pwc-archive/papers-with-abstracts huggingface dataset:
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation
Authors: Edgar Covantes Osuna, Dirk Sudholt
Date: 2018-03-26 00:00:00
Tasks: Diversity

Many real optimisation problems lead to multimodal domains and so require the
identification of multiple optima. Niching methods have been developed to
maintain the population diversity, to investigate many peaks in parallel and to
reduce the effect of genetic drift. Using rigorous runtime analysis, we analyse
for the first time two well known niching methods: probabilistic crowding and
restricted tournament selection (RTS). We incorporate both methods into a
$(\mu+1)~EA$ on the bimodal function Twomax where the goal is to find two
optima at opposite ends of the search space. In probabilistic crowding, the
offspring compete with their parents and the survivor is chosen proportionally
to its fitness. On Twomax probabilistic crowding fails to find any reasonable
solution quality even in exponential time. In RTS the offspring compete against
the closest individual amongst $w$ (window size) individuals. We prove that RTS
fails if $w$ is too small, leading to exponential times with high probability.
However, if w is chosen large enough, it finds both optima for Twomax in time
$O(\mu n \log{n})$ with high probability. Our theoretical results are
accompanied by experimental studies that match the theoretical results and also
shed light on parameters not covered by the theoretical results.
Predicting the Future with Transformational States
Authors: Andrew Jaegle, Oleh Rybkin, Konstantinos G. Derpanis, Kostas Daniilidis
Date: 2018-03-26 00:00:00
Tasks: Decoder

An intelligent observer looks at the world and sees not only what is, but
what is moving and what can be moved. In other words, the observer sees how the
present state of the world can transform in the future. We propose a model that
predicts future images by learning to represent the present state and its
transformation given only a sequence of images. To do so, we introduce an
architecture with a latent state composed of two components designed to capture
(i) the present image state and (ii) the transformation between present and
future states, respectively. We couple this latent state with a recurrent
neural network (RNN) core that predicts future frames by transforming past
states into future states by applying the accumulated state transformation with
a learned operator. We describe how this model can be integrated into an
encoder-decoder convolutional neural network (CNN) architecture that uses
weighted residual connections to integrate representations of the past with
representations of the future. Qualitatively, our approach generates image
sequences that are stable and capture realistic motion over multiple predicted
frames, without requiring adversarial training. Quantitatively, our method
achieves prediction results comparable to state-of-the-art results on standard
image prediction benchmarks (Moving MNIST, KTH, and UCF101).
English verb regularization in books and tweets
Authors: Tyler J. Gray, Andrew J. Reagan, Peter Sheridan Dodds, Christopher M. Danforth
Date: 2018-03-26 00:00:00
Tasks: Translation

The English language has evolved dramatically throughout its lifespan, to the
extent that a modern speaker of Old English would be incomprehensible without
translation. One concrete indicator of this process is the movement from
irregular to regular (-ed) forms for the past tense of verbs. In this study we
quantify the extent of verb regularization using two vastly disparate datasets:
(1) Six years of published books scanned by Google (2003--2008), and (2) A
decade of social media messages posted to Twitter (2008--2017). We find that
the extent of verb regularization is greater on Twitter, taken as a whole, than
in English Fiction books. Regularization is also greater for tweets geotagged
in the United States relative to American English books, but the opposite is
true for tweets geotagged in the United Kingdom relative to British English
books. We also find interesting regional variations in regularization across
counties in the United States. However, once differences in population are
accounted for, we do not identify strong correlations with socio-demographic
variables such as education or income.
DJAM: distributed Jacobi asynchronous method for learning personal models
Authors: Inês Almeida, João Xavier
Date: 2018-03-26 00:00:00

Processing data collected by a network of agents often boils down to solving
an optimization problem. The distributed nature of these problems calls for
methods that are, themselves, distributed. While most collaborative learning
problems require agents to reach a common (or consensus) model, there are
situations in which the consensus solution may not be optimal. For instance,
agents may want to reach a compromise between agreeing with their neighbors and
minimizing a personal loss function. We present DJAM, a Jacobi-like distributed
algorithm for learning personalized models. This method is
implementation-friendly: it has no hyperparameters that need tuning, it is
asynchronous, and its updates only require single-neighbor interactions. We
prove that DJAM converges with probability one to the solution, provided that
the personal loss functions are strongly convex and have Lipschitz gradient. We
then give evidence that DJAM is on par with state-of-the-art methods: our
method reaches a solution with error similar to the error of a carefully tuned
ADMM in about the same number of single-neighbor interactions.
Light Gated Recurrent Units for Speech Recognition
Authors: Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, Yoshua Bengio
Date: 2018-03-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

A field that has directly benefited from the recent advances in deep learning
is Automatic Speech Recognition (ASR). Despite the great achievements of the
past decades, however, a natural and robust human-machine speech interaction
still appears to be out of reach, especially in challenging environments
characterized by significant noise and reverberation. To improve robustness,
modern speech recognizers often employ acoustic models based on Recurrent
Neural Networks (RNNs), that are naturally able to exploit large time contexts
and long-term speech modulations. It is thus of great interest to continue the
study of proper techniques for improving the effectiveness of RNNs in
processing speech signals.
  In this paper, we revise one of the most popular RNN models, namely Gated
Recurrent Units (GRUs), and propose a simplified architecture that turned out
to be very effective for ASR. The contribution of this work is two-fold: First,
we analyze the role played by the reset gate, showing that a significant
redundancy with the update gate occurs. As a result, we propose to remove the
former from the GRU design, leading to a more efficient and compact single-gate
model. Second, we propose to replace hyperbolic tangent with ReLU activations.
This variation couples well with batch normalization and could help the model
learn long-term dependencies without numerical issues.
  Results show that the proposed architecture, called Light GRU (Li-GRU), not
only reduces the per-epoch training time by more than 30% over a standard GRU,
but also consistently improves the recognition accuracy across different tasks,
input features, noisy conditions, as well as across different ASR paradigms,
ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.
Resilient Active Information Gathering with Mobile Robots
Authors: Brent Schlotfeldt, Vasileios Tzoumas, Dinesh Thakur, George J. Pappas
Date: 2018-03-26 00:00:00
Tasks: valid

Applications of safety, security, and rescue in robotics, such as multi-robot
target tracking, involve the execution of information acquisition tasks by
teams of mobile robots. However, in failure-prone or adversarial environments,
robots get attacked, their communication channels get jammed, and their sensors
may fail, resulting in the withdrawal of robots from the collective task, and
consequently the inability of the remaining active robots to coordinate with
each other. As a result, traditional design paradigms become insufficient and,
in contrast, resilient designs against system-wide failures and attacks become
important. In general, resilient design problems are hard, and even though they
often involve objective functions that are monotone or submodular, scalable
approximation algorithms for their solution have been hitherto unknown. In this
paper, we provide the first algorithm, enabling the following capabilities:
minimal communication, i.e., the algorithm is executed by the robots based only
on minimal communication between them; system-wide resiliency, i.e., the
algorithm is valid for any number of denial-of-service attacks and failures;
and provable approximation performance, i.e., the algorithm ensures for all
monotone (and not necessarily submodular) objective functions a solution that
is finitely close to the optimal. We quantify our algorithm's approximation
performance using a notion of curvature for monotone set functions. We support
our theoretical analyses with simulated and real-world experiments, by
considering an active information gathering scenario, namely, multi-robot
target tracking.
CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
Authors: Simon Šuster, Walter Daelemans
Date: 2018-03-26 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Reading Comprehension, Object Tracking, Reading Comprehension

We present a new dataset for machine comprehension in the medical domain. Our
dataset uses clinical case reports with around 100,000 gap-filling queries
about these cases. We apply several baselines and state-of-the-art neural
readers to the dataset, and observe a considerable gap in performance (20% F1)
between the best human and machine readers. We analyze the skills required for
successful answering and show how reader performance varies depending on the
applicable skills. We find that inferences using domain knowledge and object
tracking are the most frequently required skills, and that recognizing omitted
information and spatio-temporal reasoning are the most difficult for the
machines.
On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism
Authors: Edgar Covantes Osuna, Dirk Sudholt
Date: 2018-03-26 00:00:00
Tasks: Diversity

Clearing is a niching method inspired by the principle of assigning the
available resources among a niche to a single individual. The clearing
procedure supplies these resources only to the best individual of each niche:
the winner. So far, its analysis has been focused on experimental approaches
that have shown that clearing is a powerful diversity-preserving mechanism.
Using rigorous runtime analysis to explain how and why it is a powerful method,
we prove that a mutation-based evolutionary algorithm with a large enough
population size, and a phenotypic distance function always succeeds in
optimising all functions of unitation for small niches in polynomial time,
while a genotypic distance function requires exponential time. Finally, we
prove that with phenotypic and genotypic distances clearing is able to find
both optima for Twomax and several general classes of bimodal functions in
polynomial expected time. We use empirical analysis to highlight some of the
characteristics that makes it a useful mechanism and to support the theoretical
results.
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
Authors: David Acuna, Huan Ling, Amlan Kar, Sanja Fidler
Date: 2018-03-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Graph Neural Network, Reinforcement Learning

Manually labeling datasets with object masks is extremely time consuming. In
this work, we follow the idea of Polygon-RNN to produce polygonal annotations
of objects interactively using humans-in-the-loop. We introduce several
important improvements to the model: 1) we design a new CNN encoder
architecture, 2) show how to effectively train the model with Reinforcement
Learning, and 3) significantly increase the output resolution using a Graph
Neural Network, allowing the model to accurately annotate high-resolution
objects in images. Extensive evaluation on the Cityscapes dataset shows that
our model, which we refer to as Polygon-RNN++, significantly outperforms the
original model in both automatic (10% absolute and 16% relative improvement in
mean IoU) and interactive modes (requiring 50% fewer clicks by annotators). We
further analyze the cross-domain scenario in which our model is trained on one
dataset, and used out of the box on datasets from varying domains. The results
show that Polygon-RNN++ exhibits powerful generalization capabilities,
achieving significant improvements over existing pixel-wise methods. Using
simple online fine-tuning we further achieve a high reduction in annotation
time for new datasets, moving a step closer towards an interactive annotation
tool to be used in practice.
Flow From Motion: A Deep Learning Approach
Authors: Cem Eteke, Hayati Havlucu, Nisa İrem Kırbaç, Mehmet Cengiz Onbaşlı, Aykut Coşkun, Terry Eskenazi, Oğuzhan Özcan, Barış Akgün
Date: 2018-03-26 00:00:00
Tasks: Deep Learning

Wearable devices have the potential to enhance sports performance, yet they
are not fulfilling this promise. Our previous studies with 6 professional
tennis coaches and 20 players indicate that this could be due the lack of
psychological or mental state feedback, which the coaches claim to provide.
Towards this end, we propose to detect the flow state, mental state of optimal
performance, using wearables data to be later used in training. We performed a
study with a professional tennis coach and two players. The coach provided
labels about the players' flow state while each player had a wearable device on
their racket holding wrist. We trained multiple models using the wearables data
and the coach labels. Our deep neural network models achieved around 98%
testing accuracy for a variety of conditions. This suggests that the flow state
or what coaches recognize as flow, can be detected using wearables data in
tennis which is a novel result. The implication for the HCI community is that
having access to such information would allow for design of novel hardware and
interaction paradigms that would be helpful in professional athlete training.
Active Reinforcement Learning with Monte-Carlo Tree Search
Authors: Sebastian Schulze, Owain Evans
Date: 2018-03-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Active Reinforcement Learning (ARL) is a twist on RL where the agent observes
reward information only if it pays a cost. This subtle change makes exploration
substantially more challenging. Powerful principles in RL like optimism,
Thompson sampling, and random exploration do not help with ARL. We relate ARL
in tabular environments to Bayes-Adaptive MDPs. We provide an ARL algorithm
using Monte-Carlo Tree Search that is asymptotically Bayes optimal.
Experimentally, this algorithm is near-optimal on small Bandit problems and
MDPs. On larger MDPs it outperforms a Q-learner augmented with specialised
heuristics for ARL. By analysing exploration behaviour in detail, we uncover
obstacles to scaling up simulation-based algorithms for ARL.
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
Authors: Mahdi Rad, Vincent Lepetit
Date: 2017-03-31 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Object Detection, 6D Pose Estimation using RGB, object-detection, Object Detection, Pose Estimation

We introduce a novel method for 3D object detection and pose estimation from
color images only. We first use segmentation to detect the objects of interest
in 2D even in presence of partial occlusions and cluttered background. By
contrast with recent patch-based methods, we rely on a "holistic" approach: We
apply to the detected objects a Convolutional Neural Network (CNN) trained to
predict their 3D poses in the form of 2D projections of the corners of their 3D
bounding boxes. This, however, is not sufficient for handling objects from the
recent T-LESS dataset: These objects exhibit an axis of rotational symmetry,
and the similarity of two images of such an object under two different poses
makes training the CNN challenging. We solve this problem by restricting the
range of poses used for training, and by introducing a classifier to identify
the range of a pose at run-time before estimating it. We also use an optional
additional step that refines the predicted poses. We improve the
state-of-the-art on the LINEMOD dataset from 73.7% to 89.3% of correctly
registered RGB frames. We are also the first to report results on the Occlusion
dataset using color images only. We obtain 54% of frames passing the Pose 6D
criterion on average on several sequences of the T-LESS dataset, compared to
the 67% of the state-of-the-art on the same sequences which uses both color and
depth. The full approach is also scalable, as a single network can be trained
for multiple objects simultaneously.
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
Authors: Mahdi Rad, Markus Oberweger, Vincent Lepetit
Date: 2017-12-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Pose Estimation

We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets.
On the Intrinsic Dimensionality of Image Representations
Authors: Sixue Gong, Vishnu Naresh Boddeti, Anil K. Jain
Date: 2018-03-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: TAR

This paper addresses the following questions pertaining to the intrinsic
dimensionality of any given image representation: (i) estimate its intrinsic
dimensionality, (ii) develop a deep neural network based non-linear mapping,
dubbed DeepMDS, that transforms the ambient representation to the minimal
intrinsic space, and (iii) validate the veracity of the mapping through image
matching in the intrinsic space. Experiments on benchmark image datasets (LFW,
IJB-C and ImageNet-100) reveal that the intrinsic dimensionality of deep neural
network representations is significantly lower than the dimensionality of the
ambient features. For instance, SphereFace's 512-dim face representation and
ResNet's 512-dim image representation have an intrinsic dimensionality of 16
and 19 respectively. Further, the DeepMDS mapping is able to obtain a
representation of significantly lower dimensionality while maintaining
discriminative ability to a large extent, 59.75% TAR @ 0.1% FAR in 16-dim vs
71.26% TAR in 512-dim on IJB-C and a Top-1 accuracy of 77.0% at 19-dim vs 83.4%
at 512-dim on ImageNet-100.
A multilayer backpropagation saliency detection algorithm and its applications
Authors: Chunbiao Zhu, Ge Li
Date: 2018-03-26 00:00:00
Tasks: object-detection, Object Detection, Saliency Detection

Saliency detection is an active topic in the multimedia field. Most previous
works on saliency detection focus on 2D images. However, these methods are not
robust against complex scenes which contain multiple objects or complex
backgrounds. Recently, depth information supplies a powerful cue for saliency
detection. In this paper, we propose a multilayer backpropagation saliency
detection algorithm based on depth mining by which we exploit depth cue from
three different layers of images. The proposed algorithm shows a good
performance and maintains the robustness in complex situations. Experiments'
results show that the proposed framework is superior to other existing saliency
approaches. Besides, we give two innovative applications by this algorithm,
such as scene reconstruction from multiple images and small target object
detection in video.
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits
Authors: Sariya Karimova, Patrick Simianer, Stefan Riezler
Date: 2017-12-13 00:00:00
Tasks: Machine Translation, NMT, Translation

The advantages of neural machine translation (NMT) have been extensively
validated for offline translation of several language pairs for different
domains of spoken and written language. However, research on interactive
learning of NMT by adaptation to human post-edits has so far been confined to
simulation experiments. We present the first user study on online adaptation of
NMT to user post-edits in the domain of patent translation. Our study involves
29 human subjects (translation students) whose post-editing effort and
translation quality were measured on about 4,500 interactions of a human
post-editor and a machine translation system integrating an online adaptive
learning algorithm. Our experimental results show a significant reduction of
human post-editing effort due to online adaptation in NMT according to several
evaluation metrics, including hTER, hBLEU, and KSMR. Furthermore, we found
significant improvements in BLEU/TER between NMT outputs and professional
translations in granted patents, providing further evidence for the advantages
of online adaptive NMT in an interactive setup.