pwc-archive/papers-with-abstracts huggingface dataset:
Towards an understanding of CNNs: analysing the recovery of activation pathways via Deep Convolutional Sparse Coding
Authors: Michael Murray, Jared Tanner
Date: 2018-06-26 00:00:00

Deep Convolutional Sparse Coding (D-CSC) is a framework reminiscent of deep convolutional neural networks (DCNNs), but by omitting the learning of the dictionaries one can more transparently analyse the role of the activation function and its ability to recover activation paths through the layers. Papyan, Romano, and Elad conducted an analysis of such an architecture, demonstrated the relationship with DCNNs and proved conditions under which the D-CSC is guaranteed to recover specific activation paths. A technical innovation of their work highlights that one can view the efficacy of the ReLU nonlinear activation function of a DCNN through a new variant of the tensor's sparsity, referred to as stripe-sparsity. Using this they proved that representations with an activation density proportional to the ambient dimension of the data are recoverable. We extend their uniform guarantees to a modified model and prove that with high probability the true activation is typically possible to recover for a greater density of activations per layer. Our extension follows from incorporating the prior work on one step thresholding by Schnass and Vandergheynst.
Multi-modal Image Processing based on Coupled Dictionary Learning
Authors: Pingfan Song, Miguel R. D. Rodrigues
Date: 2018-06-26 00:00:00
Tasks: Denoising, Dictionary Learning, Super-Resolution

In real-world scenarios, many data processing problems often involve
heterogeneous images associated with different imaging modalities. Since these
multimodal images originate from the same phenomenon, it is realistic to assume
that they share common attributes or characteristics. In this paper, we propose
a multi-modal image processing framework based on coupled dictionary learning
to capture similarities and disparities between different image modalities. In
particular, our framework can capture favorable structure similarities across
different image modalities such as edges, corners, and other elementary
primitives in a learned sparse transform domain, instead of the original pixel
domain, that can be used to improve a number of image processing tasks such as
denoising, inpainting, or super-resolution. Practical experiments demonstrate
that incorporating multimodal information using our framework brings notable
benefits.
Multi-Task Deep Convolutional Neural Network for the Segmentation of Type B Aortic Dissection
Authors: Jianning Li, Long Cao, Yangyang Ge, W. Cheng, M. Bowen, G. Wei
Date: 2018-06-26 00:00:00
Tasks: Segmentation

Segmentation of the entire aorta and true-false lumen is crucial to inform
plan and follow-up for endovascular repair of the rare yet life threatening
type B aortic dissection. Manual segmentation by slice is time-consuming and
requires expertise, while current computer-aided methods focus on the
segmentation of the entire aorta, are unable to concurrently segment true-false
lumen, and some require human interaction. We here report a fully automated
approach based on a 3-D multi-task deep convolutional neural network that
segments the entire aorta and true-false lumen from CTA images in a unified
framework. For training, we built a database containing 254 CTA images (210
preoperative and 44 postoperative) obtained using various systems from 254
unique patients with type B aortic dissection. Slice-wise manual segmentation
of the entire aorta and the true-false lumen for each 3-D CTA image was
provided. Upon evaluation of another 16 CTA images (11 preoperative and 5
postoperative) with ground truth segmentation provided by experienced vascular
surgeons, our method achieves a mean dice similarity score(DSC) of 0.910,0.849
and 0.821 for the entire aorta,true lumen and false lumen respectively.
Dropout-based Active Learning for Regression
Authors: Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev
Date: 2018-06-26 00:00:00
Tasks: Active Learning, regression

Active learning is relevant and challenging for high-dimensional regression
models when the annotation of the samples is expensive. Yet most of the
existing sampling methods cannot be applied to large-scale problems, consuming
too much time for data processing. In this paper, we propose a fast active
learning algorithm for regression, tailored for neural network models. It is
based on uncertainty estimation from stochastic dropout output of the network.
Experiments on both synthetic and real-world datasets show comparable or better
performance (depending on the accuracy metric) as compared to the baselines.
This approach can be generalized to other deep learning architectures. It can
be used to systematically improve a machine-learning model as it offers a
computationally efficient way of sampling additional data.
Quadratic Decomposable Submodular Function Minimization
Authors: Pan Li, Niao He, Olgica Milenkovic
Date: 2018-06-26 00:00:00
Proceeding: NeurIPS 2018 12

We introduce a new convex optimization problem, termed quadratic decomposable
submodular function minimization. The problem is closely related to
decomposable submodular function minimization and arises in many learning on
graphs and hypergraphs settings, such as graph-based semi-supervised learning
and PageRank. We approach the problem via a new dual strategy and describe an
objective that may be optimized via random coordinate descent (RCD) methods and
projections onto cones. We also establish the linear convergence rate of the
RCD algorithm and develop efficient projection algorithms with provable
performance guarantees. Numerical experiments in semi-supervised learning on
hypergraphs confirm the efficiency of the proposed algorithm and demonstrate
the significant improvements in prediction accuracy with respect to
state-of-the-art methods.
Graph-to-Sequence Learning using Gated Graph Neural Networks
Authors: Daniel Beck, Gholamreza Haffari, Trevor Cohn
Date: 2018-06-26 00:00:00
Proceeding: ACL 2018 7
Tasks: Graph-to-Sequence, Machine Translation, Translation

Many NLP applications can be framed as a graph-to-sequence learning problem.
Previous work proposing neural architectures on this setting obtained promising
results compared to grammar-based approaches but still rely on linearisation
heuristics and/or standard recurrent networks to achieve the best performance.
In this work, we propose a new model that encodes the full structural
information contained in the graph. Our architecture couples the recently
proposed Gated Graph Neural Networks with an input transformation that allows
nodes and edges to have their own hidden representations, while tackling the
parameter explosion problem present in previous work. Experimental results show
that our model outperforms strong baselines in generation from AMR graphs and
syntax-based neural machine translation.
Enhancing Sentence Embedding with Generalized Pooling
Authors: Qian Chen, Zhen-Hua Ling, Xiaodan Zhu
Date: 2018-06-26 00:00:00
Proceeding: COLING 2018 8
Tasks: Author Profiling, General Classification, Natural Language Inference, Sentence, Sentence Embedding, Sentence-Embedding, Sentiment Analysis, Sentiment Classification

Pooling is an essential component of a wide variety of sentence
representation and embedding models. This paper explores generalized pooling
methods to enhance sentence embedding. We propose vector-based multi-head
attention that includes the widely used max pooling, mean pooling, and scalar
self-attention as special cases. The model benefits from properly designed
penalization terms to reduce redundancy in multi-head attention. We evaluate
the proposed model on three different tasks: natural language inference (NLI),
author profiling, and sentiment classification. The experiments show that the
proposed model achieves significant improvement over strong
sentence-encoding-based methods, resulting in state-of-the-art performances on
four datasets. The proposed approach can be easily implemented for more
problems than we discuss in this paper.
Unveiling the semantic structure of text documents using paragraph-aware Topic Models
Authors: Simón Roca-Sotelo, Jerónimo Arenas-García
Date: 2018-06-26 00:00:00
Tasks: Topic Models

Classic Topic Models are built under the Bag Of Words assumption, in which
word position is ignored for simplicity. Besides, symmetric priors are
typically used in most applications. In order to easily learn topics with
different properties among the same corpus, we propose a new line of work in
which the paragraph structure is exploited. Our proposal is based on the
following assumption: in many text document corpora there are formal
constraints shared across all the collection, e.g. sections. When this
assumption is satisfied, some paragraphs may be related to general concepts
shared by all documents in the corpus, while others would contain the genuine
description of documents. Assuming each paragraph can be semantically more
general, specific, or hybrid, we look for ways to measure this, transferring
this distinction to topics and being able to learn what we call specific and
general topics. Experiments show that this is a proper methodology to highlight
certain paragraphs in structured documents at the same time we learn
interesting and more diverse topics.
Approximate Nearest Neighbor Search in High Dimensions
Authors: Alexandr Andoni, Piotr Indyk, Ilya Razenshteyn
Date: 2018-06-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$
points in some metric space $(X,D)$, build a data structure that, given any
point $q$, returns a point in $P$ that is closest to $q$ (its "nearest
neighbor" in $P$). The data structure stores additional information about the
set $P$, which is then used to find the nearest neighbor without computing all
distances between $q$ and $P$. The problem has a wide range of applications in
machine learning, computer vision, databases and other fields.
  To reduce the time needed to find nearest neighbors and the amount of memory
used by the data structure, one can formulate the {\em approximate} nearest
neighbor problem, where the the goal is to return any point $p' \in P$ such
that the distance from $q$ to $p'$ is at most $c \cdot \min_{p \in P} D(q,p)$,
for some $c \geq 1$. Over the last two decades, many efficient solutions to
this problem were developed. In this article we survey these developments, as
well as their connections to questions in geometric functional analysis and
combinatorial geometry.
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Authors: Charles Packer, Julian McAuley, Arnau Ramisa
Date: 2018-06-26 00:00:00
Tasks: Interactive Recommendation, Recommendation Systems

Visually-aware recommender systems use visual signals present in the
underlying data to model the visual characteristics of items and users'
preferences towards them. In the domain of clothing recommendation,
incorporating items' visual information (e.g., product images) is particularly
important since clothing item appearance is often a critical factor in
influencing the user's purchasing decisions. Current state-of-the-art
visually-aware recommender systems utilize image features extracted from
pre-trained deep convolutional neural networks, however these extremely
high-dimensional representations are difficult to interpret, especially in
relation to the relatively low number of visual properties that may guide
users' decisions.
  In this paper we propose a novel approach to personalized clothing
recommendation that models the dynamics of individual users' visual
preferences. By using interpretable image representations generated with a
unique feature learning process, our model learns to explain users' prior
feedback in terms of their affinity towards specific visual attributes and
styles. Our approach achieves state-of-the-art performance on personalized
ranking tasks, and the incorporation of interpretable visual features allows
for powerful model introspection, which we demonstrate by using an interactive
recommendation algorithm and visualizing the rise and fall of fashion trends
over time.
Limited Evaluation Evolutionary Optimization of Large Neural Networks
Authors: Jonas Prellberg, Oliver Kramer
Date: 2018-06-26 00:00:00
Tasks: Evolutionary Algorithms, GPU

Stochastic gradient descent is the most prevalent algorithm to train neural
networks. However, other approaches such as evolutionary algorithms are also
applicable to this task. Evolutionary algorithms bring unique trade-offs that
are worth exploring, but computational demands have so far restricted
exploration to small networks with few parameters. We implement an evolutionary
algorithm that executes entirely on the GPU, which allows to efficiently
batch-evaluate a whole population of networks. Within this framework, we
explore the limited evaluation evolutionary algorithm for neural network
training and find that its batch evaluation idea comes with a large accuracy
trade-off. In further experiments, we explore crossover operators and find that
unprincipled random uniform crossover performs extremely well. Finally, we
train a network with 92k parameters on MNIST using an EA and achieve 97.6 %
test accuracy compared to 98 % test accuracy on the same network trained with
Adam. Code is available at https://github.com/jprellberg/gpuea.
Matrix Completion and Performance Guarantees for Single Individual Haplotyping
Authors: Somsubhra Barik, Haris Vikalo
Date: 2018-06-13 00:00:00
Tasks: Matrix Completion

Single individual haplotyping is an NP-hard problem that emerges when
attempting to reconstruct an organism's inherited genetic variations using data
typically generated by high-throughput DNA sequencing platforms. Genomes of
diploid organisms, including humans, are organized into homologous pairs of
chromosomes that differ from each other in a relatively small number of variant
positions. Haplotypes are ordered sequences of the nucleotides in the variant
positions of the chromosomes in a homologous pair; for diploids, haplotypes
associated with a pair of chromosomes may be conveniently represented by means
of complementary binary sequences. In this paper, we consider a binary matrix
factorization formulation of the single individual haplotyping problem and
efficiently solve it by means of alternating minimization. We analyze the
convergence properties of the alternating minimization algorithm and establish
theoretical bounds for the achievable haplotype reconstruction error. The
proposed technique is shown to outperform existing methods when applied to
synthetic as well as real-world Fosmid-based HapMap NA12878 datasets.
Generating Counterfactual Explanations with Natural Language
Authors: Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, Zeynep Akata
Date: 2018-06-26 00:00:00
Tasks: AI Agent, Classification, counterfactual, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

Natural language explanations of deep neural network decisions provide an
intuitive way for a AI agent to articulate a reasoning process. Current textual
explanations learn to discuss class discriminative features in an image.
However, it is also helpful to understand which attributes might change a
classification decision if present in an image (e.g., "This is not a Scarlet
Tanager because it does not have black wings.") We call such textual
explanations counterfactual explanations, and propose an intuitive method to
generate counterfactual explanations by inspecting which evidence in an input
is missing, but might contribute to a different classification decision if
present in the image. To demonstrate our method we consider a fine-grained
image classification task in which we take as input an image and a
counterfactual class and output text which explains why the image does not
belong to a counterfactual class. We then analyze our generated counterfactual
explanations both qualitatively and quantitatively using proposed automatic
metrics.
SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery
Authors: Junjun Jiang, Jiayi Ma, Chen Chen, Zhongyuan Wang, Zhihua Cai, Lizhe Wang
Date: 2018-06-26 00:00:00
Tasks: Dimensionality Reduction, General Classification

As an unsupervised dimensionality reduction method, principal component
analysis (PCA) has been widely considered as an efficient and effective
preprocessing step for hyperspectral image (HSI) processing and analysis tasks.
It takes each band as a whole and globally extracts the most representative
bands. However, different homogeneous regions correspond to different objects,
whose spectral features are diverse. It is obviously inappropriate to carry out
dimensionality reduction through a unified projection for an entire HSI. In
this paper, a simple but very effective superpixelwise PCA approach, called
SuperPCA, is proposed to learn the intrinsic low-dimensional features of HSIs.
In contrast to classical PCA models, SuperPCA has four main properties. (1)
Unlike the traditional PCA method based on a whole image, SuperPCA takes into
account the diversity in different homogeneous regions, that is, different
regions should have different projections. (2) Most of the conventional feature
extraction models cannot directly use the spatial information of HSIs, while
SuperPCA is able to incorporate the spatial context information into the
unsupervised dimensionality reduction by superpixel segmentation. (3) Since the
regions obtained by superpixel segmentation have homogeneity, SuperPCA can
extract potential low-dimensional features even under noise. (4) Although
SuperPCA is an unsupervised method, it can achieve competitive performance when
compared with supervised approaches. The resulting features are discriminative,
compact, and noise resistant, leading to improved HSI classification
performance. Experiments on three public datasets demonstrate that the SuperPCA
model significantly outperforms the conventional PCA based dimensionality
reduction baselines for HSI classification. The Matlab source code is available
at https://github.com/junjun-jiang/SuperPCA
Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games
Authors: Xiaomin Lin, Stephen C. Adams, Peter A. Beling
Date: 2018-06-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, uCE-MIRL, and uNE-MIRL, each distinguished by its solution concept. Problem uCS-MIRL is a cooperative game in which the agents employ cooperative strategies that aim to maximize the total game value. In problem uCE-MIRL, agents are assumed to follow strategies that constitute a correlated equilibrium while maximizing total game value. Problem uNE-MIRL is similar to uCE-MIRL in total game value maximization, but it is assumed that the agents are playing a Nash equilibrium. Problems advE-MIRL and cooE-MIRL assume agents are playing an adversarial equilibrium and a coordination equilibrium, respectively. We propose novel approaches to address these five problems under the assumption that the game observer either knows or is able to accurate estimate the policies and solution concepts for players. For uCS-MIRL, we first develop a characteristic set of solutions ensuring that the observed bi-policy is a uCS and then apply a Bayesian inverse learning method. For uCE-MIRL, we develop a linear programming problem subject to constraints that define necessary and sufficient conditions for the observed policies to be correlated equilibria. The objective is to choose a solution that not only minimizes the total game value difference between the observed bi-policy and a local uCS, but also maximizes the scale of the solution. We apply a similar treatment to the problem of uNE-MIRL. The remaining two problems can be solved efficiently by taking advantage of solution uniqueness and setting up a convex optimization problem. Results are validated on various benchmark grid-world games.
A NoSQL Data-based Personalized Recommendation System for C2C e-Commerce
Authors: Khanh Dang, Khuong Vo, Josef Küng
Date: 2018-06-26 00:00:00
Tasks: Recommendation Systems, Topic Models

With the considerable development of customer-to-customer (C2C) e-commerce in
the recent years, there is a big demand for an effective recommendation system
that suggests suitable websites for users to sell their items with some
specified needs. Nonetheless, e-commerce recommendation systems are mostly
designed for business-to-customer (B2C) websites, where the systems offer the
consumers the products that they might like to buy. Almost none of the related
research works focus on choosing selling sites for target items. In this paper,
we introduce an approach that recommends the selling websites based upon the
item's description, category, and desired selling price. This approach employs
NoSQL data-based machine learning techniques for building and training topic
models and classification models. The trained models can then be used to rank
the websites dynamically with respect to the user needs. The experimental
results with real-world datasets from Vietnam C2C websites will demonstrate the
effectiveness of our proposed method.
A Multi-Modal Chinese Poetry Generation Model
Authors: Dayiheng Liu, Quan Guo, Wubo Li, Jiancheng Lv
Date: 2018-06-26 00:00:00
Tasks: Decoder, model, Sentence

Recent studies in sequence-to-sequence learning demonstrate that RNN
encoder-decoder structure can successfully generate Chinese poetry. However,
existing methods can only generate poetry with a given first line or user's
intent theme. In this paper, we proposed a three-stage multi-modal Chinese
poetry generation approach. Given a picture, the first line, the title and the
other lines of the poem are successively generated in three stages. According
to the characteristics of Chinese poems, we propose a hierarchy-attention
seq2seq model which can effectively capture character, phrase, and sentence
information between contexts and improve the symmetry delivered in poems. In
addition, the Latent Dirichlet allocation (LDA) model is utilized for title
generation and improve the relevance of the whole poem and the title. Compared
with strong baseline, the experimental results demonstrate the effectiveness of
our approach, using machine evaluations as well as human judgments.