pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
Authors: Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero, Rosalind W. Picard
Date: 2017-12-01 00:00:00
Tasks: Future prediction, Gaussian Processes, regression

In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE,
ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by
learning a population-level model using multi-modal data from previously seen
patients using the base Gaussian Process (GP) regression. Then, this model is
adapted sequentially over time to a new patient using domain adaptive GPs to
form the patient's pGP. We show that this new approach, together with an
auto-regressive formulation, leads to significant improvements in forecasting
future clinical status and cognitive scores for target patients when compared
to modeling the population with traditional GPs.
Local angles and dimension estimation from data on manifolds
Authors: Mateo Díaz, Adolfo J. Quiroz, Mauricio Velasco
Date: 2018-05-04 00:00:00

For data living in a manifold $M\subseteq \mathbb{R}^m$ and a point $p\in M$
we consider a statistic $U_{k,n}$ which estimates the variance of the angle
between pairs of vectors $X_i-p$ and $X_j-p$, for data points $X_i$, $X_j$,
near $p$, and evaluate this statistic as a tool for estimation of the intrinsic
dimension of $M$ at $p$. Consistency of the local dimension estimator is
established and the asymptotic distribution of $U_{k,n}$ is found under minimal
regularity assumptions. Performance of the proposed methodology is compared
against state-of-the-art methods on simulated data.
Numerical Coordinate Regression with Convolutional Neural Networks
Authors: Aiden Nibali, Zhen He, Stuart Morgan, Luke Prendergast
Date: 2018-01-23 00:00:00
Tasks: Pose Estimation, regression

We study deep learning approaches to inferring numerical coordinates for
points of interest in an input image. Existing convolutional neural
network-based solutions to this problem either take a heatmap matching approach
or regress to coordinates with a fully connected output layer. Neither of these
approaches is ideal, since the former is not entirely differentiable, and the
latter lacks inherent spatial generalization. We propose our differentiable
spatial to numerical transform (DSNT) to fill this gap. The DSNT layer adds no
trainable parameters, is fully differentiable, and exhibits good spatial
generalization. Unlike heatmap matching, DSNT works well with low heatmap
resolutions, so it can be dropped in as an output layer for a wide range of
existing fully convolutional architectures. Consequently, DSNT offers a better
trade-off between inference speed and prediction accuracy compared to existing
techniques. When used to replace the popular heatmap matching approach used in
almost all state-of-the-art methods for pose estimation, DSNT gives better
prediction accuracy for all model architectures tested.
Pixel-wise Attentional Gating for Parsimonious Pixel Labeling
Authors: Shu Kong, Charless Fowlkes
Date: 2018-05-03 00:00:00
Tasks: Boundary Detection, Semantic Segmentation, Surface Normal Estimation

To achieve parsimonious inference in per-pixel labeling tasks with a limited
computational budget, we propose a \emph{Pixel-wise Attentional Gating} unit
(\emph{PAG}) that learns to selectively process a subset of spatial locations
at each layer of a deep convolutional network. PAG is a generic,
architecture-independent, problem-agnostic mechanism that can be readily
"plugged in" to an existing model with fine-tuning. We utilize PAG in two ways:
1) learning spatially varying pooling fields that improve model performance
without the extra computation cost associated with multi-scale pooling, and 2)
learning a dynamic computation policy for each pixel to decrease total
computation while maintaining accuracy.
  We extensively evaluate PAG on a variety of per-pixel labeling tasks,
including semantic segmentation, boundary detection, monocular depth and
surface normal estimation. We demonstrate that PAG allows competitive or
state-of-the-art performance on these tasks. Our experiments show that PAG
learns dynamic spatial allocation of computation over the input image which
provides better performance trade-offs compared to related approaches (e.g.,
truncating deep models or dynamically skipping whole layers). Generally, we
observe PAG can reduce computation by $10\%$ without noticeable loss in
accuracy and performance degrades gracefully when imposing stronger
computational constraints.
Dictionary Learning and Sparse Coding on Statistical Manifolds
Authors: Rudrasis Chakraborty, Monami Banerjee, Baba C. Vemuri
Date: 2018-05-03 00:00:00
Tasks: Dictionary Learning, General Classification

In this paper, we propose a novel information theoretic framework for
dictionary learning (DL) and sparse coding (SC) on a statistical manifold (the
manifold of probability distributions). Unlike the traditional DL and SC
framework, our new formulation does not explicitly incorporate any sparsity
inducing norm in the cost function being optimized but yet yields sparse codes.
Our algorithm approximates the data points on the statistical manifold (which
are probability distributions) by the weighted Kullback-Leibeler center/mean
(KL-center) of the dictionary atoms. The KL-center is defined as the minimizer
of the maximum KL-divergence between itself and members of the set whose center
is being sought. Further, we prove that the weighted KL-center is a sparse
combination of the dictionary atoms. This result also holds for the case when
the KL-divergence is replaced by the well known Hellinger distance. From an
applications perspective, we present an extension of the aforementioned
framework to the manifold of symmetric positive definite matrices (which can be
identified with the manifold of zero mean gaussian distributions),
$\mathcal{P}_n$. We present experiments involving a variety of dictionary-based
reconstruction and classification problems in Computer Vision. Performance of
the proposed algorithm is demonstrated by comparing it to several
state-of-the-art methods in terms of reconstruction and classification accuracy
as well as sparsity of the chosen representation.
An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking
Authors: Puyang Xu, Qi Hu
Date: 2018-05-03 00:00:00
Proceeding: ACL 2018 7
Tasks: Dialogue State Tracking, Spoken Language Understanding

We highlight a practical yet rarely discussed problem in dialogue state
tracking (DST), namely handling unknown slot values. Previous approaches
generally assume predefined candidate lists and thus are not designed to output
unknown values, especially when the spoken language understanding (SLU) module
is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E
architecture based on the pointer network (PtrNet) that can effectively extract
unknown slot values while still obtains state-of-the-art accuracy on the
standard DSTC2 benchmark. We also provide extensive empirical evidence to show
that tracking unknown values can be challenging and our approach can bring
significant improvement with the help of an effective feature dropout
technique.
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing
Authors: Minh Nguyen, Toan Nguyen, Thien Huu Nguyen
Date: 2018-05-03 00:00:00
Tasks: Sentence, Text Categorization

Anti-phishing aims to detect phishing content/documents in a pool of textual
data. This is an important problem in cybersecurity that can help to guard
users from fraudulent information. Natural language processing (NLP) offers a
natural solution for this problem as it is capable of analyzing the textual
content to perform intelligent recognition. In this work, we investigate
state-of-the-art techniques for text categorization in NLP to address the
problem of anti-phishing for emails (i.e, predicting if an email is phishing or
not). These techniques are based on deep learning models that have attracted
much attention from the community recently. In particular, we present a
framework with hierarchical long short-term memory networks (H-LSTMs) and
attention mechanisms to model the emails simultaneously at the word and the
sentence level. Our expectation is to produce an effective model for
anti-phishing and demonstrate the effectiveness of deep learning for problems
in cybersecurity.
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents
Authors: Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas
Date: 2018-05-03 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Understanding, Transfer Learning

Fast expansion of natural language functionality of intelligent virtual
agents is critical for achieving engaging and informative interactions.
However, developing accurate models for new natural language domains is a time
and data intensive process. We propose efficient deep neural network
architectures that maximally re-use available resources through transfer
learning. Our methods are applied for expanding the understanding capabilities
of a popular commercial agent and are evaluated on hundreds of new domains,
designed by internal or external developers. We demonstrate that our proposed
methods significantly increase accuracy in low resource settings and enable
rapid development of accurate models with less data.
Lifted Neural Networks
Authors: Armin Askari, Geoffrey Negiar, Rajiv Sambharya, Laurent El Ghaoui
Date: 2018-05-03 00:00:00

We describe a novel family of models of multi- layer feedforward neural
networks in which the activation functions are encoded via penalties in the
training problem. Our approach is based on representing a non-decreasing
activation function as the argmin of an appropriate convex optimiza- tion
problem. The new framework allows for algo- rithms such as block-coordinate
descent methods to be applied, in which each step is composed of a simple (no
hidden layer) supervised learning problem that is parallelizable across data
points and/or layers. Experiments indicate that the pro- posed models provide
excellent initial guesses for weights for standard neural networks. In addi-
tion, the model provides avenues for interesting extensions, such as robustness
against noisy in- puts and optimizing over parameters in activation functions.
When Will AI Exceed Human Performance? Evidence from AI Experts
Authors: Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, Owain Evans
Date: 2017-05-24 00:00:00

Advances in artificial intelligence (AI) will transform modern life by
reshaping transportation, health, science, finance, and the military. To adapt
public policy, we need to better anticipate these advances. Here we report the
results from a large survey of machine learning researchers on their beliefs
about progress in AI. Researchers predict AI will outperform humans in many
activities in the next ten years, such as translating languages (by 2024),
writing high-school essays (by 2026), driving a truck (by 2027), working in
retail (by 2031), writing a bestselling book (by 2049), and working as a
surgeon (by 2053). Researchers believe there is a 50% chance of AI
outperforming humans in all tasks in 45 years and of automating all human jobs
in 120 years, with Asian respondents expecting these dates much sooner than
North Americans. These results will inform discussion amongst researchers and
policymakers about anticipating and managing trends in AI.
How deep should be the depth of convolutional neural networks: a backyard dog case study
Authors: A. N. Gorban, E. M. Mirkes, I. Y. Tyukin
Date: 2018-05-03 00:00:00
Tasks: Face Recognition

The work concerns the problem of reducing a pre-trained deep neuronal network to a smaller network, with just few layers, whilst retaining the network's functionality on a given task The proposed approach is motivated by the observation that the aim to deliver the highest accuracy possible in the broadest range of operational conditions, which many deep neural networks models strive to achieve, may not necessarily be always needed, desired, or even achievable due to the lack of data or technical constraints. In relation to the face recognition problem, we formulated an example of such a usecase, the `backyard dog' problem. The `backyard dog', implemented by a lean network, should correctly identify members from a limited group of individuals, a `family', and should distinguish between them. At the same time, the network must produce an alarm to an image of an individual who is not in a member of the family. To produce such a network, we propose a shallowing algorithm. The algorithm takes an existing deep learning model on its input and outputs a shallowed version of it. The algorithm is non-iterative and is based on the Advanced Supervised Principal Component Analysis. Performance of the algorithm is assessed in exhaustive numerical experiments. In the above usecase, the `backyard dog' problem, the method is capable of drastically reducing the depth of deep learning neural networks, albeit at the cost of mild performance deterioration. We developed a simple non-iterative method for shallowing down pre-trained deep networks. The method is generic in the sense that it applies to a broad class of feed-forward networks, and is based on the Advanced Supervise Principal Component Analysis. The method enables generation of families of smaller-size shallower specialized networks tuned for specific operational conditions and tasks from a single larger and more universal legacy network.
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings
Authors: Saba A. Al-Sayouri, Ekta Gujral, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam
Date: 2018-05-03 00:00:00
Tasks: General Classification, Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Node Classification, Representation Learning

Graph representations have increasingly grown in popularity during the last
years. Existing representation learning approaches explicitly encode network
structure. Despite their good performance in downstream processes (e.g., node
classification, link prediction), there is still room for improvement in
different aspects, like efficacy, visualization, and interpretability. In this
paper, we propose, t-PINE, a method that addresses these limitations. Contrary
to baseline methods, which generally learn explicit graph representations by
solely using an adjacency matrix, t-PINE avails a multi-view information graph,
the adjacency matrix represents the first view, and a nearest neighbor
adjacency, computed over the node features, is the second view, in order to
learn explicit and implicit node representations, using the Canonical Polyadic
(a.k.a. CP) decomposition. We argue that the implicit and the explicit mapping
from a higher-dimensional to a lower-dimensional vector space is the key to
learn more useful, highly predictable, and gracefully interpretable
representations. Having good interpretable representations provides a good
guidance to understand how each view contributes to the representation learning
process. In addition, it helps us to exclude unrelated dimensions. Extensive
experiments show that t-PINE drastically outperforms baseline methods by up to
158.6% with respect to Micro-F1, in several multi-label classification
problems, while it has high visualization and interpretability utility.
RECS: Robust Graph Embedding Using Connection Subgraphs
Authors: Saba A. Al-Sayouri, Danai Koutra, Evangelos E. Papalexakis, Sarah S. Lam
Date: 2018-05-03 00:00:00
Tasks: General Classification, Graph Embedding, Link Prediction, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Node Classification, Recommendation Systems, Representation Learning

The success of graph embeddings or node representation learning in a variety
of downstream tasks, such as node classification, link prediction, and
recommendation systems, has led to their popularity in recent years.
Representation learning algorithms aim to preserve local and global network
structure by identifying node neighborhood notions. However, many existing
algorithms generate embeddings that fail to properly preserve the network
structure, or lead to unstable representations due to random processes (e.g.,
random walks to generate context) and, thus, cannot generate to multi-graph
problems. In this paper, we propose RECS, a novel, stable graph embedding
algorithmic framework. RECS learns graph representations using connection
subgraphs by employing the analogy of graphs with electrical circuits. It
preserves both local and global connectivity patterns, and addresses the issue
of high-degree nodes. Further, it exploits the strength of weak ties and
meta-data that have been neglected by baselines. The experiments show that RECS
outperforms state-of-the-art algorithms by up to 36.85% on multi-label
classification problem. Further, in contrast to baselines, RECS, being
deterministic, is completely stable.
Noisin: Unbiased Regularization for Recurrent Neural Networks
Authors: Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei
Date: 2018-05-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Language Modeling, Language Modelling

Recurrent neural networks (RNNs) are powerful models of sequential data. They
have been successfully used in domains such as text and speech. However, RNNs
are susceptible to overfitting; regularization is important. In this paper we
develop Noisin, a new method for regularizing RNNs. Noisin injects random noise
into the hidden states of the RNN and then maximizes the corresponding marginal
likelihood of the data. We show how Noisin applies to any RNN and we study many
different types of noise. Noisin is unbiased--it preserves the underlying RNN
on average. We characterize how Noisin regularizes its RNN both theoretically
and empirically. On language modeling benchmarks, Noisin improves over dropout
by as much as 12.2% on the Penn Treebank and 9.4% on the Wikitext-2 dataset. We
also compared the state-of-the-art language model of Yang et al. 2017, both
with and without Noisin. On the Penn Treebank, the method with Noisin more
quickly reaches state-of-the-art performance.
InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games
Authors: Mohammad Shakirul Islam, Ferdouse Ahmed Foysal, Nafis Neehal, Enamul Karim, Syed Akhter Hossain
Date: 2018-05-03 00:00:00
Tasks: Decision Making, General Classification

Sports activities are an integral part of our day to day life. Introducing
autonomous decision making and predictive models to recognize and analyze
different sports events and activities has become an emerging trend in computer
vision arena. Albeit the advances and vivid applications of artificial
intelligence and computer vision in recognizing different popular western
games, there remains a very minimal amount of efforts in the application of
computer vision in recognizing traditional Bangladeshi games. We, in this
paper, have described a novel Deep Learning based approach for recognizing
traditional Bengali games. We have retrained the final layer of the renowned
Inception V3 architecture developed by Google for our classification approach.
Our approach shows promising results with an average accuracy of 80%
approximately in correctly recognizing among 5 traditional Bangladeshi sports
events.
Siamese networks for generating adversarial examples
Authors: Mandar Kulkarni, Aria Abubakar
Date: 2018-05-03 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning models are vulnerable to adversarial examples. An adversary
modifies the input data such that humans still assign the same label, however,
machine learning models misclassify it. Previous approaches in the literature
demonstrated that adversarial examples can even be generated for the remotely
hosted model. In this paper, we propose a Siamese network based approach to
generate adversarial examples for a multiclass target CNN. We assume that the
adversary do not possess any knowledge of the target data distribution, and we
use an unlabeled mismatched dataset to query the target, e.g., for the
ResNet-50 target, we use the Food-101 dataset as the query. Initially, the
target model assigns labels to the query dataset, and a Siamese network is
trained on the image pairs derived from these multiclass labels. We learn the
\emph{adversarial perturbations} for the Siamese model and show that these
perturbations are also adversarial w.r.t. the target model. In experimental
results, we demonstrate effectiveness of our approach on MNIST, CIFAR-10 and
ImageNet targets with TinyImageNet/Food-101 query datasets.
A Tidy Data Model for Natural Language Processing using cleanNLP
Authors: Taylor Arnold
Date: 2017-03-27 00:00:00
Tasks: coreference-resolution, Coreference Resolution, Dependency Parsing, Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentiment Analysis

The package cleanNLP provides a set of fast tools for converting a textual
corpus into a set of normalized tables. The underlying natural language
processing pipeline utilizes Stanford's CoreNLP library, exposing a number of
annotation tasks for text written in English, French, German, and Spanish.
Annotators include tokenization, part of speech tagging, named entity
recognition, entity linking, sentiment analysis, dependency parsing,
coreference resolution, and information extraction.
Dimensional emotion recognition using visual and textual cues
Authors: Pedro M. Ferreira, Diogo Pernes, Kelwin Fernandes, Ana Rebelo, Jaime S. Cardoso
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition

This paper addresses the problem of automatic emotion recognition in the
scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion
challenge). The underlying objective of the challenge is the automatic
estimation of emotion expressions in the two-dimensional emotion representation
space (i.e., arousal and valence). The adopted methodology is a weighted
ensemble of several models from both video and text modalities. For video-based
recognition, two different types of visual cues (i.e., face and facial
landmarks) were considered to feed a multi-input deep neural network. Regarding
the text modality, a sequential model based on a simple recurrent architecture
was implemented. In addition, we also introduce a model based on high-level
features in order to embed domain knowledge in the learning process.
Experimental results on the OMG-Emotion validation set demonstrate the
effectiveness of the implemented ensemble model as it clearly outperforms the
current baseline methods.
Consequence-based Reasoning for Description Logics with Disjunction, Inverse Roles, Number Restrictions, and Nominals
Authors: David Tena Cucala, Bernardo Cuenca Grau, Ian Horrocks
Date: 2018-05-03 00:00:00
Tasks: All, General Classification

We present a consequence-based calculus for concept subsumption and
classification in the description logic ALCHOIQ, which extends ALC with role
hierarchies, inverse roles, number restrictions, and nominals. By using
standard transformations, our calculus extends to SROIQ, which covers all of
OWL 2 DL except for datatypes. A key feature of our calculus is its
pay-as-you-go behaviour: unlike existing algorithms, our calculus is worst-case
optimal for all the well-known proper fragments of ALCHOIQ, albeit not for the
full logic.