pwc-archive/papers-with-abstracts huggingface dataset:
Liquid Splash Modeling with Neural Networks
Authors: Kiwon Um, Xiangyu Hu, Nils Thuerey
Date: 2017-04-14 00:00:00
Tasks: regression

This paper proposes a new data-driven approach to model detailed splashes for
liquid simulations with neural networks. Our model learns to generate
small-scale splash detail for the fluid-implicit-particle method using training
data acquired from physically parametrized, high resolution simulations. We use
neural networks to model the regression of splash formation using a classifier
together with a velocity modifier. For the velocity modification, we employ a
heteroscedastic model. We evaluate our method for different spatial scales,
simulation setups, and solvers. Our simulation results demonstrate that our
model significantly improves visual fidelity with a large amount of realistic
droplet formation and yields splash detail much more efficiently than finer
discretizations.
Improved Human Emotion Recognition Using Symmetry of Facial Key Points with Dihedral Group
Authors: Mehdi Ghayoumi, Arvind Bansal
Date: 2017-04-14 00:00:00
Tasks: Emotion Recognition

This article describes how to deploy dihedral group theory to detect Facial
Key Points (FKP) symmetry to recognize emotions. The method can be applied in
many other areas which those have the same data texture.
How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?
Authors: Georg Heigold, Günter Neumann, Josef van Genabith
Date: 2017-04-14 00:00:00
Proceeding: WS 2018 3
Tasks: Machine Translation, Morphological Tagging, Translation, Word Embeddings

This paper investigates the robustness of NLP against perturbed word forms.
While neural approaches can achieve (almost) human-like accuracy for certain
tasks and conditions, they often are sensitive to small changes in the input
such as non-canonical input (e.g., typos). Yet both stability and robustness
are desired properties in applications involving user-generated content, and
the more as humans easily cope with such noisy or adversary conditions. In this
paper, we study the impact of noisy input. We consider different noise
distributions (one type of noise, combination of noise types) and mismatched
noise distributions for training and testing. Moreover, we empirically evaluate
the robustness of different models (convolutional neural networks, recurrent
neural networks, non-neural models), different basic units (characters, byte
pair encoding units), and different NLP tasks (morphological tagging, machine
translation).
Incremental learning of high-level concepts by imitation
Authors: Mina Alibeigi, Majid Nili Ahmadabadi, Babak Nadjar Araabi
Date: 2017-04-14 00:00:00
Tasks: Incremental Learning, Vocal Bursts Intensity Prediction

Nowadays, robots become a companion in everyday life. To be well-accepted by
humans, robots should efficiently understand meanings of their partners'
motions and body language, and respond accordingly. Learning concepts by
imitation brings them this ability in a user-friendly way.
  This paper presents a fast and robust model for Incremental Learning of
Concepts by Imitation (ILoCI). In ILoCI, observed multimodal spatio-temporal
demonstrations are incrementally abstracted and generalized based on both their
perceptual and functional similarities during the imitation. In this method,
perceptually similar demonstrations are abstracted by a dynamic model of mirror
neuron system. An incremental method is proposed to learn their functional
similarities through a limited number of interactions with the teacher.
Learning all concepts together by the proposed memory rehearsal enables robot
to utilize the common structural relations among concepts which not only
expedites the learning process especially at the initial stages, but also
improves the generalization ability and the robustness against discrepancies
between observed demonstrations.
  Performance of ILoCI is assessed using standard LASA handwriting benchmark
data set. The results show efficiency of ILoCI in concept acquisition,
recognition and generation in addition to its robustness against variability in
demonstrations.
DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents
Authors: Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker
Date: 2017-04-14 00:00:00
Proceeding: CVPR 2017 7
Tasks: Future prediction, Multi Future Trajectory Prediction, Prediction, Trajectory Prediction

We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for
the task of future predictions of multiple interacting agents in dynamic
scenes. DESIRE effectively predicts future locations of objects in multiple
scenes by 1) accounting for the multi-modal nature of the future prediction
(i.e., given the same context, future may vary), 2) foreseeing the potential
future outcomes and make a strategic prediction based on that, and 3) reasoning
not only from the past motion history, but also from the scene context as well
as the interactions among the agents. DESIRE achieves these in a single
end-to-end trainable neural network model, while being computationally
efficient. The model first obtains a diverse set of hypothetical future
prediction samples employing a conditional variational autoencoder, which are
ranked and refined by the following RNN scoring-regression module. Samples are
scored by accounting for accumulated future rewards, which enables better
long-term strategic decisions similar to IOC frameworks. An RNN scene context
fusion module jointly captures past motion histories, the semantic scene
context and interactions among multiple agents. A feedback mechanism iterates
over the ranking and refinement to further boost the prediction accuracy. We
evaluate our model on two publicly available datasets: KITTI and Stanford Drone
Dataset. Our experiments show that the proposed model significantly improves
the prediction accuracy compared to other baseline methods.
Enabling Embedded Inference Engine with ARM Compute Library: A Case Study
Authors: Dawei Sun, Shaoshan Liu, Jean-Luc Gaudiot
Date: 2017-04-12 00:00:00
Tasks: Deep Learning

When you need to enable deep learning on low-cost embedded SoCs, is it better
to port an existing deep learning framework or should you build one from
scratch? In this paper, we share our practical experiences of building an
embedded inference engine using ARM Compute Library (ACL). The results show
that, contradictory to conventional wisdoms, for simple models, it takes much
less development time to build an inference engine from scratch compared to
porting existing frameworks. In addition, by utilizing ACL, we managed to build
an inference engine that outperforms TensorFlow by 25%. Our conclusion is that,
on embedded devices, we most likely will use very simple deep learning models
for inference, and with well-developed building blocks such as ACL, it may be
better in both performance and development time to build the engine from
scratch.
Ultrafast photonic reinforcement learning based on laser chaos
Authors: Makoto Naruse, Yuta Terashima, Atsushi Uchida, Song-Ju Kim
Date: 2017-04-14 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning involves decision making in dynamic and uncertain
environments, and constitutes one important element of artificial intelligence
(AI). In this paper, we experimentally demonstrate that the ultrafast chaotic
oscillatory dynamics of lasers efficiently solve the multi-armed bandit problem
(MAB), which requires decision making concerning a class of difficult
trade-offs called the exploration-exploitation dilemma. To solve the MAB, a
certain degree of randomness is required for exploration purposes. However,
pseudo-random numbers generated using conventional electronic circuitry
encounter severe limitations in terms of their data rate and the quality of
randomness due to their algorithmic foundations. We generate laser chaos
signals using a semiconductor laser sampled at a maximum rate of 100 GSample/s,
and combine it with a simple decision-making principle called tug-of-war with a
variable threshold, to ensure ultrafast, adaptive and accurate decision making
at a maximum adaptation speed of 1 GHz. We found that decision-making
performance was maximized with an optimal sampling interval, and we highlight
the exact coincidence between the negative autocorrelation inherent in laser
chaos and decision-making performance. This study paves the way for a new realm
of ultrafast photonics in the age of AI, where the ultrahigh bandwidth of
photons can provide new value.
Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
Authors: Abdelhamid Benaini, Achraf Berrajaa, Jaouad Boukachour, Mustapha Oudani
Date: 2017-04-14 00:00:00
Tasks: GPU

A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to
solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses
binary and integer encoding and genetic operators adapted to this problem. Our
GA is improved by generated initial solution with hubs located at middle nodes.
The obtained experimental results are compared with the best known solutions on
all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own
randomly generated instances up to 6000 nodes. Our approach outperforms most
well-known heuristics in terms of solution quality and time execution and it
allows hitherto unsolved problems to be solved.
Runtime Analysis of the $(1+(λ,λ))$ Genetic Algorithm on Random Satisfiable 3-CNF Formulas
Authors: Maxim Buzdalov, Benjamin Doerr
Date: 2017-04-14 00:00:00
Tasks: Evolutionary Algorithms

The $(1+(\lambda,\lambda))$ genetic algorithm, first proposed at GECCO 2013,
showed a surprisingly good performance on so me optimization problems. The
theoretical analysis so far was restricted to the OneMax test function, where
this GA profited from the perfect fitness-distance correlation. In this work,
we conduct a rigorous runtime analysis of this GA on random 3-SAT instances in
the planted solution model having at least logarithmic average degree, which
are known to have a weaker fitness distance correlation.
  We prove that this GA with fixed not too large population size again obtains
runtimes better than $\Theta(n \log n)$, which is a lower bound for most
evolutionary algorithms on pseudo-Boolean problems with unique optimum.
However, the self-adjusting version of the GA risks reaching population sizes
at which the intermediate selection of the GA, due to the weaker
fitness-distance correlation, is not able to distinguish a profitable offspring
from others. We show that this problem can be overcome by equipping the
self-adjusting GA with an upper limit for the population size. Apart from
sparse instances, this limit can be chosen in a way that the asymptotic
performance does not worsen compared to the idealistic OneMax case. Overall,
this work shows that the $(1+(\lambda,\lambda))$ GA can provably have a good
performance on combinatorial search and optimization problems also in the
presence of a weaker fitness-distance correlation.
Camera Calibration by Global Constraints on the Motion of Silhouettes
Authors: Gil Ben-Artzi
Date: 2017-04-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Camera Calibration

We address the problem of epipolar geometry using the motion of silhouettes.
Such methods match epipolar lines or frontier points across views, which are
then used as the set of putative correspondences. We introduce an approach that
improves by two orders of magnitude the performance over state-of-the-art
methods, by significantly reducing the number of outliers in the putative
matching. We model the frontier points' correspondence problem as constrained
flow optimization, requiring small differences between their coordinates over
consecutive frames. Our approach is formulated as a Linear Integer Program and
we show that due to the nature of our problem, it can be solved efficiently in
an iterative manner. Our method was validated on four standard datasets
providing accurate calibrations across very different viewpoints.
Environment-Independent Task Specifications via GLTL
Authors: Michael L. Littman, Ufuk Topcu, Jie Fu, Charles Isbell, Min Wen, James Macglashan
Date: 2017-04-14 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new task-specification language for Markov decision processes
that is designed to be an improvement over reward functions by being
environment independent. The language is a variant of Linear Temporal Logic
(LTL) that is extended to probabilistic specifications in a way that permits
approximations to be learned in finite time. We provide several small
environments that demonstrate the advantages of our geometric LTL (GLTL)
language and illustrate how it can be used to specify standard
reinforcement-learning tasks straightforwardly.
An entity-driven recursive neural network model for chinese discourse coherence modeling
Authors: Fan Xu, Shujing Du, Maoxi Li, Mingwen Wang
Date: 2017-04-14 00:00:00
Tasks: Coherence Evaluation, Feature Engineering, Machine Translation, Sentence, Sentence Ordering, Translation

Chinese discourse coherence modeling remains a challenge taskin Natural
Language Processing field.Existing approaches mostlyfocus on the need for
feature engineering, whichadoptthe sophisticated features to capture the logic
or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present an entity-drivenrecursive deep modelfor the Chinese discourse
coherence evaluation based on current English discourse coherenceneural network
model. Specifically, to overcome the shortage of identifying the entity(nouns)
overlap across sentences in the currentmodel, Our combined modelsuccessfully
investigatesthe entities information into the recursive neural network
freamework.Evaluation results on both sentence ordering and machine translation
coherence rating task show the effectiveness of the proposed model, which
significantly outperforms the existing strong baseline.
Cross-media Similarity Metric Learning with Unified Deep Networks
Authors: Jinwei Qi, Xin Huang, Yuxin Peng
Date: 2017-04-14 00:00:00
Tasks: Metric Learning, Representation Learning, Retrieval, Semantic Similarity, Semantic Textual Similarity, Triplet

As a highlighting research topic in the multimedia area, cross-media
retrieval aims to capture the complex correlations among multiple media types.
Learning better shared representation and distance metric for multimedia data
is important to boost the cross-media retrieval. Motivated by the strong
ability of deep neural network in feature representation and comparison
functions learning, we propose the Unified Network for Cross-media Similarity
Metric (UNCSM) to associate cross-media shared representation learning with
distance metric in a unified framework. First, we design a two-pathway deep
network pretrained with contrastive loss, and employ double triplet similarity
loss for fine-tuning to learn the shared representation for each media type by
modeling the relative semantic similarity. Second, the metric network is
designed for effectively calculating the cross-media similarity of the shared
representation, by modeling the pairwise similar and dissimilar constraints.
Compared to the existing methods which mostly ignore the dissimilar constraints
and only use sample distance metric as Euclidean distance separately, our UNCSM
approach unifies the representation learning and distance metric to preserve
the relative similarity as well as embrace more complex similarity functions
for further improving the cross-media retrieval accuracy. The experimental
results show that our UNCSM approach outperforms 8 state-of-the-art methods on
4 widely-used cross-media datasets.
Deep API Programmer: Learning to Program with APIs
Authors: Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, Pushmeet Kohli
Date: 2017-04-14 00:00:00

We present DAPIP, a Programming-By-Example system that learns to program with
APIs to perform data transformation tasks. We design a domain-specific language
(DSL) that allows for arbitrary concatenations of API outputs and constant
strings. The DSL consists of three family of APIs: regular expression-based
APIs, lookup APIs, and transformation APIs. We then present a novel neural
synthesis algorithm to search for programs in the DSL that are consistent with
a given set of examples. The search algorithm uses recently introduced neural
architectures to encode input-output examples and to model the program search
in the DSL. We show that synthesis algorithm outperforms baseline methods for
synthesizing programs on both synthetic and real-world benchmarks.
Dataset Augmentation for Pose and Lighting Invariant Face Recognition
Authors: Daniel Crispell, Octavian Biris, Nate Crosswhite, Jeffrey Byrne, Joseph L. Mundy
Date: 2017-04-14 00:00:00
Tasks: Face Recognition

The performance of modern face recognition systems is a function of the
dataset on which they are trained. Most datasets are largely biased toward
"near-frontal" views with benign lighting conditions, negatively effecting
recognition performance on images that do not meet these criteria. The proposed
approach demonstrates how a baseline training set can be augmented to increase
pose and lighting variability using semi-synthetic images with simulated pose
and lighting conditions. The semi-synthetic images are generated using a fast
and robust 3-d shape estimation and rendering pipeline which includes the full
head and background. Various methods of incorporating the semi-synthetic
renderings into the training procedure of a state of the art deep neural
network-based recognition system without modifying the structure of the network
itself are investigated. Quantitative results are presented on the challenging
IJB-A identification dataset using a state of the art recognition pipeline as a
baseline.
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Authors: Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh
Date: 2016-11-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: 2D Human Pose Estimation, 2D Pose Estimation, Keypoint Detection, Multi-Person Pose Estimation, Object Detection, Pose Estimation

We present an approach to efficiently detect the 2D pose of multiple people
in an image. The approach uses a nonparametric representation, which we refer
to as Part Affinity Fields (PAFs), to learn to associate body parts with
individuals in the image. The architecture encodes global context, allowing a
greedy bottom-up parsing step that maintains high accuracy while achieving
realtime performance, irrespective of the number of people in the image. The
architecture is designed to jointly learn part locations and their association
via two branches of the same sequential prediction process. Our method placed
first in the inaugural COCO 2016 keypoints challenge, and significantly exceeds
the previous state-of-the-art result on the MPII Multi-Person benchmark, both
in performance and efficiency.
FastVentricle: Cardiac Segmentation with ENet
Authors: Jesse Lieman-Sifry, Matthieu Le, Felix Lau, Sean Sall, Daniel Golden
Date: 2017-04-13 00:00:00
Tasks: Cardiac Segmentation, Segmentation

Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contouring ventricular structures. Automatic contouring can lower the
required time per patient by generating contour suggestions that can be lightly
modified by the annotator. Fully convolutional networks (FCNs), a variant of
convolutional neural networks, have been used to rapidly advance the
state-of-the-art in automated segmentation, which makes FCNs a natural choice
for ventricular segmentation. However, FCNs are limited by their computational
cost, which increases the monetary cost and degrades the user experience of
production systems. To combat this shortcoming, we have developed the
FastVentricle architecture, an FCN architecture for ventricular segmentation
based on the recently developed ENet architecture. FastVentricle is 4x faster
and runs with 6x less memory than the previous state-of-the-art ventricular
segmentation architecture while still maintaining excellent clinical accuracy.
Convolutional neural network architecture for geometric matching
Authors: Ignacio Rocco, Relja Arandjelović, Josef Sivic
Date: 2017-03-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Geometric Matching, parameter estimation

We address the problem of determining correspondences between two images in
agreement with a geometric model such as an affine or thin-plate spline
transformation, and estimating its parameters. The contributions of this work
are three-fold. First, we propose a convolutional neural network architecture
for geometric matching. The architecture is based on three main components that
mimic the standard steps of feature extraction, matching and simultaneous
inlier detection and model parameter estimation, while being trainable
end-to-end. Second, we demonstrate that the network parameters can be trained
from synthetically generated imagery without the need for manual annotation and
that our matching layer significantly increases generalization capabilities to
never seen before images. Finally, we show that the same model can perform both
instance-level and category-level matching giving state-of-the-art results on
the challenging Proposal Flow dataset.
Applying High-Resolution Visible Imagery to Satellite Melt Pond Fraction Retrieval: A Neural Network Approach
Authors: Qi Liu, Yawen Zhang, Qin Lv, Li Shang
Date: 2017-04-13 00:00:00
Tasks: Retrieval

During summer, melt ponds have a significant influence on Arctic sea-ice
albedo. The melt pond fraction (MPF) also has the ability to forecast the
Arctic sea-ice in a certain period. It is important to retrieve accurate melt
pond fraction (MPF) from satellite data for Arctic research. This paper
proposes a satellite MPF retrieval model based on the multi-layer neural
network, named MPF-NN. Our model uses multi-spectral satellite data as model
input and MPF information from multi-site and multi-period visible imagery as
prior knowledge for modeling. It can effectively model melt ponds evolution of
different regions and periods over the Arctic. Evaluation results show that the
MPF retrieved from MODIS data using the proposed model has an RMSE of 3.91% and
a correlation coefficient of 0.73. The seasonal distribution of MPF is also
consistent with previous results.