pwc-archive/papers-with-abstracts huggingface dataset:
Learning Time-Sensitive Strategies in Space Fortress
Authors: Akshat Agarwal, Ryan Hope, Katia Sycara
Date: 2018-05-17 00:00:00
Tasks: Atari Games, Reinforcement Learning, Reinforcement Learning (RL), Space Fortress, Transfer Learning

Although there has been remarkable progress and impressive performance on
reinforcement learning (RL) on Atari games, there are many problems with
challenging characteristics that have not yet been explored in Deep Learning
for RL. These include reward sparsity, abrupt context-dependent reversals of
strategy and time-sensitive game play. In this paper, we present Space
Fortress, a game that incorporates all these characteristics and experimentally
show that the presence of any of these renders state of the art Deep RL
algorithms incapable of learning. Then, we present our enhancements to an
existing algorithm and show big performance increases through each enhancement
through an ablation study. We discuss how each of these enhancements was able
to help and also argue that appropriate transfer learning boosts performance.
Multi-level 3D CNN for Learning Multi-scale Spatial Features
Authors: Sambit Ghadai, Xian Lee, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy
Date: 2018-05-30 00:00:00
Tasks: 3D Object Recognition, Object, Object Recognition

3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and octrees) or from unstructured representations (graphs and point clouds). Learning features from such structured representations is limited by the restriction on resolution and tree depth while unstructured representations creates a challenge due to non-uniformity among data samples. In this paper, we propose an end-to-end multi-level learning approach on a multi-level voxel grid to overcome these drawbacks. To demonstrate the utility of the proposed multi-level learning, we use a multi-level voxel representation of 3D objects to perform object recognition. The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object. In addition, each voxel in the coarse grid that contains a portion of the object boundary is subdivided into multiple fine-level voxel grids. The performance of our multi-level learning algorithm for object recognition is comparable to dense voxel representations while using significantly lower memory.
Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level
Authors: AbdelRahim Elmadany, Sherif Abdou, Mervat Gheith
Date: 2018-05-30 00:00:00
Proceeding: LREC 2018 5
Tasks: Dialogue Act Classification, General Classification

The ability to model and automatically detect dialogue act is an important
step toward understanding spontaneous speech and Instant Messages. However, it
has been difficult to infer a dialogue act from a surface utterance because it
highly depends on the context of the utterance and speaker linguistic
knowledge; especially in Arabic dialects. This paper proposes a statistical
dialogue analysis model to recognize utterance's dialogue acts using a
multi-classes hierarchical structure. The model can automatically acquire
probabilistic discourse knowledge from a dialogue corpus were collected and
annotated manually from multi-genre Egyptian call-centers. Extensive
experiments were conducted using Support Vector Machines classifier to evaluate
the system performance. The results attained in the term of average F-measure
scores of 0.912; showed that the proposed approach has moderately improved
F-measure by approximately 20%.
Mining gold from implicit models to improve likelihood-free inference
Authors: Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer
Date: 2018-05-30 00:00:00

Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.
Novel Video Prediction for Large-scale Scene using Optical Flow
Authors: Henglai Wei, Xiaochuan Yin, Penghong Lin
Date: 2018-05-30 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation, Prediction, Video Prediction

Making predictions of future frames is a critical challenge in autonomous
driving research. Most of the existing methods for video prediction attempt to
generate future frames in simple and fixed scenes. In this paper, we propose a
novel and effective optical flow conditioned method for the task of video
prediction with an application to complex urban scenes. In contrast with
previous work, the prediction model only requires video sequences and optical
flow sequences for training and testing. Our method uses the rich
spatial-temporal features in video sequences. The method takes advantage of the
motion information extracting from optical flow maps between neighbor images as
well as previous images. Empirical evaluations on the KITTI dataset and the
Cityscapes dataset demonstrate the effectiveness of our method.
How Important Is a Neuron?
Authors: Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan
Date: 2018-05-30 00:00:00
Tasks: feature selection, Sentiment Analysis

The problem of attributing a deep network's prediction to its
\emph{input/base} features is well-studied. We introduce the notion of
\emph{conductance} to extend the notion of attribution to the understanding the
importance of \emph{hidden} units.
  Informally, the conductance of a hidden unit of a deep network is the
\emph{flow} of attribution via this hidden unit. We use conductance to
understand the importance of a hidden unit to the prediction for a specific
input, or over a set of inputs. We evaluate the effectiveness of conductance in
multiple ways, including theoretical properties, ablation studies, and a
feature selection task. The empirical evaluations are done using the Inception
network over ImageNet data, and a sentiment analysis network over reviews. In
both cases, we demonstrate the effectiveness of conductance in identifying
interesting insights about the internal workings of these networks.
State Space Gaussian Processes with Non-Gaussian Likelihood
Authors: Hannes Nickisch, Arno Solin, Alexander Grigorievskiy
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Gaussian Processes, regression

We provide a comprehensive overview and tooling for GP modeling with
non-Gaussian likelihoods using state space methods. The state space formulation
allows for solving one-dimensional GP models in $\mathcal{O}(n)$ time and
memory complexity. While existing literature has focused on the connection
between GP regression and state space methods, the computational primitives
allowing for inference using general likelihoods in combination with the
Laplace approximation (LA), variational Bayes (VB), and assumed density
filtering (ADF, a.k.a. single-sweep expectation propagation, EP) schemes has
been largely overlooked. We present means of combining the efficient
$\mathcal{O}(n)$ state space methodology with existing inference methods. We
extend existing methods, and provide unifying code implementing all approaches.
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations
Authors: Bohao Huang, Daniel Reichman, Leslie M. Collins, Kyle Bradbury, Jordan M. Malof
Date: 2018-05-30 00:00:00
Tasks: Segmentation Of Remote Sensing Imagery, Semantic Segmentation

In this work we consider the application of convolutional neural networks
(CNNs) for pixel-wise labeling (a.k.a., semantic segmentation) of remote
sensing imagery (e.g., aerial color or hyperspectral imagery). Remote sensing
imagery is usually stored in the form of very large images, referred to as
"tiles", which are too large to be segmented directly using most CNNs and their
associated hardware. As a result, during label inference, smaller sub-images,
called "patches", are processed individually and then "stitched" (concatenated)
back together to create a tile-sized label map. This approach suffers from
computational ineffiency and can result in discontinuities at output
boundaries. We propose a simple alternative approach in which the input size of
the CNN is dramatically increased only during label inference. This does not
avoid stitching altogether, but substantially mitigates its limitations. We
evaluate the performance of the proposed approach against a vonventional
stitching approach using two popular segmentation CNN models and two
large-scale remote sensing imagery datasets. The results suggest that the
proposed approach substantially reduces label inference time, while also
yielding modest overall label accuracy increases. This approach contributed to
our wining entry (overall performance) in the INRIA building labeling
competition.
Convolutional Embedded Networks for Population Scale Clustering and Bio-ancestry Inferencing
Authors: Md. Rezaul Karim, Michael Cochez, Achille Zappa, Ratnesh Sahay, Oya Beyan, Dietrich-Rebholz Schuhmann, Stefan Decker
Date: 2018-05-30 00:00:00
Tasks: Clustering, feature selection, Representation Learning

The study of genetic variants can help find correlating population groups to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation, deep neural networks can learn non-linear mappings that allow transforming GVs data into more clustering and classification friendly representations than manual feature selection. In this paper, we proposed convolutional embedded networks in which we combine two DNN architectures called convolutional embedded clustering and convolutional autoencoder classifier for clustering individuals and predicting geographic ethnicity based on GVs, respectively. We employed CAE-based representation learning on 95 million GVs from the 1000 genomes and Simons genome diversity projects. Quantitative and qualitative analyses with a focus on accuracy and scalability show that our approach outperforms state-of-the-art approaches such as VariantSpark and ADMIXTURE. In particular, CEC can cluster targeted population groups in 22 hours with an adjusted rand index of 0.915, the normalized mutual information of 0.92, and the clustering accuracy of 89%. Contrarily, the CAE classifier can predict the geographic ethnicity of unknown samples with an F1 and Mathews correlation coefficient(MCC) score of 0.9004 and 0.8245, respectively. To provide interpretations of the predictions, we identify significant biomarkers using gradient boosted trees(GBT) and SHAP. Overall, our approach is transparent and faster than the baseline methods, and scalable for 5% to 100% of the full human genome.
A Web-scale system for scientific knowledge exploration
Authors: Zhihong Shen, Hao Ma, Kuansan Wang
Date: 2018-05-30 00:00:00
Proceeding: ACL 2018 7
Tasks: Efficient Exploration, TAG

To enable efficient exploration of Web-scale scientific knowledge, it is
necessary to organize scientific publications into a hierarchical concept
structure. In this work, we present a large-scale system to (1) identify
hundreds of thousands of scientific concepts, (2) tag these identified concepts
to hundreds of millions of scientific publications by leveraging both text and
graph structure, and (3) build a six-level concept hierarchy with a
subsumption-based model. The system builds the most comprehensive cross-domain
scientific concept ontology published to date, with more than 200 thousand
concepts and over one million relationships.
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
Authors: Michael Bernico, Yuntao Li, Dingchao Zhang
Date: 2017-12-11 00:00:00
Tasks: Transfer Learning

Transfer learning allows practitioners to recognize and apply knowledge
learned in previous tasks (source task) to new tasks or new domains (target
task), which share some commonality. The two important factors impacting the
performance of transfer learning models are: (a) the size of the target
dataset, and (b) the similarity in distribution between source and target
domains. Thus far, there has been little investigation into just how important
these factors are. In this paper, we investigate the impact of target dataset
size and source/target domain similarity on model performance through a series
of experiments. We find that more data is always beneficial, and model
performance improves linearly with the log of data size, until we are out of
data. As source/target domains differ, more data is required and fine tuning
will render better performance than feature extraction. When source/target
domains are similar and data size is small, fine tuning and feature extraction
renders equivalent performance. Our hope is that by beginning this quantitative
investigation on the effect of data volume and domain similarity in transfer
learning we might inspire others to explore the significance of data in
developing more accurate statistical models.
Too Fast Causal Inference under Causal Insufficiency
Authors: Mieczysław A. Kłopotek
Date: 2018-05-30 00:00:00
Tasks: Causal Inference

Causally insufficient structures (models with latent or hidden variables, or
with confounding etc.) of joint probability distributions have been subject of
intense study not only in statistics, but also in various AI systems. In AI,
belief networks, being representations of joint probability distribution with
an underlying directed acyclic graph structure, are paid special attention due
to the fact that efficient reasoning (uncertainty propagation) methods have
been developed for belief network structures. Algorithms have been therefore
developed to acquire the belief network structure from data. As artifacts due
to variable hiding negatively influence the performance of derived belief
networks, models with latent variables have been studied and several algorithms
for learning belief network structure under causal insufficiency have also been
developed.
  Regrettably, some of them are known already to be erroneous (e.g. IC
algorithm of [Pearl:Verma:91]. This paper is devoted to another algorithm, the
Fast Causal Inference (FCI) Algorithm of [Spirtes:93]. It is proven by a
specially constructed example that this algorithm, as it stands in
[Spirtes:93], is also erroneous. Fundamental reason for failure of this
algorithm is the temporary introduction of non-real links between nodes of the
network with the intention of later removal. While for trivial dependency
structures these non-real links may be actually removed, this may not be the
case for complex ones, e.g. for the case described in this paper. A remedy of
this failure is proposed.
Applications of Trajectory Data from the Perspective of a Road Transportation Agency: Literature Review and Maryland Case Study
Authors: Nikola Marković, Przemysław Sekuła, Zachary Vander Laan, Gennady Andrienko, Natalia Andrienko
Date: 2017-08-23 00:00:00
Tasks: Decision Making

Transportation agencies have an opportunity to leverage
increasingly-available trajectory datasets to improve their analyses and
decision-making processes. However, this data is typically purchased from
vendors, which means agencies must understand its potential benefits beforehand
in order to properly assess its value relative to the cost of acquisition.
While the literature concerned with trajectory data is rich, it is naturally
fragmented and focused on technical contributions in niche areas, which makes
it difficult for government agencies to assess its value across different
transportation domains. To overcome this issue, the current paper explores
trajectory data from the perspective of a road transportation agency interested
in acquiring trajectories to enhance its analyses. The paper provides a
literature review illustrating applications of trajectory data in six areas of
road transportation systems analysis: demand estimation, modeling human
behavior, designing public transit, traffic performance measurement and
prediction, environment and safety. In addition, it visually explores 20
million GPS traces in Maryland, illustrating existing and suggesting new
applications of trajectory data.
An Extended Beta-Elliptic Model and Fuzzy Elementary Perceptual Codes for Online Multilingual Writer Identification using Deep Neural Network
Authors: Thameur Dhieb, Sourour Njah, Houcine Boubaker, Wael Ouarda, Mounir Ben Ayed, Adel M. ALIMI
Date: 2018-04-16 00:00:00
Tasks: Position

Actually, the ability to identify the documents authors provides more chances
for using these documents for various purposes. In this paper, we present a new
effective biometric writer identification system from online handwriting. The
system consists of the preprocessing and the segmentation of online handwriting
into a sequence of Beta strokes in a first step. Then, from each stroke, we
extract a set of static and dynamic features from new proposed model that we
called Extended Beta-Elliptic model and from the Fuzzy Elementary Perceptual
Codes. Next, all the segments which are composed of N consecutive strokes are
categorized into groups and subgroups according to their position and their
geometric characteristics. Finally, Deep Neural Network is used as classifier.
Experimental results reveal that the proposed system achieves interesting
results as compared to those of the existing writer identification systems on
Latin and Arabic scripts.
Extracting Scientific Figures with Distantly Supervised Neural Networks
Authors: Noah Siegel, Nicholas Lourie, Russell Power, Waleed Ammar
Date: 2018-04-06 00:00:00

Non-textual components such as charts, diagrams and tables provide key
information in many scientific documents, but the lack of large labeled
datasets has impeded the development of data-driven methods for scientific
figure extraction. In this paper, we induce high-quality training labels for
the task of figure extraction in a large number of scientific documents, with
no human intervention. To accomplish this we leverage the auxiliary data
provided in two large web collections of scientific documents (arXiv and
PubMed) to locate figures and their associated captions in the rasterized PDF.
We share the resulting dataset of over 5.5 million induced labels---4,000 times
larger than the previous largest figure extraction dataset---with an average
precision of 96.8%, to enable the development of modern data-driven methods for
this task. We use this dataset to train a deep neural network for end-to-end
figure detection, yielding a model that can be more easily extended to new
domains compared to previous work. The model was successfully deployed in
Semantic Scholar, a large-scale academic search engine, and used to extract
figures in 13 million scientific documents.
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Authors: Kang Liu, Brendan Dolan-Gavitt, Siddharth Garg
Date: 2018-05-30 00:00:00

Deep neural networks (DNNs) provide excellent performance across a wide range
of classification tasks, but their training requires high computational
resources and is often outsourced to third parties. Recent work has shown that
outsourced training introduces the risk that a malicious trainer will return a
backdoored DNN that behaves normally on most inputs but causes targeted
misclassifications or degrades the accuracy of the network when a trigger known
only to the attacker is present. In this paper, we provide the first effective
defenses against backdoor attacks on DNNs. We implement three backdoor attacks
from prior work and use them to investigate two promising defenses, pruning and
fine-tuning. We show that neither, by itself, is sufficient to defend against
sophisticated attackers. We then evaluate fine-pruning, a combination of
pruning and fine-tuning, and show that it successfully weakens or even
eliminates the backdoors, i.e., in some cases reducing the attack success rate
to 0% with only a 0.4% drop in accuracy for clean (non-triggering) inputs. Our
work provides the first step toward defenses against backdoor attacks in deep
neural networks.
Context Exploitation using Hierarchical Bayesian Models
Authors: Christopher A. George, Pranab Banerjee, Kendra E. Moore
Date: 2018-05-30 00:00:00
Tasks: General Classification, Specificity

We consider the problem of how to improve automatic target recognition by
fusing the naive sensor-level classification decisions with "intuition," or
context, in a mathematically principled way. This is a general approach that is
compatible with many definitions of context, but for specificity, we consider
context as co-occurrence in imagery. In particular, we consider images that
contain multiple objects identified at various confidence levels. We learn the
patterns of co-occurrence in each context, then use these patterns as
hyper-parameters for a Hierarchical Bayesian Model. The result is that
low-confidence sensor classification decisions can be dramatically improved by
fusing those readings with context. We further use hyperpriors to address the
case where multiple contexts may be appropriate. We also consider the Bayesian
Network, an alternative to the Hierarchical Bayesian Model, which is
computationally more efficient but assumes that context and sensor readings are
uncorrelated.
Why do deep convolutional networks generalize so poorly to small image transformations?
Authors: Aharon Azulay, Yair Weiss
Date: 2018-05-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation, Object Recognition

Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation. Recently, several authors have shown that this is not the case: small translations or rescalings of the input image can drastically change the network's prediction. In this paper, we quantify this phenomena and ask why neither the convolutional architecture nor data augmentation are sufficient to achieve the desired invariance. Specifically, we show that the convolutional architecture does not give invariance since architectures ignore the classical sampling theorem, and data augmentation does not give invariance because the CNNs learn to be invariant to transformations only for images that are very similar to typical images from the training set. We discuss two possible solutions to this problem: (1) antialiasing the intermediate representations and (2) increasing data augmentation and show that they provide only a partial solution at best. Taken together, our results indicate that the problem of insuring invariance to small image transformations in neural networks while preserving high accuracy remains unsolved.