pwc-archive/papers-with-abstracts huggingface dataset:
Learning how to be robust: Deep polynomial regression
Authors: Juan-Manuel Perez-Rua, Tomas Crivelli, Patrick Bouthemy, Patrick Perez
Date: 2018-04-17 00:00:00
Tasks: Decoder, regression, Video Stabilization

Polynomial regression is a recurrent problem with a large number of
applications. In computer vision it often appears in motion analysis. Whatever
the application, standard methods for regression of polynomial models tend to
deliver biased results when the input data is heavily contaminated by outliers.
Moreover, the problem is even harder when outliers have strong structure.
Departing from problem-tailored heuristics for robust estimation of parametric
models, we explore deep convolutional neural networks. Our work aims to find a
generic approach for training deep regression models without the explicit need
of supervised annotation. We bypass the need for a tailored loss function on
the regression parameters by attaching to our model a differentiable hard-wired
decoder corresponding to the polynomial operation at hand. We demonstrate the
value of our findings by comparing with standard robust regression methods.
Furthermore, we demonstrate how to use such models for a real computer vision
problem, i.e., video stabilization. The qualitative and quantitative
experiments show that neural networks are able to learn robustness for general
polynomial regression, with results that well overpass scores of traditional
robust estimation methods.
Saliency deep embedding for aurora image search
Authors: Xi Yang, Xinbo Gao, Bin Song, Nannan Wang, Dong Yang
Date: 2018-05-23 00:00:00
Tasks: Image Retrieval, Region Proposal

Deep neural networks have achieved remarkable success in the field of image
search. However, the state-of-the-art algorithms are trained and tested for
natural images captured with ordinary cameras. In this paper, we aim to explore
a new search method for images captured with circular fisheye lens, especially
the aurora images. To reduce the interference from uninformative regions and
focus on the most interested regions, we propose a saliency proposal network
(SPN) to replace the region proposal network (RPN) in the recent Mask R-CNN. In
our SPN, the centers of the anchors are not distributed in a rectangular
meshing manner, but exhibit spherical distortion. Additionally, the directions
of the anchors are along the deformation lines perpendicular to the magnetic
meridian, which perfectly accords with the imaging principle of circular
fisheye lens. Extensive experiments are performed on the big aurora data,
demonstrating the superiority of our method in both search accuracy and
efficiency.
Empirical Analysis of Foundational Distinctions in Linked Open Data
Authors: Luigi Asprino, Valerio Basile, Paolo Ciancarini, Valentina Presutti
Date: 2018-03-26 00:00:00
Tasks: Common Sense Reasoning, Natural Language Understanding, Scene Recognition

The Web and its Semantic extension (i.e. Linked Open Data) contain open
global-scale knowledge and make it available to potentially intelligent
machines that want to benefit from it. Nevertheless, most of Linked Open Data
lack ontological distinctions and have sparse axiomatisation. For example,
distinctions such as whether an entity is inherently a class or an individual,
or whether it is a physical object or not, are hardly expressed in the data,
although they have been largely studied and formalised by foundational
ontologies (e.g. DOLCE, SUMO). These distinctions belong to common sense too,
which is relevant for many artificial intelligence tasks such as natural
language understanding, scene recognition, and the like. There is a gap between
foundational ontologies, that often formalise or are inspired by pre-existing
philosophical theories and are developed with a top-down approach, and Linked
Open Data that mostly derive from existing databases or crowd-based effort
(e.g. DBpedia, Wikidata). We investigate whether machines can learn
foundational distinctions over Linked Open Data entities, and if they match
common sense. We want to answer questions such as "does the DBpedia entity for
dog refer to a class or to an instance?". We report on a set of experiments
based on machine learning and crowdsourcing that show promising results.
Minimum Margin Loss for Deep Face Recognition
Authors: Xin Wei, Hui Wang, Bryan Scotney, Huan Wan
Date: 2018-05-17 00:00:00
Tasks: Face Recognition

Face recognition has achieved great progress owing to the fast development of
the deep neural network in the past a few years. As an important part of deep
neural networks, a number of the loss functions have been proposed which
significantly improve the state-of-the-art methods. In this paper, we proposed
a new loss function called Minimum Margin Loss (MML) which aims at enlarging
the margin of those overclose class centre pairs so as to enhance the
discriminative ability of the deep features. MML supervises the training
process together with the Softmax Loss and the Centre Loss, and also makes up
the defect of Softmax + Centre Loss. The experimental results on MegaFace, LFW
and YTF datasets show that the proposed method achieves the state-of-the-art
performance, which demonstrates the effectiveness of the proposed MML.
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Authors: Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar
Date: 2018-05-23 00:00:00
Tasks: Semantic Segmentation

Dense conditional random fields (CRFs) have become a popular framework for
modelling several problems in computer vision such as stereo correspondence and
multi-class semantic segmentation. By modelling long-range interactions, dense
CRFs provide a labelling that captures finer detail than their sparse
counterparts. Currently, the state-of-the-art algorithm performs mean-field
inference using a filter-based method but fails to provide a strong theoretical
guarantee on the quality of the solution. A question naturally arises as to
whether it is possible to obtain a maximum a posteriori (MAP) estimate of a
dense CRF using a principled method. Within this paper, we show that this is
indeed possible. We will show that, by using a filter-based method, continuous
relaxations of the MAP problem can be optimised efficiently using
state-of-the-art algorithms. Specifically, we will solve a quadratic
programming (QP) relaxation using the Frank-Wolfe algorithm and a linear
programming (LP) relaxation by developing a proximal minimisation framework. By
exploiting labelling consistency in the higher-order potentials and utilising
the filter-based method, we are able to formulate the above algorithms such
that each iteration has a complexity linear in the number of classes and random
variables. The presented algorithms can be applied to any labelling problem
using a dense CRF with sparse higher-order potentials. In this paper, we use
semantic segmentation as an example application as it demonstrates the ability
of the algorithm to scale to dense CRFs with large dimensions. We perform
experiments on the Pascal dataset to indicate that the presented algorithms are
able to attain lower energies than the mean-field inference method.
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks
Authors: Pantelis R. Vlachas, Wonmin Byeon, Zhong Y. Wan, Themistoklis P. Sapsis, Petros Koumoutsakos
Date: 2018-02-21 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.
Phocas: dimensional Byzantine-resilient stochastic gradient descent
Authors: Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
Date: 2018-05-23 00:00:00

We propose a novel robust aggregation rule for distributed synchronous
Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The
attackers can arbitrarily manipulate the data transferred between the servers
and the workers in the parameter server~(PS) architecture. We prove the
Byzantine resilience of the proposed aggregation rules. Empirical analysis
shows that the proposed techniques outperform current approaches for realistic
use cases and Byzantine attack scenarios.
Universal Language Model Fine-tuning for Text Classification
Authors: Jeremy Howard, Sebastian Ruder
Date: 2018-01-18 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Text Classification, Transfer Learning

Inductive transfer learning has greatly impacted computer vision, but
existing approaches in NLP still require task-specific modifications and
training from scratch. We propose Universal Language Model Fine-tuning
(ULMFiT), an effective transfer learning method that can be applied to any task
in NLP, and introduce techniques that are key for fine-tuning a language model.
Our method significantly outperforms the state-of-the-art on six text
classification tasks, reducing the error by 18-24% on the majority of datasets.
Furthermore, with only 100 labeled examples, it matches the performance of
training from scratch on 100x more data. We open-source our pretrained models
and code.
Addressing the Item Cold-start Problem by Attribute-driven Active Learning
Authors: Yu Zhu, Jinhao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai
Date: 2018-05-23 00:00:00
Tasks: Active Learning, Attribute, Collaborative Filtering, Recommendation Systems

In recommender systems, cold-start issues are situations where no previous
events, e.g. ratings, are known for certain users or items. In this paper, we
focus on the item cold-start problem. Both content information (e.g. item
attributes) and initial user ratings are valuable for seizing users'
preferences on a new item. However, previous methods for the item cold-start
problem either 1) incorporate content information into collaborative filtering
to perform hybrid recommendation, or 2) actively select users to rate the new
item without considering content information and then do collaborative
filtering. In this paper, we propose a novel recommendation scheme for the item
cold-start problem by leverage both active learning and items' attribute
information. Specifically, we design useful user selection criteria based on
items' attributes and users' rating history, and combine the criteria in an
optimization framework for selecting users. By exploiting the feedback ratings,
users' previous ratings and items' attributes, we then generate accurate rating
predictions for the other unselected users. Experimental results on two
real-world datasets show the superiority of our proposed method over
traditional methods.
CNN+CNN: Convolutional Decoders for Image Captioning
Authors: Qingzhong Wang, Antoni B. Chan
Date: 2018-05-23 00:00:00
Tasks: Image Captioning, Sentence

Image captioning is a challenging task that combines the field of computer
vision and natural language processing. A variety of approaches have been
proposed to achieve the goal of automatically describing an image, and
recurrent neural network (RNN) or long-short term memory (LSTM) based models
dominate this field. However, RNNs or LSTMs cannot be calculated in parallel
and ignore the underlying hierarchical structure of a sentence. In this paper,
we propose a framework that only employs convolutional neural networks (CNNs)
to generate captions. Owing to parallel computing, our basic model is around 3
times faster than NIC (an LSTM-based model) during training time, while also
providing better results. We conduct extensive experiments on MSCOCO and
investigate the influence of the model width and depth. Compared with
LSTM-based models that apply similar attention mechanisms, our proposed models
achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of
CIDEr. We also test our model on the paragraph annotation dataset, and get
higher CIDEr score compared with hierarchical LSTMs
Learning Attentional Communication for Multi-Agent Cooperation
Authors: Jiechuan Jiang, Zongqing Lu
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making

Communication could potentially be an effective way for multi-agent
cooperation. However, information sharing among all agents or in predefined
communication architectures that existing methods adopt can be problematic.
When there is a large number of agents, agents cannot differentiate valuable
information that helps cooperative decision making from globally shared
information. Therefore, communication barely helps, and could even impair the
learning of multi-agent cooperation. Predefined communication architectures, on
the other hand, restrict communication among agents and thus restrain potential
cooperation. To tackle these difficulties, in this paper, we propose an
attentional communication model that learns when communication is needed and
how to integrate shared information for cooperative decision making. Our model
leads to efficient and effective communication for large-scale multi-agent
cooperation. Empirically, we show the strength of our model in a variety of
cooperative scenarios, where agents are able to develop more coordinated and
sophisticated strategies than existing methods.
Transfer Learning for Illustration Classification
Authors: Manuel Lagunas, Elena Garces
Date: 2018-05-23 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

The field of image classification has shown an outstanding success thanks to
the development of deep learning techniques. Despite the great performance
obtained, most of the work has focused on natural images ignoring other domains
like artistic depictions. In this paper, we use transfer learning techniques to
propose a new classification network with better performance in illustration
images. Starting from the deep convolutional network VGG19, pre-trained with
natural images, we propose two novel models which learn object representations
in the new domain. Our optimized network will learn new low-level features of
the images (colours, edges, textures) while keeping the knowledge of the
objects and shapes that it already learned from the ImageNet dataset. Thus,
requiring much less data for the training. We propose a novel dataset of
illustration images labelled by content where our optimized architecture
achieves $\textbf{86.61\%}$ of top-1 and $\textbf{97.21\%}$ of top-5 precision.
We additionally demonstrate that our model is still able to recognize objects
in photographs.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Authors: Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deep Learning, General Classification, Graph Classification, Node Classification, Superpixel Image Classification

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant
of deep neural networks for irregular structured and geometric input, e.g.,
graphs or meshes. Our main contribution is a novel convolution operator based
on B-splines, that makes the computation time independent from the kernel size
due to the local support property of the B-spline basis functions. As a result,
we obtain a generalization of the traditional CNN convolution operator by using
continuous kernel functions parametrized by a fixed number of trainable
weights. In contrast to related approaches that filter in the spectral domain,
the proposed method aggregates features purely in the spatial domain. In
addition, SplineCNN allows entire end-to-end training of deep architectures,
using only the geometric structure as input, instead of handcrafted feature
descriptors. For validation, we apply our method on tasks from the fields of
image graph classification, shape correspondence and graph node classification,
and show that it outperforms or pars state-of-the-art approaches while being
significantly faster and having favorable properties like domain-independence.
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Authors: Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Date: 2018-05-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Cross-Lingual Sentiment Classification, Machine Translation, Sentence, Sentiment Analysis, Sentiment Classification, Translation, Word Embeddings

Sentiment analysis in low-resource languages suffers from a lack of annotated
corpora to estimate high-performing models. Machine translation and bilingual
word embeddings provide some relief through cross-lingual sentiment approaches.
However, they either require large amounts of parallel data or do not
sufficiently capture sentiment information. We introduce Bilingual Sentiment
Embeddings (BLSE), which jointly represent sentiment information in a source
and target language. This model only requires a small bilingual lexicon, a
source-language corpus annotated for sentiment, and monolingual word embeddings
for each language. We perform experiments on three language combinations
(Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment
classification and find that our model significantly outperforms
state-of-the-art methods on four out of six experimental setups, as well as
capturing complementary information to machine translation. Our analysis of the
resulting embedding space provides evidence that it represents sentiment
information in the resource-poor target language without any annotated data in
that language.
Learning to Design Games: Strategic Environments in Reinforcement Learning
Authors: Haifeng Zhang, Jun Wang, Zhiming Zhou, Wei-Nan Zhang, Ying Wen, Yong Yu, Wenxin Li
Date: 2017-07-05 00:00:00
Tasks: Game Design, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. This extension is motivated by environment design scenarios in the real-world, including game design, shopping space design and traffic signal design. Theoretically, we find a dual Markov decision process (MDP) w.r.t. the environment to that w.r.t. the agent, and derive a policy gradient solution to optimizing the parametrized environment. Furthermore, discontinuous environments are addressed by a proposed general generative framework. Our experiments on a Maze game design task show the effectiveness of the proposed algorithms in generating diverse and challenging Mazes against various agent settings.
Optimal Single Sample Tests for Structured versus Unstructured Network Data
Authors: Guy Bresler, Dheeraj Nagaraj
Date: 2018-02-17 00:00:00

We study the problem of testing, using only a single sample, between mean
field distributions (like Curie-Weiss, Erd\H{o}s-R\'enyi) and structured Gibbs
distributions (like Ising model on sparse graphs and Exponential Random
Graphs). Our goal is to test without knowing the parameter values of the
underlying models: only the \emph{structure} of dependencies is known. We
develop a new approach that applies to both the Ising and Exponential Random
Graph settings based on a general and natural statistical test. The test can
distinguish the hypotheses with high probability above a certain threshold in
the (inverse) temperature parameter, and is optimal in that below the threshold
no test can distinguish the hypotheses.
  The thresholds do not correspond to the presence of long-range order in the
models. By aggregating information at a global scale, our test works even at
very high temperatures.
  The proofs are based on distributional approximation and sharp concentration
of quadratic forms, when restricted to Hamming spheres. The restriction to
Hamming spheres is necessary, since otherwise any scalar statistic is useless
without explicit knowledge of the temperature parameter. At the same time, this
restriction radically changes the behavior of the functions under
consideration, resulting in a much smaller variance than in the independent
setting; this makes it hard to directly apply standard methods (i.e., Stein's
method) for concentration of weakly dependent variables. Instead, we carry out
an additional tensorization argument using a Markov chain that respects the
symmetry of the Hamming sphere.
Wasserstein Discriminant Analysis
Authors: Rémi Flamary, Marco Cuturi, Nicolas Courty, Alain Rakotomamonjy
Date: 2016-08-29 00:00:00

Wasserstein Discriminant Analysis (WDA) is a new supervised method that can
improve classification of high-dimensional data by computing a suitable linear
map onto a lower dimensional subspace. Following the blueprint of classical
Linear Discriminant Analysis (LDA), WDA selects the projection matrix that
maximizes the ratio of two quantities: the dispersion of projected points
coming from different classes, divided by the dispersion of projected points
coming from the same class. To quantify dispersion, WDA uses regularized
Wasserstein distances, rather than cross-variance measures which have been
usually considered, notably in LDA. Thanks to the the underlying principles of
optimal transport, WDA is able to capture both global (at distribution scale)
and local (at samples scale) interactions between classes. Regularized
Wasserstein distances can be computed using the Sinkhorn matrix scaling
algorithm; We show that the optimization of WDA can be tackled using automatic
differentiation of Sinkhorn iterations. Numerical experiments show promising
results both in terms of prediction and visualization on toy examples and real
life datasets such as MNIST and on deep features obtained from a subset of the
Caltech dataset.
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning
Authors: Yichi Zhang, Zhijian Ou
Date: 2018-03-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling, Network Pruning

An ensemble of neural networks is known to be more robust and accurate than
an individual network, however usually with linearly-increased cost in both
training and testing. In this work, we propose a two-stage method to learn
Sparse Structured Ensembles (SSEs) for neural networks. In the first stage, we
run SG-MCMC with group sparse priors to draw an ensemble of samples from the
posterior distribution of network parameters. In the second stage, we apply
weight-pruning to each sampled network and then perform retraining over the
remained connections. In this way of learning SSEs with SG-MCMC and pruning, we
not only achieve high prediction accuracy since SG-MCMC enhances exploration of
the model-parameter space, but also reduce memory and computation cost
significantly in both training and testing of NN ensembles. This is thoroughly
evaluated in the experiments of learning SSE ensembles of both FNNs and LSTMs.
For example, in LSTM based language modeling (LM), we obtain 21% relative
reduction in LM perplexity by learning a SSE of 4 large LSTM models, which has
only 30% of model parameters and 70% of computations in total, as compared to
the baseline large LSTM LM. To the best of our knowledge, this work represents
the first methodology and empirical study of integrating SG-MCMC, group sparse
prior and network pruning together for learning NN ensembles.