pwc-archive/papers-with-abstracts huggingface dataset:
Representing the Insincere: Strategically Robust Proportional Representation
Authors: Barton E. Lee
Date: 2018-01-29 00:00:00

Proportional representation (PR) is a fundamental principle of many
democracies world-wide which employ PR-based voting rules to elect their
representatives. The normative properties of these voting rules however, are
often only understood in the context of sincere voting.
  In this paper we consider PR in the presence of strategic voters. We
construct a voting rule such that for every preference profile there exists at
least one costly voting equilibrium satisfying PR with respect to voters'
private and unrevealed preferences - such a voting rule is said to be
strategically robust. In contrast, a commonly applied voting rule is shown not
be strategically robust. Furthermore, we prove a limit on `how strategically
robust' a PR-based voting rule can be; we show that there is no PR-based voting
rule which ensures that every equilibrium satisfies PR. Collectively, our
results highlight the possibility and limit of achieving PR in the presence of
strategic voters and a positive role for mechanisms, such as pre-election
polls, which coordinate voter behaviour towards equilibria which satisfy PR.
Certified Defenses against Adversarial Examples
Authors: Aditi Raghunathan, Jacob Steinhardt, Percy Liang
Date: 2018-01-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Attack, Adversarial Defense, image-classification, Image Classification

While neural networks have achieved high accuracy on standard image classification benchmarks, their accuracy drops to nearly zero in the presence of small adversarial perturbations to test inputs. Defenses based on regularization and adversarial training have been proposed, but often followed by new, stronger attacks that defeat these defenses. Can we somehow end this arms race? In this work, we study this problem for neural networks with one hidden layer. We first propose a method based on a semidefinite relaxation that outputs a certificate that for a given network and test input, no attack can force the error to exceed a certain value. Second, as this certificate is differentiable, we jointly optimize it with the network parameters, providing an adaptive regularizer that encourages robustness against all attacks. On MNIST, our approach produces a network and a certificate that no attack that perturbs each pixel by at most \epsilon = 0.1 can cause more than 35% test error.
Gabor Convolutional Networks
Authors: Shangzhen Luan, Baochang Zhang, Chen Chen, Xian-Bin Cao, Jungong Han, Jianzhuang Liu
Date: 2017-05-03 00:00:00

Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes. By only manipulating the basic element of DCNNs based on Gabor filters, i.e., the convolution operator, GCNs can be easily implemented and are compatible with any popular deep learning architecture. Experimental results demonstrate the super capability of our algorithm in recognizing objects, where the scale and rotation changes occur frequently. The proposed GCNs have much fewer learnable network parameters, and thus is easier to train with an end-to-end pipeline.
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
Authors: Jason Kuen, Xiangfei Kong, Zhe Lin, Gang Wang, Jianxiong Yin, Simon See, Yap-Peng Tan
Date: 2018-01-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Object Recognition

It is desirable to train convolutional networks (CNNs) to run more
efficiently during inference. In many cases however, the computational budget
that the system has for inference cannot be known beforehand during training,
or the inference budget is dependent on the changing real-time resource
availability. Thus, it is inadequate to train just inference-efficient CNNs,
whose inference costs are not adjustable and cannot adapt to varied inference
budgets. We propose a novel approach for cost-adjustable inference in CNNs -
Stochastic Downsampling Point (SDPoint). During training, SDPoint applies
feature map downsampling to a random point in the layer hierarchy, with a
random downsampling ratio. The different stochastic downsampling configurations
known as SDPoint instances (of the same model) have computational costs
different from each other, while being trained to minimize the same prediction
loss. Sharing network parameters across different instances provides
significant regularization boost. During inference, one may handpick a SDPoint
instance that best fits the inference budget. The effectiveness of SDPoint, as
both a cost-adjustable inference approach and a regularizer, is validated
through extensive experiments on image classification.
Controllable Invariance through Adversarial Feature Learning
Authors: Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, Graham Neubig
Date: 2017-05-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification, image-classification, Image Classification, Representation Learning

Learning meaningful representations that maintain the content necessary for a
particular task while filtering away detrimental variations is a problem of
great interest in machine learning. In this paper, we tackle the problem of
learning representations invariant to a specific factor or trait of data. The
representation learning process is formulated as an adversarial minimax game.
We analyze the optimal equilibrium of such a game and find that it amounts to
maximizing the uncertainty of inferring the detrimental factor given the
representation while maximizing the certainty of making task-specific
predictions. On three benchmark tasks, namely fair and bias-free
classification, language-independent generation, and lighting-independent image
classification, we show that the proposed framework induces an invariant
representation, and leads to better generalization evidenced by the improved
performance.
Clustering based on the In-tree Graph Structure and Affinity Propagation
Authors: Teng Qiu, Yong-Jie Li
Date: 2015-01-18 00:00:00
Tasks: Clustering

A recently proposed clustering method, called the Nearest Descent (ND), can
organize the whole dataset into a sparsely connected graph, called the In-tree.
This ND-based Intree structure proves able to reveal the clustering structure
underlying the dataset, except one imperfect place, that is, there are some
undesired edges in this In-tree which require to be removed. Here, we propose
an effective way to automatically remove the undesired edges in In-tree via an
effective combination of the In-tree structure with affinity propagation (AP).
The key for the combination is to add edges between the reachable nodes in
In-tree before using AP to remove the undesired edges. The experiments on both
synthetic and real datasets demonstrate the effectiveness of the proposed
method.
Sparse and Low-rank Tensor Estimation via Cubic Sketchings
Authors: Botao Hao, Anru Zhang, Guang Cheng
Date: 2018-01-29 00:00:00
Tasks: regression, Tensor Decomposition

In this paper, we propose a general framework for sparse and low-rank tensor estimation from cubic sketchings. A two-stage non-convex implementation is developed based on sparse tensor decomposition and thresholded gradient descent, which ensures exact recovery in the noiseless case and stable recovery in the noisy case with high probability. The non-asymptotic analysis sheds light on an interplay between optimization error and statistical error. The proposed procedure is shown to be rate-optimal under certain conditions. As a technical by-product, novel high-order concentration inequalities are derived for studying high-moment sub-Gaussian tensors. An interesting tensor formulation illustrates the potential application to high-order interaction pursuit in high-dimensional linear regression.
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
Authors: Arindam Das, Saikat Roy, Ujjwal Bhattacharya, Swapan Kumar Parui
Date: 2018-01-29 00:00:00
Tasks: document-image-classification, Document Image Classification, General Classification, image-classification, Image Classification, Transfer Learning

In this work, a region-based Deep Convolutional Neural Network framework is
proposed for document structure learning. The contribution of this work
involves efficient training of region based classifiers and effective
ensembling for document image classification. A primary level of `inter-domain'
transfer learning is used by exporting weights from a pre-trained VGG16
architecture on the ImageNet dataset to train a document classifier on whole
document images. Exploiting the nature of region based influence modelling, a
secondary level of `intra-domain' transfer learning is used for rapid training
of deep learning models for image segments. Finally, stacked generalization
based ensembling is utilized for combining the predictions of the base deep
neural network models. The proposed method achieves state-of-the-art accuracy
of 92.2% on the popular RVL-CDIP document image dataset, exceeding benchmarks
set by existing algorithms.
A Cyber Science Based Ontology for Artificial General Intelligence Containment
Authors: Jason M. Pittman, Courtney Crosby
Date: 2018-01-28 00:00:00

The development of artificial general intelligence is considered by many to be inevitable. What such intelligence does after becoming aware is not so certain. To that end, research suggests that the likelihood of artificial general intelligence becoming hostile to humans is significant enough to warrant inquiry into methods to limit such potential. Thus, containment of artificial general intelligence is a timely and meaningful research topic. While there is limited research exploring possible containment strategies, such work is bounded by the underlying field the strategies draw upon. Accordingly, we set out to construct an ontology to describe necessary elements in any future containment technology. Using existing academic literature, we developed a single domain ontology containing five levels, 32 codes, and 32 associated descriptors. Further, we constructed ontology diagrams to demonstrate intended relationships. We then identified humans, AGI, and the cyber world as novel agent objects necessary for future containment activities. Collectively, the work addresses three critical gaps: (a) identifying and arranging fundamental constructs; (b) situating AGI containment within cyber science; and (c) developing scientific rigor within the field.
A high-performance analog Max-SAT solver and its application to Ramsey numbers
Authors: Botond Molnár, Melinda Varga, Zoltan Toroczkai, Mária Ercsey-Ravasz
Date: 2018-01-20 00:00:00

We introduce a continuous-time analog solver for MaxSAT, a quintessential
class of NP-hard discrete optimization problems, where the task is to find a
truth assignment for a set of Boolean variables satisfying the maximum number
of given logical constraints. We show that the scaling of an invariant of the
solver's dynamics, the escape rate, as function of the number of unsatisfied
clauses can predict the global optimum value, often well before reaching the
corresponding state. We demonstrate the performance of the solver on hard
MaxSAT competition problems. We then consider the two-color Ramsey number
$R(m,m)$ problem, translate it to SAT, and apply our algorithm to the still
unknown $R(5,5)$. We find edge colorings without monochromatic 5-cliques for
complete graphs up to 42 vertices, while on 43 vertices we find colorings with
only two monochromatic 5-cliques, the best coloring found so far, supporting
the conjecture that $R(5,5) = 43$.
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data
Authors: Ning Liu, Ying Liu, Brent Logan, Zhiyuan Xu, Jian Tang, Yanzhi Wang
Date: 2018-01-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents the first deep reinforcement learning (DRL) framework to
estimate the optimal Dynamic Treatment Regimes from observational medical data.
This framework is more flexible and adaptive for high dimensional action and
state spaces than existing reinforcement learning methods to model real-life
complexity in heterogeneous disease progression and treatment choices, with the
goal of providing doctor and patients the data-driven personalized decision
recommendations. The proposed DRL framework comprises (i) a supervised learning
step to predict the most possible expert actions, and (ii) a deep reinforcement
learning step to estimate the long-term value function of Dynamic Treatment
Regimes. Both steps depend on deep neural networks.
  As a key motivational example, we have implemented the proposed framework on
a data set from the Center for International Bone Marrow Transplant Research
(CIBMTR) registry database, focusing on the sequence of prevention and
treatments for acute and chronic graft versus host disease after
transplantation. In the experimental results, we have demonstrated promising
accuracy in predicting human experts' decisions, as well as the high expected
reward function in the DRL-based dynamic treatment regimes.
Multi-Pointer Co-Attention Networks for Recommendation
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2018-01-28 00:00:00
Tasks: Recommendation Systems, Representation Learning

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and
DeepCoNN exploit reviews for representation learning. This paper proposes a new
neural architecture for recommendation with reviews. Our model operates on a
multi-hierarchical paradigm and is based on the intuition that not all reviews
are created equal, i.e., only a select few are important. The importance,
however, should be dynamically inferred depending on the current target. To
this end, we propose a review-by-review pointer-based learning scheme that
extracts important reviews, subsequently matching them in a word-by-word
fashion. This enables not only the most informative reviews to be utilized for
prediction but also a deeper word-level interaction. Our pointer-based method
operates with a novel gumbel-softmax based pointer mechanism that enables the
incorporation of discrete vectors within differentiable neural architectures.
Our pointer mechanism is co-attentive in nature, learning pointers which are
co-dependent on user-item relationships. Finally, we propose a multi-pointer
learning scheme that learns to combine multiple views of interactions between
user and item. Overall, we demonstrate the effectiveness of our proposed model
via extensive experiments on \textbf{24} benchmark datasets from Amazon and
Yelp. Empirical results show that our approach significantly outperforms
existing state-of-the-art, with up to 19% and 71% relative improvement when
compared to TransNet and DeepCoNN respectively. We study the behavior of our
multi-pointer learning mechanism, shedding light on evidence aggregation
patterns in review-based recommender systems.
Densely Connected Convolutional Networks
Authors: Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
Date: 2016-08-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Breast Tumour Classification, Classification, Crowd Counting, Image Classification, Medical Image Classification, Medical Image Segmentation, Multi-Label Classification, Object Recognition, Pedestrian Attribute Recognition, Person Re-Identification, Speaker-Specific Lip to Speech Synthesis

Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), which connects each layer to every other layer in a
feed-forward fashion. Whereas traditional convolutional networks with L layers
have L connections - one between each layer and its subsequent layer - our
network has L(L+1)/2 direct connections. For each layer, the feature-maps of
all preceding layers are used as inputs, and its own feature-maps are used as
inputs into all subsequent layers. DenseNets have several compelling
advantages: they alleviate the vanishing-gradient problem, strengthen feature
propagation, encourage feature reuse, and substantially reduce the number of
parameters. We evaluate our proposed architecture on four highly competitive
object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet).
DenseNets obtain significant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high performance. Code and
pre-trained models are available at https://github.com/liuzhuang13/DenseNet .
Fader Networks: Manipulating Images by Sliding Attributes
Authors: Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic Denoyer, Marc'Aurelio Ranzato
Date: 2017-06-01 00:00:00
Tasks: Attribute, Decoder

This paper introduces a new encoder-decoder architecture that is trained to
reconstruct images by disentangling the salient information of the image and
the values of attributes directly in the latent space. As a result, after
training, our model can generate different realistic versions of an input image
by varying the attribute values. By using continuous attribute values, we can
choose how much a specific attribute is perceivable in the generated image.
This property could allow for applications where users can modify an image
using sliding knobs, like faders on a mixing console, to change the facial
expression of a portrait, or to update the color of some objects. Compared to
the state-of-the-art which mostly relies on training adversarial networks in
pixel space by altering attribute values at train time, our approach results in
much simpler training schemes and nicely scales to multiple attributes. We
present evidence that our model can significantly change the perceived value of
the attributes while preserving the naturalness of images.
Graph Based Analysis for Gene Segment Organization In a Scrambled Genome
Authors: Mustafa Hajij, Nataša Jonoska, Denys Kukushkin, Masahico Saito
Date: 2018-01-18 00:00:00
Tasks: Topological Data Analysis

DNA rearrangement processes recombine gene segments that are organized on the
chromosome in a variety of ways. The segments can overlap, interleave or one
may be a subsegment of another. We use directed graphs to represent segment
organizations on a given locus where contigs containing rearranged segments
represent vertices and the edges correspond to the segment relationships. Using
graph properties we associate a point in a higher dimensional Euclidean space
to each graph such that cluster formations and analysis can be performed with
methods from topological data analysis. The method is applied to a recently
sequenced model organism \textit{Oxytricha trifallax}, a species of ciliate
with highly scrambled genome that undergoes massive rearrangement process after
conjugation. The analysis shows some emerging star-like graph structures
indicating that segments of a single gene can interleave, or even contain all
of the segments from fifteen or more other genes in between its segments. We
also observe that as many as six genes can have their segments mutually
interleaving or overlapping.
Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization
Authors: Hongwen Zhang, Qi Li, Zhenan Sun
Date: 2018-01-28 00:00:00
Tasks: 3D Facial Landmark Localization, Depth Estimation, Face Alignment, Facial Landmark Detection, regression

3D face shape is more expressive and viewpoint-consistent than its 2D
counterpart. However, 3D facial landmark localization in a single image is
challenging due to the ambiguous nature of landmarks under 3D perspective.
Existing approaches typically adopt a suboptimal two-step strategy, performing
2D landmark localization followed by depth estimation. In this paper, we
propose the Joint Voxel and Coordinate Regression (JVCR) method for 3D facial
landmark localization, addressing it more effectively in an end-to-end fashion.
First, a compact volumetric representation is proposed to encode the per-voxel
likelihood of positions being the 3D landmarks. The dimensionality of such a
representation is fixed regardless of the number of target landmarks, so that
the curse of dimensionality could be avoided. Then, a stacked hourglass network
is adopted to estimate the volumetric representation from coarse to fine,
followed by a 3D convolution network that takes the estimated volume as input
and regresses 3D coordinates of the face shape. In this way, the 3D structural
constraints between landmarks could be learned by the neural network in a more
efficient manner. Moreover, the proposed pipeline enables end-to-end training
and improves the robustness and accuracy of 3D facial landmark localization.
The effectiveness of our approach is validated on the 3DFAW and AFLW2000-3D
datasets. Experimental results show that the proposed method achieves
state-of-the-art performance in comparison with existing methods.
Cross-language Framework for Word Recognition and Spotting of Indic Scripts
Authors: Ayan Kumar Bhunia, Partha Pratim Roy, Akash Mohta, Umapada Pal
Date: 2017-12-19 00:00:00

Handwritten word recognition and spotting of low-resource scripts are
difficult as sufficient training data is not available and it is often
expensive for collecting data of such scripts. This paper presents a novel
cross language platform for handwritten word recognition and spotting for such
low-resource scripts where training is performed with a sufficiently large
dataset of an available script (considered as source script) and testing is
done on other scripts (considered as target script). Training with one source
script and testing with another script to have a reasonable result is not easy
in handwriting domain due to the complex nature of handwriting variability
among scripts. Also it is difficult in mapping between source and target
characters when they appear in cursive word images. The proposed Indic cross
language framework exploits a large resource of dataset for training and uses
it for recognizing and spotting text of other target scripts where sufficient
amount of training data is not available. Since, Indic scripts are mostly
written in 3 zones, namely, upper, middle and lower, we employ zone-wise
character (or component) mapping for efficient learning purpose. The
performance of our cross-language framework depends on the extent of similarity
between the source and target scripts. Hence, we devise an entropy based script
similarity score using source to target character mapping that will provide a
feasibility of cross language transcription. We have tested our approach in
three Indic scripts, namely, Bangla, Devanagari and Gurumukhi, and the
corresponding results are reported.