pwc-archive/papers-with-abstracts huggingface dataset:
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
Authors: Yaodong Yang, Lantao Yu, Yiwei Bai, Jun Wang, Wei-Nan Zhang, Ying Wen, Yong Yu
Date: 2017-09-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.
Computing the Shattering Coefficient of Supervised Learning Algorithms
Authors: Rodrigo Fernandes de Mello, Moacir Antonelli Ponti, Carlos Henrique Grossi Ferreira
Date: 2018-05-07 00:00:00
Tasks: Learning Theory

The Statistical Learning Theory (SLT) provides the theoretical guarantees for
supervised machine learning based on the Empirical Risk Minimization Principle
(ERMP). Such principle defines an upper bound to ensure the uniform convergence
of the empirical risk Remp(f), i.e., the error measured on a given data sample,
to the expected value of risk R(f) (a.k.a. actual risk), which depends on the
Joint Probability Distribution P(X x Y) mapping input examples x in X to class
labels y in Y. The uniform convergence is only ensured when the Shattering
coefficient N(F,2n) has a polynomial growing behavior. This paper proves the
Shattering coefficient for any Hilbert space H containing the input space X and
discusses its effects in terms of learning guarantees for supervised machine
algorithms.
Optimal and Myopic Information Acquisition
Authors: Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
Date: 2017-03-18 00:00:00

We consider the problem of optimal dynamic information acquisition from many
correlated information sources. Each period, the decision-maker jointly takes
an action and allocates a fixed number of observations across the available
sources. His payoff depends on the actions taken and on an unknown state. In
the canonical setting of jointly normal information sources, we show that the
optimal dynamic information acquisition rule proceeds myopically after finitely
many periods. If signals are acquired in large blocks each period, then the
optimal rule turns out to be myopic from period 1. These results demonstrate
the possibility of robust and "simple" optimal information acquisition, and
simplify the analysis of dynamic information acquisition in a widely used
informational environment.
Domain Adaptation with Adversarial Training and Graph Embeddings
Authors: Firoj Alam, Shafiq Joty, Muhammad Imran
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Domain Adaptation

The success of deep neural networks (DNNs) is heavily dependent on the
availability of labeled data. However, obtaining labeled data is a big
challenge in many real-world problems. In such scenarios, a DNN model can
leverage labeled and unlabeled data from a related domain, but it has to deal
with the shift in data distributions between the source and the target domains.
In this paper, we study the problem of classifying social media posts during a
crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data
from past similar events (e.g., Flood) and unlabeled data for the current
event. We propose a novel model that performs adversarial learning based domain
adaptation to deal with distribution drifts and graph based semi-supervised
learning to leverage unlabeled data within a single unified deep learning
framework. Our experiments with two real-world crisis datasets collected from
Twitter demonstrate significant improvements over several baselines.
Infrared and visible image fusion using Latent Low-Rank Representation
Authors: Hui Li, Xiao-Jun Wu
Date: 2018-04-24 00:00:00
Tasks: Infrared And Visible Image Fusion

Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. Firstly, the source images are decomposed into low-rank parts(global structure) and salient parts(local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information. Then, the salient parts are simply fused by sum strategy which is a efficient operation in this fusion framework. Finally, the fused image is obtained by combining the fused low-rank part and the fused salient part. Compared with other fusion methods experimentally, the proposed method has better fusion performance than state-of-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at https://github.com/hli1221/imagefusion\_Infrared\_visible\_latlrr
Model selection with lasso-zero: adding straw to the haystack to better find needles
Authors: Pascaline Descloux, Sylvain Sardy
Date: 2018-05-14 00:00:00
Tasks: Model Selection

The high-dimensional linear model $y = X \beta^0 + \epsilon$ is considered
and the focus is put on the problem of recovering the support $S^0$ of the
sparse vector $\beta^0.$ We introduce Lasso-Zero, a new $\ell_1$-based
estimator whose novelty resides in an "overfit, then threshold" paradigm and
the use of noise dictionaries concatenated to $X$ for overfitting the response.
To select the threshold, we employ the quantile universal threshold based on a
pivotal statistic that requires neither knowledge nor preliminary estimation of
the noise level. Numerical simulations show that Lasso-Zero performs well in
terms of support recovery and provides an excellent trade-off between high true
positive rate and low false discovery rate compared to competitors. Our
methodology is supported by theoretical results showing that when no noise
dictionary is used, Lasso-Zero recovers the signs of $\beta^0$ under weaker
conditions on $X$ and $S^0$ than the Lasso and achieves sign consistency for
correlated Gaussian designs. The use of noise dictionary improves the procedure
for low signals.
Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection
Authors: Chunbiao Zhu, Wen-Hao Zhang, Thomas H. Li, Ge Li
Date: 2018-05-14 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

Saliency detection aims to detect the most attractive objects in images and
is widely used as a foundation for various applications. In this paper, we
propose a novel salient object detection algorithm for RGB-D images using
center-dark channel priors. First, we generate an initial saliency map based on
a color saliency map and a depth saliency map of a given RGB-D image. Then, we
generate a center-dark channel map based on center saliency and dark channel
priors. Finally, we fuse the initial saliency map with the center dark channel
map to generate the final saliency map. Extensive evaluations over four
benchmark datasets demonstrate that our proposed method performs favorably
against most of the state-of-the-art approaches. Besides, we further discuss
the application of the proposed algorithm in small target detection and
demonstrate the universal value of center-dark channel priors in the field of
object detection.
Controlling the privacy loss with the input feature maps of the layers in convolutional neural networks
Authors: Woohyung Chun, Sung-Min Hong, Junho Huh, Inyup Kang
Date: 2018-05-09 00:00:00

We propose the method to sanitize the privacy of the IFM(Input Feature Map)s
that are fed into the layers of CNN(Convolutional Neural Network)s. The method
introduces the degree of the sanitization that makes the application using a
CNN be able to control the privacy loss represented as the ratio of the
probabilistic accuracies for original IFM and sanitized IFM. For the
sanitization of an IFM, the sample-and-hold based approximation scheme is
devised to satisfy an application-specific degree of the sanitization. The
scheme approximates an IFM by replacing all the samples in a window with the
non-zero sample closest to the mean of the sampling window. It also removes the
dependency on CNN configuration by unfolding multi-dimensional IFM tensors into
one-dimensional streams to be approximated.
Black-box Variational Inference for Stochastic Differential Equations
Authors: Thomas Ryder, Andrew Golightly, A. Stephen McGough, Dennis Prangle
Date: 2018-02-09 00:00:00
Proceeding: ICML 2018 7
Tasks: Variational Inference

Parameter inference for stochastic differential equations is challenging due
to the presence of a latent diffusion process. Working with an Euler-Maruyama
discretisation for the diffusion, we use variational inference to jointly learn
the parameters and the diffusion paths. We use a standard mean-field
variational approximation of the parameter posterior, and introduce a recurrent
neural network to approximate the posterior for the diffusion paths conditional
on the parameters. This neural network learns how to provide Gaussian state
transitions which bridge between observations in a very similar way to the
conditioned diffusion process. The resulting black-box inference method can be
applied to any SDE system with light tuning requirements. We illustrate the
method on a Lotka-Volterra system and an epidemic model, producing accurate
parameter estimates in a few hours.
Hu-Fu: Hardware and Software Collaborative Attack Framework against Neural Networks
Authors: Wenshuo Li, Jincheng Yu, Xuefei Ning, Pengjun Wang, Qi Wei, Yu Wang, Huazhong Yang
Date: 2018-05-14 00:00:00
Tasks: Autonomous Driving, Cloud Computing, Face Recognition, General Classification, Human Detection, image-classification, Image Classification, Image Segmentation, Semantic Segmentation

Recently, Deep Learning (DL), especially Convolutional Neural Network (CNN),
develops rapidly and is applied to many tasks, such as image classification,
face recognition, image segmentation, and human detection. Due to its superior
performance, DL-based models have a wide range of application in many areas,
some of which are extremely safety-critical, e.g. intelligent surveillance and
autonomous driving. Due to the latency and privacy problem of cloud computing,
embedded accelerators are popular in these safety-critical areas. However, the
robustness of the embedded DL system might be harmed by inserting
hardware/software Trojans into the accelerator and the neural network model,
since the accelerator and deploy tool (or neural network model) are usually
provided by third-party companies. Fortunately, inserting hardware Trojans can
only achieve inflexible attack, which means that hardware Trojans can easily
break down the whole system or exchange two outputs, but can't make CNN
recognize unknown pictures as targets. Though inserting software Trojans has
more freedom of attack, it often requires tampering input images, which is not
easy for attackers. So, in this paper, we propose a hardware-software
collaborative attack framework to inject hidden neural network Trojans, which
works as a back-door without requiring manipulating input images and is
flexible for different scenarios. We test our attack framework for image
classification and face recognition tasks, and get attack success rate of 92.6%
and 100% on CIFAR10 and YouTube Faces, respectively, while keeping almost the
same accuracy as the unattacked model in the normal mode. In addition, we show
a specific attack scenario in which a face recognition system is attacked and
gives a specific wrong answer.
Bianet: A Parallel News Corpus in Turkish, Kurdish and English
Authors: Duygu Ataman
Date: 2018-05-14 00:00:00
Tasks: Articles, Machine Translation, Translation

We present a new open-source parallel corpus consisting of news articles
collected from the Bianet magazine, an online newspaper that publishes Turkish
news, often along with their translations in English and Kurdish. In this
paper, we describe the collection process of the corpus and its statistical
properties. We validate the benefit of using the Bianet corpus by evaluating
bilingual and multilingual neural machine translation models in English-Turkish
and English-Kurdish directions.
The Spot the Difference corpus: a multi-modal corpus of spontaneous task oriented spoken interactions
Authors: José Lopes, Nils Hemmingsson, Oliver Åstrand
Date: 2018-05-14 00:00:00
Proceeding: LREC 2018 5

This paper describes the Spot the Difference Corpus which contains 54
interactions between pairs of subjects interacting to find differences in two
very similar scenes. The setup used, the participants' metadata and details
about collection are described. We are releasing this corpus of task-oriented
spontaneous dialogues. This release includes rich transcriptions, annotations,
audio and video. We believe that this dataset constitutes a valuable resource
to study several dimensions of human communication that go from turn-taking to
the study of referring expressions. In our preliminary analyses we have looked
at task success (how many differences were found out of the total number of
differences) and how it evolves over time. In addition we have looked at scene
complexity provided by the RGB components' entropy and how it could relate to
speech overlaps, interruptions and the expression of uncertainty. We found
there is a tendency that more complex scenes have more competitive
interruptions.
Parser Training with Heterogeneous Treebanks
Authors: Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Open-Ended Question Answering

How to make the most of multiple heterogeneous treebanks when training a
monolingual dependency parser is an open question. We start by investigating
previously suggested, but little evaluated, strategies for exploiting multiple
treebanks based on concatenating training sets, with or without fine-tuning. We
go on to propose a new method based on treebank embeddings. We perform
experiments for several languages and show that in many cases fine-tuning and
treebank embeddings lead to substantial improvements over single treebanks or
concatenation, with average gains of 2.0--3.5 LAS points. We argue that
treebank embeddings should be preferred due to their conceptual simplicity,
flexibility and extensibility.
Unsupervised Intuitive Physics from Visual Observations
Authors: Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi
Date: 2018-05-14 00:00:00

While learning models of intuitive physics is an increasingly active area of
research, current approaches still fall short of natural intelligences in one
important regard: they require external supervision, such as explicit access to
physical states, at training and sometimes even at test times. Some authors
have relaxed such requirements by supplementing the model with an handcrafted
physical simulator. Still, the resulting methods are unable to automatically
learn new complex environments and to understand physical interactions within
them. In this work, we demonstrated for the first time learning such predictors
directly from raw visual observations and without relying on simulators. We do
so in two steps: first, we learn to track mechanically-salient objects in
videos using causality and equivariance, two unsupervised learning principles
that do not require auto-encoding. Second, we demonstrate that the extracted
positions are sufficient to successfully train visual motion predictors that
can take the underlying environment into account. We validate our predictors on
synthetic datasets; then, we introduce a new dataset, ROLL4REAL, consisting of
real objects rolling on complex terrains (pool table, elliptical bowl, and
random height-field). We show that in all such cases it is possible to learn
reliable extrapolators of the object trajectories from raw videos alone,
without any form of external supervision and with no more prior knowledge than
the choice of a convolutional neural network architecture.
Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data
Authors: Alison Q. O'Neil, Antanas Kascenas, Joseph Henry, Daniel Wyeth, Matthew Shepherd, Erin Beveridge, Lauren Clunie, Carrie Sansom, Evelina Šeduikytė, Keith Muir, Ian Poole
Date: 2018-05-14 00:00:00
Tasks: Anatomical Landmark Detection, Efficient Neural Network, GPU

We present an efficient neural network method for locating anatomical
landmarks in 3D medical CT scans, using atlas location autocontext in order to
learn long-range spatial context. Location predictions are made by regression
to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise
application of a shallow network, thus enabling multiple volumetric heatmaps to
be predicted concurrently without prohibitive GPU memory requirements. Further,
the system allows inter-landmark spatial relationships to be exploited using a
simple overdetermined affine mapping that is robust to detection failures and
occlusion or partial views. Evaluation is performed for 22 landmarks defined on
a range of structures in head CT scans. Models are trained and validated on 201
scans. Over the final test set of 20 scans which was independently annotated by
2 human annotators, the neural network reaches an accuracy which matches the
annotator variability, with similar human and machine patterns of variability
across landmark classes.
KL-UCB-switch: optimal regret bounds for stochastic bandits from both a distribution-dependent and a distribution-free viewpoints
Authors: Aurélien Garivier, Hédi Hadiji, Pierre Menard, Gilles Stoltz
Date: 2018-05-14 00:00:00

We consider $K$-armed stochastic bandits and consider cumulative regret bounds up to time $T$. We are interested in strategies achieving simultaneously a distribution-free regret bound of optimal order $\sqrt{KT}$ and a distribution-dependent regret that is asymptotically optimal, that is, matching the $\kappa\ln T$ lower bound by Lai and Robbins (1985) and Burnetas and Katehakis (1996), where $\kappa$ is the optimal problem-dependent constant. This constant $\kappa$ depends on the model $\mathcal{D}$ considered (the family of possible distributions over the arms). M\'enard and Garivier (2017) provided strategies achieving such a bi-optimality in the parametric case of models given by one-dimensional exponential families, while Lattimore (2016, 2018) did so for the family of (sub)Gaussian distributions with variance less than $1$. We extend this result to the non-parametric case of all distributions over $[0,1]$. We do so by combining the MOSS strategy by Audibert and Bubeck (2009), which enjoys a distribution-free regret bound of optimal order $\sqrt{KT}$, and the KL-UCB strategy by Capp\'e et al. (2013), for which we provide in passing the first analysis of an optimal distribution-dependent $\kappa\ln T$ regret bound in the model of all distributions over $[0,1]$. We were able to obtain this non-parametric bi-optimality result while working hard to streamline the proofs (of previously known regret bounds and thus of the new analyses carried out); a second merit of the present contribution is therefore to provide a review of proofs of classical regret bounds for index-based strategies for $K$-armed stochastic bandits.
Token-level and sequence-level loss smoothing for RNN language models
Authors: Maha Elbayad, Laurent Besacier, Jakob Verbeek
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Captioning, Machine Translation, Translation

Despite the effectiveness of recurrent neural network language models, their
maximum likelihood estimation suffers from two limitations. It treats all
sentences that do not match the ground truth as equally poor, ignoring the
structure of the output space. Second, it suffers from "exposure bias": during
training tokens are predicted given ground-truth sequences, while at test time
prediction is conditioned on generated output sequences. To overcome these
limitations we build upon the recent reward augmented maximum likelihood
approach \ie sequence-level smoothing that encourages the model to predict
sentences close to the ground truth according to a given performance metric. We
extend this approach to token-level loss smoothing, and propose improvements to
the sequence-level smoothing approach. Our experiments on two different tasks,
image captioning and machine translation, show that token-level and
sequence-level loss smoothing are complementary, and significantly improve
results.
Embedding Learning Through Multilingual Concept Induction
Authors: Philipp Dufter, Mengjie Zhao, Martin Schmitt, Alexander Fraser, Hinrich Schütze
Date: 2018-01-21 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentiment Analysis, Word Similarity

We present a new method for estimating vector space representations of words:
embedding learning by concept induction. We test this method on a highly
parallel corpus and learn semantic representations of words in 1259 different
languages in a single common space. An extensive experimental evaluation on
crosslingual word similarity and sentiment analysis indicates that
concept-based multilingual embedding learning performs better than previous
approaches.
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Authors: Jose Camacho-Collados, Mohammad Taher Pilehvar
Date: 2018-05-10 00:00:00
Tasks: Survey

Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.