pwc-archive/papers-with-abstracts huggingface dataset:
Intent Detection and Slots Prompt in a Closed-Domain Chatbot
Authors: Amber Nigam, Prashik Sahare, Kushagra Pandya
Date: 2018-12-27 00:00:00
Tasks: Chatbot, Classification, Decision Making, General Classification, intent-classification, Intent Classification, Intent Detection

In this paper, we introduce a methodology for predicting intent and slots of
a query for a chatbot that answers career-related queries. We take a
multi-staged approach where both the processes (intent-classification and
slot-tagging) inform each other's decision-making in different stages. The
model breaks down the problem into stages, solving one problem at a time and
passing on relevant results of the current stage to the next, thereby reducing
search space for subsequent stages, and eventually making classification and
tagging more viable after each stage. We also observe that relaxing rules for a
fuzzy entity-matching in slot-tagging after each stage (by maintaining a
separate Named Entity Tagger per stage) helps us improve performance, although
at a slight cost of false-positives. Our model has achieved state-of-the-art
performance with F1-score of 77.63% for intent-classification and 82.24% for
slot-tagging on our dataset that we would publicly release along with the
paper.
Stanza: Layer Separation for Distributed Training in Deep Learning
Authors: Xiaorui Wu, Hong Xu, Bo Li, Yongqiang Xiong
Date: 2018-12-27 00:00:00
Tasks: Deep Learning, GPU

The parameter server architecture is prevalently used for distributed deep
learning. Each worker machine in a parameter server system trains the complete
model, which leads to a hefty amount of network data transfer between workers
and servers. We empirically observe that the data transfer has a non-negligible
impact on training time.
  To tackle the problem, we design a new distributed training system called
Stanza. Stanza exploits the fact that in many models such as convolution neural
networks, most data exchange is attributed to the fully connected layers, while
most computation is carried out in convolutional layers. Thus, we propose layer
separation in distributed training: the majority of the nodes just train the
convolutional layers, and the rest train the fully connected layers only.
Gradients and parameters of the fully connected layers no longer need to be
exchanged across the cluster, thereby substantially reducing the data transfer
volume. We implement Stanza on PyTorch and evaluate its performance on Azure
and EC2. Results show that Stanza accelerates training significantly over
current parameter server systems: on EC2 instances with Tesla V100 GPU and 10Gb
bandwidth for example, Stanza is 1.34x--13.9x faster for common deep learning
models.
Features and Machine Learning for Correlating and Classifying between Brain Areas and Dyslexia
Authors: Alex Frid, Larry M. Manevitz
Date: 2018-12-27 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), ERP

We develop a method that is based on processing gathered Event Related
Potentials (ERP) signals and the use of machine learning technique for
multivariate analysis (i.e. classification) that we apply in order to analyze
the differences between Dyslexic and Skilled readers.
  No human intervention is needed in the analysis process. This is the state of
the art results for automatic identification of Dyslexic readers using a
Lexical Decision Task. We use mathematical and machine learning based
techniques to automatically discover novel complex features that (i) allow for
reliable distinction between Dyslexic and Normal Control Skilled readers and
(ii) to validate the assumption that the most of the differences between
Dyslexic and Skilled readers located in the left hemisphere.
  Interestingly, these tools also pointed to the fact that High Pass signals
(typically considered as "noise" during ERP/EEG analyses) in fact contains
significant relevant information. Finally, the proposed scheme can be used for
analysis of any ERP based studies.
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery
Authors: Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying
Date: 2018-12-27 00:00:00
Tasks: Dimensionality Reduction

This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI). Each temporal-domain MR image is viewed as a point that lies onto or close to a smooth manifold, and landmark points are identified to describe the point cloud concisely. To facilitate computations, a dimensionality reduction module generates low-dimensional/compressed renditions of the landmark points. Recovery of the high-fidelity MRI data is realized by solving a non-convex minimization task for the linear decompression operator and those affine combinations of landmark points which locally approximate the latent manifold geometry. An algorithm with guaranteed convergence to stationary solutions of the non-convex minimization task is also provided. The aforementioned framework exploits the underlying spatio-temporal patterns and geometry of the acquired data without any prior training on external data or information. Extensive numerical results on simulated as well as real cardiac-cine and perfusion MRI data illustrate noteworthy improvements of the advocated machine-learning framework over state-of-the-art reconstruction techniques.
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Authors: Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song
Date: 2018-12-27 00:00:00
Tasks: Generative Adversarial Network, Model-based Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a novel model-based reinforcement learning framework for recommendation systems, where we develop a generative adversarial network to imitate user behavior dynamics and learn her reward function. Using this user model as the simulation environment, we develop a novel Cascading DQN algorithm to obtain a combinatorial recommendation policy which can handle a large number of candidate items efficiently. In our experiments with real data, we show this generative adversarial user model can better explain user behavior than alternatives, and the RL policy based on this model can lead to a better long-term reward for the user and higher click rate for the system.
Double Neural Counterfactual Regret Minimization
Authors: Hui Li, Kailiang Hu, Zhibang Ge, Tao Jiang, Yuan Qi, Le Song
Date: 2018-12-27 00:00:00
Proceeding: ICLR 2020 1
Tasks: counterfactual, Reinforcement Learning

Counterfactual Regret Minimization (CRF) is a fundamental and effective
technique for solving Imperfect Information Games (IIG). However, the original
CRF algorithm only works for discrete state and action spaces, and the
resulting strategy is maintained as a tabular representation. Such tabular
representation limits the method from being directly applied to large games and
continuing to improve from a poor strategy profile. In this paper, we propose a
double neural representation for the imperfect information games, where one
neural network represents the cumulative regret, and the other represents the
average strategy. Furthermore, we adopt the counterfactual regret minimization
algorithm to optimize this double neural representation. To make neural
learning efficient, we also developed several novel techniques including a
robust sampling method, mini-batch Monte Carlo Counterfactual Regret
Minimization (MCCFR) and Monte Carlo Counterfactual Regret Minimization Plus
(MCCFR+) which may be of independent interests. Experimentally, we demonstrate
that the proposed double neural algorithm converges significantly better than
the reinforcement learning counterpart.
Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction
Authors: Yujin Yuan, Liyuan Liu, Siliang Tang, Zhongfei Zhang, Yueting Zhuang, ShiLiang Pu, Fei Wu, Xiang Ren
Date: 2018-12-27 00:00:00
Tasks: Relation, Relation Extraction, Sentence

Distant supervision leverages knowledge bases to automatically label
instances, thus allowing us to train relation extractor without human
annotations. However, the generated training data typically contain massive
noise, and may result in poor performances with the vanilla supervised
learning. In this paper, we propose to conduct multi-instance learning with a
novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to
noise-robust training for distant supervised relation extractor. Specifically,
we employ the sentence-level selective attention to reduce the effect of noisy
or mismatched sentences, while the correlation among relations were captured to
improve the quality of attention weights. Moreover, instead of treating all
entity-pairs equally, we try to pay more attention to entity-pairs with a
higher quality. Similarly, we adopt the selective attention mechanism to
achieve this goal. Experiments with two types of relation extractor demonstrate
the superiority of the proposed approach over the state-of-the-art, while
further ablation studies verify our intuitions and demonstrate the
effectiveness of our proposed two techniques.
Deep Learning based Early Detection and Grading of Diabetic Retinopathy Using Retinal Fundus Images
Authors: Sheikh Muhammad Saiful Islam, Md Mahedi Hasan, Sohaib Abdullah
Date: 2018-12-27 00:00:00
Tasks: Specificity

Diabetic Retinopathy (DR) is a constantly deteriorating disease, being one of
the leading causes of vision impairment and blindness. Subtle distinction among
different grades and existence of many significant small features make the task
of recognition very challenging. In addition, the present approach of
retinopathy detection is a very laborious and time-intensive task, which
heavily relies on the skill of a physician. Automated detection of diabetic
retinopathy is essential to tackle these problems. Early-stage detection of
diabetic retinopathy is also very important for diagnosis, which can prevent
blindness with proper treatment. In this paper, we developed a novel deep
convolutional neural network, which performs the early-stage detection by
identifying all microaneurysms (MAs), the first signs of DR, along with
correctly assigning labels to retinal fundus images which are graded into five
categories. We have tested our network on the largest publicly available Kaggle
diabetic retinopathy dataset, and achieved 0.851 quadratic weighted kappa score
and 0.844 AUC score, which achieves the state-of-the-art performance on
severity grading. In the early-stage detection, we have achieved a sensitivity
of 98% and specificity of above 94%, which demonstrates the effectiveness of
our proposed method. Our proposed architecture is at the same time very simple
and efficient with respect to computational time and space are concerned.
Learning Dynamic Generator Model by Alternating Back-Propagation Through Time
Authors: Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu
Date: 2018-12-27 00:00:00

This paper studies the dynamic generator model for spatial-temporal processes
such as dynamic textures and action sequences in video data. In this model,
each time frame of the video sequence is generated by a generator model, which
is a non-linear transformation of a latent state vector, where the non-linear
transformation is parametrized by a top-down neural network. The sequence of
latent state vectors follows a non-linear auto-regressive model, where the
state vector of the next frame is a non-linear transformation of the state
vector of the current frame as well as an independent noise vector that
provides randomness in the transition. The non-linear transformation of this
transition model can be parametrized by a feedforward neural network. We show
that this model can be learned by an alternating back-propagation through time
algorithm that iteratively samples the noise vectors and updates the parameters
in the transition model and the generator model. We show that our training
method can learn realistic models for dynamic textures and action patterns.
Towards effective AI-powered agile project management
Authors: Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei
Date: 2018-12-27 00:00:00
Tasks: Management

The rise of Artificial intelligence (AI) has the potential to significantly
transform the practice of project management. Project management has a large
socio-technical element with many uncertainties arising from variability in
human aspects e.g., customers' needs, developers' performance and team
dynamics. AI can assist project managers and team members by automating
repetitive, high-volume tasks to enable project analytics for estimation and
risk prediction, providing actionable recommendations, and even making
decisions. AI is potentially a game changer for project management in helping
to accelerate productivity and increase project success rates. In this paper,
we propose a framework where AI technologies can be leveraged to offer support
for managing agile projects, which have become increasingly popular in the
industry.
Deconfounding Reinforcement Learning in Observational Settings
Authors: Chaochao Lu, Bernhard Schölkopf, José Miguel Hernández-Lobato
Date: 2018-12-26 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a general formulation for addressing reinforcement learning (RL)
problems in settings with observational data. That is, we consider the problem
of learning good policies solely from historical data in which unobserved
factors (confounders) affect both observed actions and rewards. Our formulation
allows us to extend a representative RL algorithm, the Actor-Critic method, to
its deconfounding variant, with the methodology for this extension being easily
applied to other RL algorithms. In addition to this, we develop a new benchmark
for evaluating deconfounding RL algorithms by modifying the OpenAI Gym
environments and the MNIST dataset. Using this benchmark, we demonstrate that
the proposed algorithms are superior to traditional RL methods in confounded
environments with observational data. To the best of our knowledge, this is the
first time that confounders are taken into consideration for addressing full RL
problems with observational data. Code is available at
https://github.com/CausalRL/DRL.
BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
Authors: Yongjoo Park, Jingyi Qing, Xiaoyang Shen, Barzan Mozafari
Date: 2018-12-26 00:00:00
Tasks: Decision Making, regression

The rising volume of datasets has made training machine learning (ML) models
a major computational cost in the enterprise. Given the iterative nature of
model and parameter tuning, many analysts use a small sample of their entire
data during their initial stage of analysis to make quick decisions (e.g., what
features or hyperparameters to use) and use the entire dataset only in later
stages (i.e., when they have converged to a specific model). This sampling,
however, is performed in an ad-hoc fashion. Most practitioners cannot precisely
capture the effect of sampling on the quality of their model, and eventually on
their decision-making process during the tuning phase. Moreover, without
systematic support for sampling operators, many optimizations and reuse
opportunities are lost.
  In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML
training. BlinkML allows users to make error-computation tradeoffs: instead of
training a model on their full data (i.e., full model), BlinkML can quickly
train an approximate model with quality guarantees using a sample. The quality
guarantees ensure that, with high probability, the approximate model makes the
same predictions as the full model. BlinkML currently supports any ML model
that relies on maximum likelihood estimation (MLE), which includes Generalized
Linear Models (e.g., linear regression, logistic regression, max entropy
classifier, Poisson regression) as well as PPCA (Probabilistic Principal
Component Analysis). Our experiments show that BlinkML can speed up the
training of large-scale ML tasks by 6.26x-629x while guaranteeing the same
predictions, with 95% probability, as the full model.
Deception Detection by 2D-to-3D Face Reconstruction from Videos
Authors: Minh Ngô, Burak Mandira, Selim Fırat Yılmaz, Ward Heij, Sezer Karaoglu, Henri Bouma, Hamdi Dibeklioglu, Theo Gevers
Date: 2018-12-26 00:00:00
Tasks: 3D Face Reconstruction, Deception Detection, Face Reconstruction

Lies and deception are common phenomena in society, both in our private and
professional lives. However, humans are notoriously bad at accurate deception
detection. Based on the literature, human accuracy of distinguishing between
lies and truthful statements is 54% on average, in other words it is slightly
better than a random guess. While people do not much care about this issue, in
high-stakes situations such as interrogations for series crimes and for
evaluating the testimonies in court cases, accurate deception detection methods
are highly desirable. To achieve a reliable, covert, and non-invasive deception
detection, we propose a novel method that jointly extracts reliable low- and
high-level facial features namely, 3D facial geometry, skin reflectance,
expression, head pose, and scene illumination in a video sequence. Then these
features are modeled using a Recurrent Neural Network to learn temporal
characteristics of deceptive and honest behavior. We evaluate the proposed
method on the Real-Life Trial (RLT) dataset that contains high-stake deceptive
and honest videos recorded in courtrooms. Our results show that the proposed
method (with an accuracy of 72.8%) improves the state of the art as well as
outperforming the use of manually coded facial attributes 67.6%) in deception
detection.
Solving Archaeological Puzzles
Authors: Niv Derech, Ayellet Tal, Ilan Shimshoni
Date: 2018-12-26 00:00:00
Tasks: valid

Puzzle solving is a difficult problem in its own right, even when the pieces
are all square and build up a natural image. But what if these ideal conditions
do not hold? One such application domain is archaeology, where restoring an
artifact from its fragments is highly important. From the point of view of
computer vision, archaeological puzzle solving is very challenging, due to
three additional difficulties: the fragments are of general shape; they are
abraded, especially at the boundaries (where the strongest cues for matching
should exist); and the domain of valid transformations between the pieces is
continuous. The key contribution of this paper is a fully-automatic and general
algorithm that addresses puzzle solving in this intriguing domain. We show that
our state-of-the-art approach manages to correctly reassemble dozens of broken
artifacts and frescoes.
Generalized Score Matching for Non-Negative Data
Authors: Shiqing Yu, Mathias Drton, Ali Shojaie
Date: 2018-12-26 00:00:00
Tasks: Numerical Integration

A common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant. While in such cases maximum likelihood estimation may be implemented using numerical integration, the approach becomes computationally intensive. The score matching method of Hyv\"arinen [2005] avoids direct calculation of the normalizing constant and yields closed-form estimates for exponential families of continuous distributions over $\mathbb{R}^m$. Hyv\"arinen [2007] extended the approach to distributions supported on the non-negative orthant, $\mathbb{R}_+^m$. In this paper, we give a generalized form of score matching for non-negative data that improves estimation efficiency. As an example, we consider a general class of pairwise interaction models. Addressing an overlooked inexistence problem, we generalize the regularized score matching method of Lin et al. [2016] and improve its theoretical guarantees for non-negative Gaussian graphical models.
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
Authors: Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio A. Velastin
Date: 2018-12-26 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Action Recognition In Videos, Temporal Action Localization

Recognizing human actions in untrimmed videos is an important challenging
task. An effective 3D motion representation and a powerful learning model are
two key factors influencing recognition performance. In this paper we introduce
a new skeleton-based representation for 3D action recognition in videos. The
key idea of the proposed representation is to transform 3D joint coordinates of
the human body carried in skeleton sequences into RGB images via a color
encoding process. By normalizing the 3D joint coordinates and dividing each
skeleton frame into five parts, where the joints are concatenated according to
the order of their physical connections, the color-coded representation is able
to represent spatio-temporal evolutions of complex 3D motions, independently of
the length of each sequence. We then design and train different Deep
Convolutional Neural Networks (D-CNNs) based on the Residual Network
architecture (ResNet) on the obtained image-based representations to learn 3D
motion features and classify them into classes. Our method is evaluated on two
widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very
large-scale dataset for 3D human action recognition. The experimental results
demonstrate that the proposed method outperforms previous state-of-the-art
approaches whilst requiring less computation for training and prediction.
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
Authors: Aditya Grover, Stefano Ermon
Date: 2018-12-26 00:00:00
Tasks: compressed sensing, Dimensionality Reduction, Representation Learning

Compressed sensing techniques enable efficient acquisition and recovery of
sparse, high-dimensional data signals via low-dimensional projections. In this
work, we propose Uncertainty Autoencoders, a learning framework for
unsupervised representation learning inspired by compressed sensing. We treat
the low-dimensional projections as noisy latent representations of an
autoencoder and directly learn both the acquisition (i.e., encoding) and
amortized recovery (i.e., decoding) procedures. Our learning objective
optimizes for a tractable variational lower bound to the mutual information
between the datapoints and the latent representations. We show how our
framework provides a unified treatment to several lines of research in
dimensionality reduction, compressed sensing, and generative modeling.
Empirically, we demonstrate a 32% improvement on average over competing
approaches for the task of statistical compressed sensing of high-dimensional
datasets.