonomous vehicle
could be easily generated. In this paper, we propose a Semantic-based Intention
and Motion Prediction (SIMP) method, which can be adapted to any driving
scenarios by using semantic-defined vehicle behaviors. It utilizes a
probabilistic framework based on deep neural network to estimate the
intentions, final locations, and the corresponding time information for
surrounding vehicles. An exemplar real-world scenario was used to implement and
examine the proposed method.
Nonparametric Preference Completion
Authors: Julian Katz-Samuels, Clayton Scott
Date: 2017-05-24 00:00:00

We consider the task of collaborative preference completion: given a pool of
items, a pool of users and a partially observed item-user rating matrix, the
goal is to recover the \emph{personalized ranking} of each user over all of the
items. Our approach is nonparametric: we assume that each item $i$ and each
user $u$ have unobserved features $x_i$ and $y_u$, and that the associated
rating is given by $g_u(f(x_i,y_u))$ where $f$ is Lipschitz and $g_u$ is a
monotonic transformation that depends on the user. We propose a $k$-nearest
neighbors-like algorithm and prove that it is consistent. To the best of our
knowledge, this is the first consistency result for the collaborative
preference completion problem in a nonparametric setting. Finally, we
demonstrate the performance of our algorithm with experiments on the Netflix
and Movielens datasets.
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Authors: Chih-Ting Liu, Yi-Heng Wu, Yu-Sheng Lin, Shao-Yi Chien
Date: 2017-05-30 00:00:00
Tasks: Super-Resolution

Deep Convolutional Neural Networks (CNNs) are widely employed in modern
computer vision algorithms, where the input image is convolved iteratively by
many kernels to extract the knowledge behind it. However, with the depth of
convolutional layers getting deeper and deeper in recent years, the enormous
computational complexity makes it difficult to be deployed on embedded systems
with limited hardware resources. In this paper, we propose two
computation-performance optimization methods to reduce the redundant
convolution kernels of a CNN with performance and architecture constraints, and
apply it to a network for super resolution (SR). Using PSNR drop compared to
the original network as the performance criterion, our method can get the
optimal PSNR under a certain computation budget constraint. On the other hand,
our method is also capable of minimizing the computation required under a given
PSNR drop.
Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation
Authors: Ariel Ephrat, Inbar Mosseri, Oran Lang, Tali Dekel, Kevin Wilson, Avinatan Hassidim, William T. Freeman, Michael Rubinstein
Date: 2018-04-10 00:00:00
Tasks: Speech Separation

We present a joint audio-visual model for isolating a single speech signal
from a mixture of sounds such as other speakers and background noise. Solving
this task using only audio as input is extremely challenging and does not
provide an association of the separated speech signals with speakers in the
video. In this paper, we present a deep network-based model that incorporates
both visual and auditory signals to solve this task. The visual features are
used to "focus" the audio on desired speakers in a scene and to improve the
speech separation quality. To train our joint audio-visual model, we introduce
AVSpeech, a new dataset comprised of thousands of hours of video segments from
the Web. We demonstrate the applicability of our method to classic speech
separation tasks, as well as real-world scenarios involving heated interviews,
noisy bars, and screaming children, only requiring the user to specify the face
of the person in the video whose speech they want to isolate. Our method shows
clear advantage over state-of-the-art audio-only speech separation in cases of
mixed speech. In addition, our model, which is speaker-independent (trained
once, applicable to any speaker), produces better results than recent
audio-visual speech separation methods that are speaker-dependent (require
training a separate model for each speaker of interest).
Sentiment Transfer using Seq2Seq Adversarial Autoencoders
Authors: Ayush Singh, Ritu Palod
Date: 2018-04-10 00:00:00
Tasks: Style Transfer

Expressing in language is subjective. Everyone has a different style of
reading and writing, apparently it all boil downs to the way their mind
understands things (in a specific format). Language style transfer is a way to
preserve the meaning of a text and change the way it is expressed. Progress in
language style transfer is lagged behind other domains, such as computer
vision, mainly because of the lack of parallel data, use cases, and reliable
evaluation metrics. In response to the challenge of lacking parallel data, we
explore learning style transfer from non-parallel data. We propose a model
combining seq2seq, autoencoders, and adversarial loss to achieve this goal. The
key idea behind the proposed models is to learn separate content
representations and style representations using adversarial networks.
Considering the problem of evaluating style transfer tasks, we frame the
problem as sentiment transfer and evaluation using a sentiment classifier to
calculate how many sentiments was the model able to transfer. We report our
results on several kinds of models.
Robust Physical-World Attacks on Deep Learning Models
Authors: Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, Dawn Song
Date: 2017-07-27 00:00:00
Tasks: Deep Learning

Recent studies show that the state-of-the-art deep neural networks (DNNs) are
vulnerable to adversarial examples, resulting from small-magnitude
perturbations added to the input. Given that that emerging physical systems are
using DNNs in safety-critical situations, adversarial examples could mislead
these systems and cause dangerous situations.Therefore, understanding
adversarial examples in the physical world is an important step towards
developing resilient learning algorithms. We propose a general attack
algorithm,Robust Physical Perturbations (RP2), to generate robust visual
adversarial perturbations under different physical conditions. Using the
real-world case of road sign classification, we show that adversarial examples
generated using RP2 achieve high targeted misclassification rates against
standard-architecture road sign classifiers in the physical world under various
environmental conditions, including viewpoints. Due to the current lack of a
standardized testing method, we propose a two-stage evaluation methodology for
robust physical adversarial examples consisting of lab and field tests. Using
this methodology, we evaluate the efficacy of physical adversarial
manipulations on real objects. Witha perturbation in the form of only black and
white stickers,we attack a real stop sign, causing targeted misclassification
in 100% of the images obtained in lab settings, and in 84.8%of the captured
video frames obtained on a moving vehicle(field test) for the target
classifier.
Subsampled Optimization: Statistical Guarantees, Mean Squared Error Approximation, and Sampling Method
Authors: Rong Zhu, Jiming Jiang
Date: 2018-04-10 00:00:00

For optimization on large-scale data, exactly calculating its solution may be
computationally difficulty because of the large size of the data. In this paper
we consider subsampled optimization for fast approximating the exact solution.
In this approach, one gets a surrogate dataset by sampling from the full data,
and then obtains an approximate solution by solving the subsampled optimization
based on the surrogate. One main theoretical contributions are to provide the
asymptotic properties of the approximate solution with respect to the exact
solution as statistical guarantees, and to rigorously derive an accurate
approximation of the mean squared error (MSE) and an approximately unbiased MSE
estimator. These results help us better diagnose the subsampled optimization in
the context that a confidence region on the exact solution is provided using
the approximate solution. The other consequence of our results is to propose an
optimal sampling method, Hessian-based sampling, whose probabilities are
proportional to the norms of Newton directions. Numerical experiments with
least-squares and logistic regression show promising performance, in line with
our results.
Binary Space Partitioning as Intrinsic Reward
Authors: Wojciech Skaba
Date: 2018-04-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An autonomous agent embodied in a humanoid robot, in order to learn from the
overwhelming flow of raw and noisy sensory, has to effectively reduce the high
spatial-temporal data dimensionality. In this paper we propose a novel method
of unsupervised feature extraction and selection with binary space
partitioning, followed by a computation of information gain that is interpreted
as intrinsic reward, then applied as immediate-reward signal for the
reinforcement-learning. The space partitioning is executed by tiny codelets
running on a simulated Turing Machine. The features are represented by concept
nodes arranged in a hierarchy, in which those of a lower level become the input
vectors of a higher level.
Imagine This! Scripts to Compositions to Videos
Authors: Tanmay Gupta, Dustin Schwenk, Ali Farhadi, Derek Hoiem, Aniruddha Kembhavi
Date: 2018-04-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Retrieval, World Knowledge

Imagining a scene described in natural language with realistic layout and
appearance of entities is the ultimate test of spatial, visual, and semantic
world knowledge. Towards this goal, we present the Composition, Retrieval, and
Fusion Network (CRAFT), a model capable of learning this knowledge from
video-caption data and applying it while generating videos from novel captions.
CRAFT explicitly predicts a temporal-layout of mentioned entities (characters
and objects), retrieves spatio-temporal entity segments from a video database
and fuses them to generate scene videos. Our contributions include sequential
training of components of CRAFT while jointly modeling layout and appearances,
and losses that encourage learning compositional representations for retrieval.
We evaluate CRAFT on semantic fidelity to caption, composition consistency, and
visual quality. CRAFT outperforms direct pixel generation approaches and
generalizes well to unseen captions and to unseen video databases with no text
annotations. We demonstrate CRAFT on FLINTSTONES, a new richly annotated
video-caption dataset with over 25000 videos. For a glimpse of videos generated
by CRAFT, see https://youtu.be/688Vv86n0z8.
Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification
Authors: Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
Date: 2017-11-30 00:00:00
Tasks: Classification, General Classification, LIDAR Semantic Segmentation, Segmentation

This paper introduces a new Urban Point Cloud Dataset for Automatic
Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
describe how the dataset is obtained from acquisition to post-processing and
labeling. This dataset can be used to learn classification algorithm, however,
given that a great attention has been paid to the split between the different
objects, this dataset can also be used to learn the segmentation. The dataset
consists of around 2km of MLS point cloud acquired in two cities. The number of
points and range of classes make us consider that it can be used to train
Deep-Learning methods. Besides we show some results of automatic segmentation
and classification. The dataset is available at:
http://caor-mines-paristech.fr/fr/paris-lille-3d-dataset/
Understanding disentangling in $β$-VAE
Authors: Christopher P. Burgess, Irina Higgins, Arka Pal, Loic Matthey, Nick Watters, Guillaume Desjardins, Alexander Lerchner
Date: 2018-04-10 00:00:00

We present new intuitions and theoretical assessments of the emergence of
disentangled representation in variational autoencoders. Taking a
rate-distortion theory perspective, we show the circumstances under which
representations aligned with the underlying generative factors of variation of
data emerge when optimising the modified ELBO bound in $\beta$-VAE, as training
progresses. From these insights, we propose a modification to the training
regime of $\beta$-VAE, that progressively increases the information capacity of
the latent code during training. This modification facilitates the robust
learning of disentangled representations in $\beta$-VAE, without the previous
trade-off in reconstruction accuracy.
A Robust Genetic Algorithm for Learning Temporal Specifications from Data
Authors: Laura Nenzi, Simone Silvetti, Ezio Bartocci, Luca Bortolussi
Date: 2017-11-13 00:00:00

We consider the problem of mining signal temporal logical requirements from a
dataset of regular (good) and anomalous (bad) trajectories of a dynamical
system. We assume the training set to be labeled by human experts and that we
have access only to a limited amount of data, typically noisy. We provide a
systematic approach to synthesize both the syntactical structure and the
parameters of the temporal logic formula using a two-steps procedure: first, we
leverage a novel evolutionary algorithm for learning the structure of the
formula; second, we perform the parameter synthesis operating on the
statistical emulation of the average robustness for a candidate formula w.r.t.
its parameters. We compare our results with our previous work [{BufoBSBLB14]
and with a recently proposed decision-tree [bombara_decision_2016] based
method. We present experimental results on two case studies: an anomalous
trajectory detection problem of a naval surveillance system and the
characterization of an Ineffective Respiratory effort, showing the usefulness
of our work.
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
Authors: Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
Date: 2018-04-10 00:00:00
Tasks: compressed sensing, Medical Image Analysis, MRI Reconstruction

In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory
can accelerate imaging by sampling fewer measurements within each contrast. The
conventional optimization-based models suffer several limitations: strict
assumption of shared sparse support, time-consuming optimization and "shallow"
models with difficulties in encoding the rich patterns hiding in massive MRI
data. In this paper, we propose the first deep learning model for
multi-contrast MRI reconstruction. We achieve information sharing through
feature sharing units, which significantly reduces the number of parameters.
The feature sharing unit is combined with a data fidelity unit to comprise an
inference block. These inference blocks are cascaded with dense connections,
which allows for information transmission across different depths of the
network efficiently. Our extensive experiments on various multi-contrast MRI
datasets show that proposed model outperforms both state-of-the-art
single-contrast and multi-contrast MRI methods in accuracy and efficiency. We
show the improved reconstruction quality can bring great benefits for the later
medical image analysis stage. Furthermore, the robustness of the proposed model
to the non-registration environment shows its potential in real MRI
applications.
Inductive Representation Learning on Large Graphs
Authors: William L. Hamilton, Rex Ying, Jure Leskovec
Date: 2017-06-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Graph Classification, Graph Regression, Link Prediction, Link Property Prediction, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs, Node Property Prediction, Representation Learning

Low-dimensional embeddings of nodes in large graphs have proved extremely
useful in a variety of prediction tasks, from content recommendation to
identifying protein functions. However, most existing approaches require that
all nodes in the graph are present during training of the embeddings; these
previous approaches are inherently transductive and do not naturally generalize
to unseen nodes. Here we present GraphSAGE, a general, inductive framework that
leverages node feature information (e.g., text attributes) to efficiently
generate node embeddings for previously unseen data. Instead of training
individual embeddings for each node, we learn a function that generates
embeddings by sampling and aggregating features from a node's local
neighborhood. Our algorithm outperforms strong baselines on three inductive
node-classification benchmarks: we classify the category of unseen nodes in
evolving information graphs based on citation and Reddit post data, and we show
that our algorithm generalizes to completely unseen graphs using a multi-graph
dataset of protein-protein interactions.
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions
Authors: Ali el Hassouni, Mark Hoogendoorn, Martijn van Otterlo, A. E. Eiben, Vesa Muhonen, Eduardo Barbaro
Date: 2018-04-10 00:00:00
Tasks: Clustering, Dynamic Time Warping, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Personalization is very powerful in improving the effectiveness of health interventions. Reinforcement learning (RL) algorithms are suitable for learning these tailored interventions from sequential data collected about individuals. However, learning can be very fragile. The time to learn intervention policies is limited as disengagement from the user can occur quickly. Also, in e-Health intervention timing can be crucial before the optimal window passes. We present an approach that learns tailored personalization policies for groups of users by combining RL and clustering. The benefits are two-fold: speeding up the learning to prevent disengagement while maintaining a high level of personalization. Our clustering approach utilizes dynamic time warping to compare user trajectories consisting of states and rewards. We apply online and batch RL to learn policies over clusters of individuals and introduce our self-developed and publicly available simulator for e-Health interventions to evaluate our approach. We compare our methods with an e-Health intervention benchmark. We demonstrate that batch learning outperforms online learning for our setting. Furthermore, our proposed clustering approach for RL finds near-optimal clusterings which lead to significantly better policies in terms of cumulative reward compared to learning a policy per individual or learning one non-personalized policy across all individuals. Our findings also indicate that the learned policies accurately learn to send interventions at the right moments and that the users workout more and at the right times of the day.
Representation Learning on Graphs: Methods and Applications
Authors: William L. Hamilton, Rex Ying, Jure Leskovec
Date: 2017-09-17 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Drug Design, Representation Learning

Machine learning on graphs is an important and ubiquitous task with
applications ranging from drug design to friendship recommendation in social
networks. The primary challenge in this domain is finding a way to represent,
or encode, graph structure so that it can be easily exploited by machine
learning models. Traditionally, machine learning approaches relied on
user-defined heuristics to extract features encoding structural information
about a graph (e.g., degree statistics or kernel functions). However, recent
years have seen a surge in approaches that automatically learn to encode graph
structure into low-dimensional embeddings, using techniques based on deep
learning and nonlinear dimensionality reduction. Here we provide a conceptual
review of key advancements in this area of representation learning on graphs,
including matrix factorization-based methods, random-walk based algorithms, and
graph neural networks. We review methods to embed individual nodes as well as
approaches to embed entire (sub)graphs. In doing so, we develop a unified
framework to describe these recent approaches, and we highlight a number of
important applications and directions for future work.
Geometrical analysis of polynomial lens distortion models
Authors: José I. Ronda, Antonio Valdés
Date: 2018-04-10 00:00:00

Polynomial functions are a usual choice to model the nonlinearity of lenses.
Typically, these models are obtained through physical analysis of the lens
system or on purely empirical grounds. The aim of this work is to facilitate an
alternative approach to the selection or design of these models based on
establishing a priori the desired geometrical properties of the distortion
functions. With this purpose we obtain all the possible isotropic linear models
and also those that are formed by functions with symmetry with respect to some
axis. In this way, the classical models (decentering, thin prism distortion)
are found to be particular instances of the family of models found by geometric
considerations. These results allow to find generalizations of the most usually
employed models while preserving the desired geometrical properties. Our
results also provide a better understanding of the geometric properties of the
models employed in the most usual computer vision software libraries.
Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
Authors: Xavier Roynard, Jean-Emmanuel Deschaud, François Goulette
Date: 2018-04-10 00:00:00
Tasks: Classification, General Classification, Position, Semantic Segmentation

In this article we describe a new convolutional neural network (CNN) to
classify 3D point clouds of urban or indoor scenes. Solutions are given to the
problems encountered working on scene point clouds, and a network is described
that allows for point classification using only the position of points in a
multi-scale neighborhood.
  On the reduced-8 Semantic3D benchmark [Hackel et al., 2017], this network,
ranked second, beats the state of the art of point classification methods
(those not using a regularization step).