pwc-archive/papers-with-abstracts huggingface dataset:
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Authors: Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Date: 2019-01-18 00:00:00
Tasks: Small Data Image Classification, Uncertainty Quantification

Surrogate modeling and uncertainty quantification tasks for PDE systems are
most often considered as supervised learning problems where input and output
data pairs are used for training. The construction of such emulators is by
definition a small data problem which poses challenges to deep learning
approaches that have been developed to operate in the big data regime. Even in
cases where such models have been shown to have good predictive capability in
high dimensions, they fail to address constraints in the data implied by the
PDE model. This paper provides a methodology that incorporates the governing
equations of the physical model in the loss/likelihood functions. The resulting
physics-constrained, deep learning models are trained without any labeled data
(e.g. employing only input data) and provide comparable predictive responses
with data-driven models while obeying the constraints of the problem at hand.
This work employs a convolutional encoder-decoder neural network approach as
well as a conditional flow-based generative model for the solution of PDEs,
surrogate model construction, and uncertainty quantification tasks. The
methodology is posed as a minimization problem of the reverse Kullback-Leibler
(KL) divergence between the model predictive density and the reference
conditional density, where the later is defined as the Boltzmann-Gibbs
distribution at a given inverse temperature with the underlying potential
relating to the PDE system of interest. The generalization capability of these
models to out-of-distribution input is considered. Quantification and
interpretation of the predictive uncertainty is provided for a number of
problems.
Improving Sequence-to-Sequence Learning via Optimal Transport
Authors: Liqun Chen, Yizhe Zhang, Ruiyi Zhang, Chenyang Tao, Zhe Gan, Haichao Zhang, Bai Li, Dinghan Shen, Changyou Chen, Lawrence Carin
Date: 2019-01-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Abstractive Text Summarization, Image Captioning, Machine Translation, Sentence, Text Summarization, Translation

Sequence-to-sequence models are commonly trained via maximum likelihood
estimation (MLE). However, standard MLE training considers a word-level
objective, predicting the next word given the previous ground-truth partial
sentence. This procedure focuses on modeling local syntactic patterns, and may
fail to capture long-range semantic structure. We present a novel solution to
alleviate these issues. Our approach imposes global sequence-level guidance via
new supervision based on optimal transport, enabling the overall
characterization and preservation of semantic features. We further show that
this method can be understood as a Wasserstein gradient flow trying to match
our model to the ground truth sequence distribution. Extensive experiments are
conducted to validate the utility of the proposed approach, showing consistent
improvements over a wide variety of NLP tasks, including machine translation,
abstractive text summarization, and image captioning.
Predicting Performance using Approximate State Space Model for Liquid State Machines
Authors: Ajinkya Gorad, Vivek Saraswat, Udayan Ganguly
Date: 2019-01-18 00:00:00
Tasks: speech-recognition, Speech Recognition, Time Series Analysis, Time Series Prediction

Liquid State Machine (LSM) is a brain-inspired architecture used for solving
problems like speech recognition and time series prediction. LSM comprises of a
randomly connected recurrent network of spiking neurons. This network
propagates the non-linear neuronal and synaptic dynamics. Maass et al. have
argued that the non-linear dynamics of LSMs is essential for its performance as
a universal computer. Lyapunov exponent (mu), used to characterize the
"non-linearity" of the network, correlates well with LSM performance. We
propose a complementary approach of approximating the LSM dynamics with a
linear state space representation. The spike rates from this model are well
correlated to the spike rates from LSM. Such equivalence allows the extraction
of a "memory" metric (tau_M) from the state transition matrix. tau_M displays
high correlation with performance. Further, high tau_M system require lesser
epochs to achieve a given accuracy. Being computationally cheap (1800x time
efficient compared to LSM), the tau_M metric enables exploration of the vast
parameter design space. We observe that the performance correlation of the
tau_M surpasses the Lyapunov exponent (mu), (2-4x improvement) in the
high-performance regime over multiple datasets. In fact, while mu increases
monotonically with network activity, the performance reaches a maxima at a
specific activity described in literature as the "edge of chaos". On the other
hand, tau_M remains correlated with LSM performance even as mu increases
monotonically. Hence, tau_M captures the useful memory of network activity that
enables LSM performance. It also enables rapid design space exploration and
fine-tuning of LSM parameters for high performance.
Computing large market equilibria using abstractions
Authors: Christian Kroer, Alexander Peysakhovich, Eric Sodomka, Nicolas E. Stier-Moses
Date: 2019-01-18 00:00:00
Tasks: Matrix Completion

Computing market equilibria is an important practical problem for market design, for example in fair division of items. However, computing equilibria requires large amounts of information (typically the valuation of every buyer for every item) and computing power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocations back to the original market. We show how to bound important quantities such as regret, envy, Nash social welfare, Pareto optimality, and maximin share/proportionality when the abstracted prices and allocations are used in place of the real equilibrium. We then study two abstraction methods of interest for practitioners: (1) filling in unknown valuations using techniques from matrix completion, (2) reducing the problem size by aggregating groups of buyers/items into smaller numbers of representative buyers/items and solving for equilibrium in this coarsened market. We find that in real data allocations/prices that are relatively close to equilibria can be computed from even very coarse abstractions.
Computing Optimal Coarse Correlated Equilibria in Sequential Games
Authors: Andrea Celli, Stefano Coniglio, Nicola Gatti
Date: 2019-01-18 00:00:00
Tasks: Form

We investigate the computation of equilibria in extensive-form games where ex
ante correlation is possible, focusing on correlated equilibria requiring the
least amount of communication between the players and the mediator. Motivated
by the hardness results on the computation of normal-form correlated
equilibria, we introduce the notion of normal-form coarse correlated
equilibrium, extending the definition of coarse correlated equilibrium to
sequential games. We show that, in two-player games without chance moves, an
optimal (e.g., social welfare maximizing) normal-form coarse correlated
equilibrium can be computed in polynomial time, and that in general
multi-player games (including two-player games with Chance), the problem is
NP-hard. For the former case, we provide a polynomial-time algorithm based on
the ellipsoid method and also propose a more practical one, which can be
efficiently applied to problems of considerable size. Then, we discuss how our
algorithm can be extended to games with Chance and games with more than two
players.
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks
Authors: Oleksandr Bailo, DongShik Ham, Young Min Shin
Date: 2019-01-18 00:00:00
Tasks: Data Augmentation, Image Generation, Image-to-Image Translation, object-detection, Object Detection, Segmentation, Translation

In this paper, we describe how to apply image-to-image translation techniques
to medical blood smear data to generate new data samples and meaningfully
increase small datasets. Specifically, given the segmentation mask of the
microscopy image, we are able to generate photorealistic images of blood cells
which are further used alongside real data during the network training for
segmentation and object detection tasks. This image data generation approach is
based on conditional generative adversarial networks which have proven
capabilities to high-quality image synthesis. In addition to synthesizing blood
images, we synthesize segmentation mask as well which leads to a diverse
variety of generated samples. The effectiveness of the technique is thoroughly
analyzed and quantified through a number of experiments on a manually collected
and annotated dataset of blood smear taken under a microscope.
On-Policy Trust Region Policy Optimisation with Replay Buffers
Authors: Dmitry Kangin, Nicolas Pugeault
Date: 2019-01-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Continuous Control, Deep Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Building upon the recent success of deep reinforcement learning methods, we
investigate the possibility of on-policy reinforcement learning improvement by
reusing the data from several consecutive policies. On-policy methods bring
many benefits, such as ability to evaluate each resulting policy. However, they
usually discard all the information about the policies which existed before. In
this work, we propose adaptation of the replay buffer concept, borrowed from
the off-policy learning setting, to create the method, combining advantages of
on- and off-policy learning. To achieve this, the proposed algorithm
generalises the $Q$-, value and advantage functions for data from multiple
policies. The method uses trust region optimisation, while avoiding some of the
common problems of the algorithms such as TRPO or ACKTR: it uses
hyperparameters to replace the trust region selection heuristics, as well as
the trainable covariance matrix instead of the fixed one. In many cases, the
method not only improves the results comparing to the state-of-the-art trust
region on-policy learning algorithms such as PPO, ACKTR and TRPO, but also with
respect to their off-policy counterpart DDPG.
Optimized Realization of Bayesian Networks in Reduced Normal Form using Latent Variable Model
Authors: Giovanni Di Gennaro, Amedeo Buonanno, Francesco A. N. Palmieri
Date: 2019-01-18 00:00:00
Tasks: Form

Bayesian networks in their Factor Graph Reduced Normal Form (FGrn) are a
powerful paradigm for implementing inference graphs. Unfortunately, the
computational and memory costs of these networks may be considerable, even for
relatively small networks, and this is one of the main reasons why these
structures have often been underused in practice. In this work, through a
detailed algorithmic and structural analysis, various solutions for cost
reduction are proposed. An online version of the classic batch learning
algorithm is also analyzed, showing very similar results (in an unsupervised
context); which is essential even if multilevel structures are to be built. The
solutions proposed, together with the possible online learning algorithm, are
included in a C++ library that is quite efficient, especially if compared to
the direct use of the well-known sum-product and Maximum Likelihood (ML)
algorithms. The results are discussed with particular reference to a Latent
Variable Model (LVM) structure.
Generative Adversarial Classifier for Handwriting Characters Super-Resolution
Authors: Zhuang Qian, Kai-Zhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang
Date: 2019-01-18 00:00:00
Tasks: Classification, General Classification, Image Generation, Super-Resolution

Generative Adversarial Networks (GAN) receive great attentions recently due
to its excellent performance in image generation, transformation, and
super-resolution. However, GAN has rarely been studied and trained for
classification, leading that the generated images may not be appropriate for
classification. In this paper, we propose a novel Generative Adversarial
Classifier (GAC) particularly for low-resolution Handwriting Character
Recognition. Specifically, involving additionally a classifier in the training
process of normal GANs, GAC is calibrated for learning suitable structures and
restored characters images that benefits the classification. Experimental
results show that our proposed method can achieve remarkable performance in
handwriting characters 8x super-resolution, approximately 10% and 20% higher
than the present state-of-the-art methods respectively on benchmark data
CASIA-HWDB1.1 and MNIST.
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors
Authors: Alberto Garcia-Garcia, Brayan Stiven Zapata-Impata, Sergio Orts-Escolano, Pablo Gil, Jose Garcia-Rodriguez
Date: 2019-01-18 00:00:00
Tasks: Graph Neural Network

Tactile sensors provide useful contact data during the interaction with an
object which can be used to accurately learn to determine the stability of a
grasp. Most of the works in the literature represented tactile readings as
plain feature vectors or matrix-like tactile images, using them to train
machine learning models. In this work, we explore an alternative way of
exploiting tactile information to predict grasp stability by leveraging
graph-like representations of tactile data, which preserve the actual spatial
arrangement of the sensor's taxels and their locality. In experimentation, we
trained a Graph Neural Network to binary classify grasps as stable or slippery
ones. To train such network and prove its predictive capabilities for the
problem at hand, we captured a novel dataset of approximately 5000
three-fingered grasps across 41 objects for training and 1000 grasps with 10
unknown objects for testing. Our experiments prove that this novel approach can
be effectively used to predict grasp stability.
Identifying Unclear Questions in Community Question Answering Websites
Authors: Jan Trienes, Krisztian Balog
Date: 2019-01-18 00:00:00
Tasks: Community Question Answering, General Classification, Question Answering, text-classification, Text Classification

Thousands of complex natural language questions are submitted to community
question answering websites on a daily basis, rendering them as one of the most
important information sources these days. However, oftentimes submitted
questions are unclear and cannot be answered without further clarification
questions by expert community members. This study is the first to investigate
the complex task of classifying a question as clear or unclear, i.e., if it
requires further clarification. We construct a novel dataset and propose a
classification approach that is based on the notion of similar questions. This
approach is compared to state-of-the-art text classification baselines. Our
main finding is that the similar questions approach is a viable alternative
that can be used as a stepping stone towards the development of supportive user
interfaces for question formulation.
Infeasibility and structural bias in Differential Evolution
Authors: Fabio Caraffini, Anna V. Kononova, David Corne
Date: 2019-01-18 00:00:00

This paper thoroughly investigates a range of popular DE configurations to
identify components responsible for the emergence of structural bias - recently
identified tendency of the algorithm to prefer some regions of the search space
for reasons directly unrelated to the objective function values. Such tendency
was already studied in GA and PSO where a connection was established between
the strength of structural bias and population sizes and potential weaknesses
of these algorithms was highlighted. For DE, this study goes further and
extends the range of aspects that can contribute to presence of structural bias
by including algorithmic component which is usually overlooked - constraint
handling technique. A wide range of DE configurations were subjected to the
protocol for testing for bias. Results suggest that triggering mechanism for
the bias in DE differs to the one previously found for GA and PSO - no clear
dependency on population size exists. Setting of DE parameters is based on a
separate study which on its own leads to interesting directions of new
research. Overall, DE turned out to be robust against structural bias - only
DE/current-to-best/1/bin is clearly biased but this effect is mitigated by the
use of penalty constraint handling technique.
Protein Classification using Machine Learning and Statistical Techniques: A Comparative Analysis
Authors: Chhote Lal Prasad Gupta, Anand Bihari, Sudhakar Tripathi
Date: 2019-01-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, feature selection, General Classification, Prediction

In recent era prediction of enzyme class from an unknown protein is one of
the challenging tasks in bioinformatics. Day to day the number of proteins is
increases as result the prediction of enzyme class gives a new opportunity to
bioinformatics scholars. The prime objective of this article is to implement
the machine learning classification technique for feature selection and
predictions also find out an appropriate classification technique for function
prediction. In this article the seven different classification technique like
CRT, QUEST, CHAID, C5.0, ANN (Artificial Neural Network), SVM and Bayesian has
been implemented on 4368 protein data that has been extracted from UniprotKB
databank and categories into six different class. The proteins data is high
dimensional sequence data and contain a maximum of 48 features.To manipulate
the high dimensional sequential protein data with different classification
technique, the SPSS has been used as an experimental tool. Different
classification techniques give different results for every model and shows that
the data are imbalanced for class C4, C5 and C6. The imbalanced data affect the
performance of model. In these three classes the precision and recall value is
very less or negligible. The experimental results highlight that the C5.0
classification technique accuracy is more suited for protein feature
classification and predictions. The C5.0 classification technique gives 95.56%
accuracy and also gives high precision and recall value. Finally, we conclude
that the features that is selected can be used for function prediction.
Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification
Authors: Youngmin Ro, Jongwon Choi, Dae Ung Jo, Byeongho Heo, Jongin Lim, Jin Young Choi
Date: 2019-01-18 00:00:00
Tasks: Person Re-Identification, Pose Estimation

In person re-identification (ReID) task, because of its shortage of trainable
dataset, it is common to utilize fine-tuning method using a classification
network pre-trained on a large dataset. However, it is relatively difficult to
sufficiently fine-tune the low-level layers of the network due to the gradient
vanishing problem. In this work, we propose a novel fine-tuning strategy that
allows low-level layers to be sufficiently trained by rolling back the weights
of high-level layers to their initial pre-trained weights. Our strategy
alleviates the problem of gradient vanishing in low-level layers and robustly
trains the low-level layers to fit the ReID dataset, thereby increasing the
performance of ReID tasks. The improved performance of the proposed strategy is
validated via several experiments. Furthermore, without any add-ons such as
pose estimation or segmentation, our strategy exhibits state-of-the-art
performance using only vanilla deep convolutional neural network architecture.
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Authors: Weitao Feng, Zhihao Hu, Wei Wu, Junjie Yan, Wanli Ouyang
Date: 2019-01-18 00:00:00
Tasks: General Classification, Multi-Object Tracking, Object Tracking

In this paper, we propose a unified Multi-Object Tracking (MOT) framework
learning to make full use of long term and short term cues for handling complex
cases in MOT scenes. Besides, for better association, we propose switcher-aware
classification (SAC), which takes the potential identity-switch causer
(switcher) into consideration. Specifically, the proposed framework includes a
Single Object Tracking (SOT) sub-net to capture short term cues, a
re-identification (ReID) sub-net to extract long term cues and a switcher-aware
classifier to make matching decisions using extracted features from the main
target and the switcher. Short term cues help to find false negatives, while
long term cues avoid critical mistakes when occlusion happens, and the SAC
learns to combine multiple cues in an effective way and improves robustness.
The method is evaluated on the challenging MOT benchmarks and achieves the
state-of-the-art results.
Cold-start Playlist Recommendation with Multitask Learning
Authors: Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon
Date: 2019-01-18 00:00:00
Tasks: Binary Classification, General Classification

Playlist recommendation involves producing a set of songs that a user might
enjoy. We investigate this problem in three cold-start scenarios: (i) cold
playlists, where we recommend songs to form new personalised playlists for an
existing user; (ii) cold users, where we recommend songs to form new playlists
for a new user; and (iii) cold songs, where we recommend newly released songs
to extend users' existing playlists. We propose a flexible multitask learning
method to deal with all three settings. The method learns from user-curated
playlists, and encourages songs in a playlist to be ranked higher than those
that are not by minimising a bipartite ranking loss. Inspired by an equivalence
between bipartite ranking and binary classification, we show how one can
efficiently approximate an optimal solution of the multitask learning objective
by minimising a classification loss. Empirical results on two real playlist
datasets show the proposed approach has good performance for cold-start
playlist recommendation.
Nonconvex Rectangular Matrix Completion via Gradient Descent without $\ell_{2,\infty}$ Regularization
Authors: Ji Chen, Dekai Liu, Xiao-Dong Li
Date: 2019-01-18 00:00:00
Tasks: Matrix Completion

The analysis of nonconvex matrix completion has recently attracted much attention in the community of machine learning thanks to its computational convenience. Existing analysis on this problem, however, usually relies on $\ell_{2,\infty}$ projection or regularization that involves unknown model parameters, although they are observed to be unnecessary in numerical simulations, see, e.g., Zheng and Lafferty [2016]. In this paper, we extend the analysis of the vanilla gradient descent for positive semidefinite matrix completion proposed in Ma et al. [2017] to the rectangular case, and more significantly, improve the required sampling rate from $O(\operatorname{poly}(\kappa)\mu^3 r^3 \log^3 n/n )$ to $O(\mu^2 r^2 \kappa^{14} \log n/n )$. Our technical ideas and contributions are potentially useful in improving the leave-one-out analysis in other related problems.