pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Learning Through an Information Retrieval Lens
Authors: Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Date: 2017-07-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Few-Shot Learning, General Classification, Information Retrieval, Retrieval, Structured Prediction

Few-shot learning refers to understanding new concepts from only a few
examples. We propose an information retrieval-inspired approach for this
problem that is motivated by the increased importance of maximally leveraging
all the available information in this low-data regime. We define a training
objective that aims to extract as much information as possible from each
training batch by effectively optimizing over all relative orderings of the
batch points simultaneously. In particular, we view each batch point as a
`query' that ranks the remaining ones based on its predicted relevance to them
and we define a model within the framework of structured prediction to optimize
mean Average Precision over these rankings. Our method achieves impressive
results on the standard few-shot classification benchmarks while is also
capable of few-shot retrieval.
Fast and reliable inference algorithm for hierarchical stochastic block models
Authors: Yongjin Park, Joel S. Bader
Date: 2017-11-14 00:00:00
Tasks: Clustering, Stochastic Block Model

Network clustering reveals the organization of a network or corresponding
complex system with elements represented as vertices and interactions as edges
in a (directed, weighted) graph. Although the notion of clustering can be
somewhat loose, network clusters or groups are generally considered as nodes
with enriched interactions and edges sharing common patterns. Statistical
inference often treats groups as latent variables, with observed networks
generated from latent group structure, termed a stochastic block model.
Regardless of the definitions, statistical inference can be either translated
to modularity maximization, which is provably an NP-complete problem.
  Here we present scalable and reliable algorithms that recover hierarchical
stochastic block models fast and accurately. Our algorithm scales almost
linearly in number of edges, and inferred models were more accurate that other
scalable methods.
ABC random forests for Bayesian parameter inference
Authors: Louis Raynal, Jean-Michel Marin, Pierre Pudlo, Mathieu Ribatet, Christian P. Robert, Arnaud Estoup
Date: 2016-05-18 00:00:00
Tasks: Bayesian Inference

This preprint has been reviewed and recommended by Peer Community In
Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036).
Approximate Bayesian computation (ABC) has grown into a standard methodology
that manages Bayesian inference for models associated with intractable
likelihood functions. Most ABC implementations require the preliminary
selection of a vector of informative statistics summarizing raw data.
Furthermore, in almost all existing implementations, the tolerance level that
separates acceptance from rejection of simulated parameter values needs to be
calibrated. We propose to conduct likelihood-free Bayesian inferences about
parameters with no prior selection of the relevant components of the summary
statistics and bypassing the derivation of the associated tolerance level. The
approach relies on the random forest methodology of Breiman (2001) applied in a
(non parametric) regression setting. We advocate the derivation of a new random
forest for each component of the parameter vector of interest. When compared
with earlier ABC solutions, this method offers significant gains in terms of
robustness to the choice of the summary statistics, does not depend on any type
of tolerance level, and is a good trade-off in term of quality of point
estimator precision and credible interval estimations for a given computing
time. We illustrate the performance of our methodological proposal and compare
it with earlier ABC methods on a Normal toy example and a population genetics
example dealing with human population evolution. All methods designed here have
been incorporated in the R package abcrf (version 1.7) available on CRAN.
Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants
Authors: Eduardo Aguilar, Beatriz Remeseiro, Marc Bolaños, Petia Radeva
Date: 2017-11-14 00:00:00

The increase in awareness of people towards their nutritional habits has
drawn considerable attention to the field of automatic food analysis. Focusing
on self-service restaurants environment, automatic food analysis is not only
useful for extracting nutritional information from foods selected by customers,
it is also of high interest to speed up the service solving the bottleneck
produced at the cashiers in times of high demand. In this paper, we address the
problem of automatic food tray analysis in canteens and restaurants
environment, which consists in predicting multiple foods placed on a tray
image. We propose a new approach for food analysis based on convolutional
neural networks, we name Semantic Food Detection, which integrates in the same
framework food localization, recognition and segmentation. We demonstrate that
our method improves the state of the art food detection by a considerable
margin on the public dataset UNIMIB2016 achieving about 90% in terms of
F-measure, and thus provides a significant technological advance towards the
automatic billing in restaurant environments.
An Empirical Study of the Effects of Spurious Transitions on Abstraction-based Heuristics
Authors: Mehdi Sadeqi, Robert C. Holte, Sandra Zilles
Date: 2017-11-14 00:00:00

The efficient solution of state space search problems is often attempted by
guiding search algorithms with heuristics (estimates of the distance from any
state to the goal). A popular way for creating heuristic functions is by using
an abstract version of the state space. However, the quality of
abstraction-based heuristic functions, and thus the speed of search, can suffer
from spurious transitions, i.e., state transitions in the abstract state space
for which no corresponding transitions in the reachable component of the
original state space exist. Our first contribution is a quantitative study
demonstrating that the harmful effects of spurious transitions on heuristic
functions can be substantial, in terms of both the increase in the number of
abstract states and the decrease in the heuristic values, which may slow down
search. Our second contribution is an empirical study on the benefits of
removing a certain kind of spurious transition, namely those that involve
states with a pair of mutually exclusive (mutex) variablevalue assignments. In
the context of state space planning, a mutex pair is a pair of variable-value
assignments that does not occur in any reachable state. Detecting mutex pairs
is a problem that has been addressed frequently in the planning literature. Our
study shows that there are cases in which mutex detection helps to eliminate
harmful spurious transitions to a large extent and thus to speed up search
substantially.
An optimized shape descriptor based on structural properties of networks
Authors: Gisele H. B. Miranda, Jeaneth Machicao, Odemir M. Bruno
Date: 2017-11-14 00:00:00

The structural analysis of shape boundaries leads to the characterization of
objects as well as to the understanding of shape properties. The literature on
graphs and networks have contributed to the structural characterization of
shapes with different theoretical approaches. We performed a study on the
relationship between the shape architecture and the network topology
constructed over the shape boundary. For that, we used a method for network
modeling proposed in 2009. Firstly, together with curvature analysis, we
evaluated the proposed approach for regular polygons. This way, it was possible
to investigate how the network measurements vary according to some specific
shape properties. Secondly, we evaluated the performance of the proposed shape
descriptor in classification tasks for three datasets, accounting for both
real-world and synthetic shapes. We demonstrated that not only degree related
measurements are capable of distinguishing classes of objects. Yet, when using
measurements that account for distinct properties of the network structure, the
construction of the shape descriptor becomes more computationally efficient.
Given the fact the network is dynamically constructed, the number of iterations
can be reduced. The proposed approach accounts for a more robust set of
structural measurements, that improved the discriminant power of the shape
descriptors.
The Multi-layer Information Bottleneck Problem
Authors: Qianqian Yang, Pablo Piantanida, Deniz Gündüz
Date: 2017-11-14 00:00:00

The muti-layer information bottleneck (IB) problem, where information is
propagated (or successively refined) from layer to layer, is considered. Based
on information forwarded by the preceding layer, each stage of the network is
required to preserve a certain level of relevance with regards to a specific
hidden variable, quantified by the mutual information. The hidden variables and
the source can be arbitrarily correlated. The optimal trade-off between rates
of relevance and compression (or complexity) is obtained through a
single-letter characterization, referred to as the rate-relevance region.
Conditions of successive refinabilty are given. Binary source with BSC hidden
variables and binary source with BSC/BEC mixed hidden variables are both proved
to be successively refinable. We further extend our result to Guassian models.
A counterexample of successive refinability is also provided.
Web Robot Detection in Academic Publishing
Authors: Athanasios Lagopoulos, Grigorios Tsoumakas, Georgios Papadopoulos
Date: 2017-11-14 00:00:00
Tasks: Articles

Recent industry reports assure the rise of web robots which comprise more
than half of the total web traffic. They not only threaten the security,
privacy and efficiency of the web but they also distort analytics and metrics,
doubting the veracity of the information being promoted. In the academic
publishing domain, this can cause articles to be faulty presented as prominent
and influential. In this paper, we present our approach on detecting web robots
in academic publishing websites. We use different supervised learning
algorithms with a variety of characteristics deriving from both the log files
of the server and the content served by the website. Our approach relies on the
assumption that human users will be interested in specific domains or articles,
while web robots crawl a web library incoherently. We experiment with features
adopted in previous studies with the addition of novel semantic characteristics
which derive after performing a semantic analysis using the Latent Dirichlet
Allocation (LDA) algorithm. Our real-world case study shows promising results,
pinpointing the significance of semantic features in the web robot detection
problem.
Efficiency Analysis of ASP Encodings for Sequential Pattern Mining Tasks
Authors: Thomas Guyet, Yves Moinard, René Quiniou, Torsten Schaub
Date: 2017-11-14 00:00:00
Tasks: Sequential Pattern Mining

This article presents the use of Answer Set Programming (ASP) to mine
sequential patterns. ASP is a high-level declarative logic programming paradigm
for high level encoding combinatorial and optimization problem solving as well
as knowledge representation and reasoning. Thus, ASP is a good candidate for
implementing pattern mining with background knowledge, which has been a data
mining issue for a long time. We propose encodings of the classical sequential
pattern mining tasks within two representations of embeddings (fill-gaps vs
skip-gaps) and for various kinds of patterns: frequent, constrained and
condensed. We compare the computational performance of these encodings with
each other to get a good insight into the efficiency of ASP encodings. The
results show that the fill-gaps strategy is better on real problems due to
lower memory consumption. Finally, compared to a constraint programming
approach (CPSM), another declarative programming paradigm, our proposal showed
comparable performance.
TripletGAN: Training Generative Model with Triplet Loss
Authors: Gongze Cao, Yezhou Yang, Jie Lei, Cheng Jin, Yang Liu, Mingli Song
Date: 2017-11-14 00:00:00
Tasks: Face Recognition, General Classification, Metric Learning, model, Triplet

As an effective way of metric learning, triplet loss has been widely used in
many deep learning tasks, including face recognition and person-ReID, leading
to many states of the arts. The main innovation of triplet loss is using
feature map to replace softmax in the classification task. Inspired by this
concept, we propose here a new adversarial modeling method by substituting the
classification loss of discriminator with triplet loss. Theoretical proof based
on IPM (Integral probability metric) demonstrates that such setting will help
the generator converge to the given distribution theoretically under some
conditions. Moreover, since triplet loss requires the generator to maximize
distance within a class, we justify tripletGAN is also helpful to prevent mode
collapse through both theory and experiment.
A unified decision making framework for supply and demand management in microgrid networks
Authors: Diddigi Raghuram Bharadwaj, Sai Koti Reddy Danda, Krishnasuri Narayanam, Shalabh Bhatnagar
Date: 2017-11-14 00:00:00
Tasks: Decision Making, Management, Q-Learning, Reinforcement Learning, Scheduling

This paper considers two important problems -- on the supply-side and demand-side respectively and studies both in a unified framework. On the supply side, we study the problem of energy sharing among microgrids with the goal of maximizing profit obtained from selling power while at the same time not deviating much from the customer demand. On the other hand, under shortage of power, this problem becomes one of deciding the amount of power to be bought with dynamically varying prices. On the demand side, we consider the problem of optimally scheduling the time-adjustable demand - i.e., of loads with flexible time windows in which they can be scheduled. While previous works have treated these two problems in isolation, we combine these problems together and provide a unified Markov decision process (MDP) framework for these problems. We then apply the Q-learning algorithm, a popular model-free reinforcement learning technique, to obtain the optimal policy. Through simulations, we show that the policy obtained by solving our MDP model provides more profit to the microgrids.
Learning an Executable Neural Semantic Parser
Authors: Jianpeng Cheng, Siva Reddy, Vijay Saraswat, Mirella Lapata
Date: 2017-11-14 00:00:00
Proceeding: CL 2019 3

This paper describes a neural semantic parser that maps natural language
utterances onto logical forms which can be executed against a task-specific
environment, such as a knowledge base or a database, to produce a response. The
parser generates tree-structured logical forms with a transition-based approach
which combines a generic tree-generation algorithm with domain-general
operations defined by the logical language. The generation process is modeled
by structured recurrent neural networks, which provide a rich encoding of the
sentential context and generation history for making predictions. To tackle
mismatches between natural language and logical form tokens, various attention
mechanisms are explored. Finally, we consider different training settings for
the neural semantic parser, including a fully supervised training where
annotated logical forms are given, weakly-supervised training where denotations
are provided, and distant supervision where only unlabeled sentences and a
knowledge base are available. Experiments across a wide range of datasets
demonstrate the effectiveness of our parser.
Dynamic Principal Projection for Cost-Sensitive Online Multi-Label Classification
Authors: Hong-Min Chu, Kuan-Hao Huang, Hsuan-Tien Lin
Date: 2017-11-14 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sensitivity

We study multi-label classification (MLC) with three important real-world
issues: online updating, label space dimensional reduction (LSDR), and
cost-sensitivity. Current MLC algorithms have not been designed to address
these three issues simultaneously. In this paper, we propose a novel algorithm,
cost-sensitive dynamic principal projection (CS-DPP) that resolves all three
issues. The foundation of CS-DPP is an online LSDR framework derived from a
leading LSDR algorithm. In particular, CS-DPP is equipped with an efficient
online dimension reducer motivated by matrix stochastic gradient, and
establishes its theoretical backbone when coupled with a carefully-designed
online regression learner. In addition, CS-DPP embeds the cost information into
label weights to achieve cost-sensitivity along with theoretical guarantees.
Experimental results verify that CS-DPP achieves better practical performance
than current MLC algorithms across different evaluation criteria, and
demonstrate the importance of resolving the three issues simultaneously.
Exploiting Layerwise Convexity of Rectifier Networks with Sign Constrained Weights
Authors: Senjian An, Farid Boussaid, Mohammed Bennamoun, Ferdous Sohel
Date: 2017-11-14 00:00:00

By introducing sign constraints on the weights, this paper proposes sign
constrained rectifier networks (SCRNs), whose training can be solved
efficiently by the well known majorization-minimization (MM) algorithms. We
prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights
in the second hidden layer and negative weights in the output layer, are
capable of separating any two (or more) disjoint pattern sets. Furthermore, the
proposed two-hidden-layer SCRNs can decompose the patterns of each class into
several clusters so that each cluster is convexly separable from all the
patterns from the other classes. This provides a means to learn the pattern
structures and analyse the discriminant factors between different classes of
patterns.
Multiple-Source Adaptation for Regression Problems
Authors: Judy Hoffman, Mehryar Mohri, Ningshan Zhang
Date: 2017-11-14 00:00:00
Tasks: regression, Sentiment Analysis

We present a detailed theoretical analysis of the problem of multiple-source
adaptation in the general stochastic scenario, extending known results that
assume a single target labeling function. Our results cover a more realistic
scenario and show the existence of a single robust predictor accurate for
\emph{any} target mixture of the source distributions. Moreover, we present an
efficient and practical optimization solution to determine the robust predictor
in the important case of squared loss, by casting the problem as an instance of
DC-programming. We report the results of experiments with both an artificial
task and a sentiment analysis task. We find that our algorithm outperforms
competing approaches by producing a single robust model that performs well on
any target mixture distribution.
The Matrix Hilbert Space and Its Application to Matrix Learning
Authors: Yunfei Ye
Date: 2017-06-25 00:00:00
Tasks: Tensor Decomposition

Theoretical studies have proven that the Hilbert space has remarkable
performance in many fields of applications. Frames in tensor product of Hilbert
spaces were introduced to generalize the inner product to high-order tensors.
However, these techniques require tensor decomposition which could lead to the
loss of information and it is a NP-hard problem to determine the rank of
tensors. Here, we present a new framework, namely matrix Hilbert space to
perform a matrix inner product space when data observations are represented as
matrices. We preserve the structure of initial data and multi-way correlation
among them is captured in the process. In addition, we extend the reproducing
kernel Hilbert space (RKHS) to reproducing kernel matrix Hilbert space (RKMHS)
and propose an equivalent condition of the space uses of the certain kernel
function. A new family of kernels is introduced in our framework to apply the
classifier of Support Tensor Machine(STM) and comparative experiments are
performed on a number of real-world datasets to support our contributions.
Knowledge Transfer Between Artificial Intelligence Systems
Authors: Ivan Y. Tyukin, Alexander N. Gorban, Konstantin Sofeikov, Ilya Romanenko
Date: 2017-09-05 00:00:00
Tasks: Transfer Learning

We consider the fundamental question: how a legacy "student" Artificial
Intelligent (AI) system could learn from a legacy "teacher" AI system or a
human expert without complete re-training and, most importantly, without
requiring significant computational resources. Here "learning" is understood as
an ability of one system to mimic responses of the other and vice-versa. We
call such learning an Artificial Intelligence knowledge transfer. We show that
if internal variables of the "student" Artificial Intelligent system have the
structure of an $n$-dimensional topological vector space and $n$ is
sufficiently high then, with probability close to one, the required knowledge
transfer can be implemented by simple cascades of linear functionals. In
particular, for $n$ sufficiently large, with probability close to one, the
"student" system can successfully and non-iteratively learn $k\ll n$ new
examples from the "teacher" (or correct the same number of mistakes) at the
cost of two additional inner products. The concept is illustrated with an
example of knowledge transfer from a pre-trained convolutional neural network
to a simple linear classifier with HOG features.
Concurrent Pump Scheduling and Storage Level Optimization Using Meta-Models and Evolutionary Algorithms
Authors: Morad Behandish, Zheng Yi Wu
Date: 2017-11-14 00:00:00
Tasks: Evolutionary Algorithms, Scheduling

In spite of the growing computational power offered by the commodity
hardware, fast pump scheduling of complex water distribution systems is still a
challenge. In this paper, the Artificial Neural Network (ANN) meta-modeling
technique has been employed with a Genetic Algorithm (GA) for simultaneously
optimizing the pump operation and the tank levels at the ends of the cycle. The
generalized GA+ANN algorithm has been tested on a real system in the UK.
Comparing to the existing operation, the daily cost is reduced by about 10-15%,
while the number of pump switches are kept below 4 switches-per-day. In
addition, tank levels are optimized ensure a periodic behavior, which results
in a predictable and stable performance over repeated cycles.
SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring
Authors: Yi Tay, Minh C. Phan, Luu Anh Tuan, Siu Cheung Hui
Date: 2017-11-14 00:00:00
Tasks: Automated Essay Scoring, Deep Learning, Feature Engineering, Memorization

Deep learning has demonstrated tremendous potential for Automatic Text
Scoring (ATS) tasks. In this paper, we describe a new neural architecture that
enhances vanilla neural network models with auxiliary neural coherence
features. Our new method proposes a new \textsc{SkipFlow} mechanism that models
relationships between snapshots of the hidden representations of a long
short-term memory (LSTM) network as it reads. Subsequently, the semantic
relationships between multiple snapshots are used as auxiliary features for
prediction. This has two main benefits. Firstly, essays are typically long
sequences and therefore the memorization capability of the LSTM network may be
insufficient. Implicit access to multiple snapshots can alleviate this problem
by acting as a protection against vanishing gradients. The parameters of the
\textsc{SkipFlow} mechanism also acts as an auxiliary memory. Secondly,
modeling relationships between multiple positions allows our model to learn
features that represent and approximate textual coherence. In our model, we
call this \textit{neural coherence} features. Overall, we present a unified
deep learning architecture that generates neural coherence features as it reads
in an end-to-end fashion. Our approach demonstrates state-of-the-art
performance on the benchmark ASAP dataset, outperforming not only feature
engineering baselines but also other deep learning models.