pwc-archive/papers-with-abstracts huggingface dataset:
The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students
Authors: Eda Okur, Sinem Aslan, Nese Alyuz, Asli Arslan Esme, Ryan S. Baker
Date: 2019-01-12 00:00:00
Tasks: Open-Ended Question Answering

The development of real-time affect detection models often depends upon
obtaining annotated data for supervised learning by employing human experts to
label the student data. One open question in annotating affective data for
affect detection is whether the labelers (i.e., human experts) need to be
socio-culturally similar to the students being labeled, as this impacts the
cost feasibility of obtaining the labels. In this study, we investigate the
following research questions: For affective state annotation, how does the
socio-cultural background of human expert labelers, compared to the subjects,
impact the degree of consensus and distribution of affective states obtained?
Secondly, how do differences in labeler background impact the performance of
affect detection models that are trained using these labels?
Semi-interactive Attention Network for Answer Understanding in Reverse-QA
Authors: Qing Yin, Guan Luo, Xiaodong Zhu, QinGhua Hu, Ou wu
Date: 2019-01-12 00:00:00
Tasks: Question Answering, text-classification

Question answering (QA) is an important natural language processing (NLP)
task and has received much attention in academic research and industry
communities. Existing QA studies assume that questions are raised by humans and
answers are generated by machines. Nevertheless, in many real applications,
machines are also required to determine human needs or perceive human states.
In such scenarios, machines may proactively raise questions and humans supply
answers. Subsequently, machines should attempt to understand the true meaning
of these answers. This new QA approach is called reverse-QA (rQA) throughout
this paper. In this work, the human answer understanding problem is
investigated and solved by classifying the answers into predefined answer-label
categories (e.g., True, False, Uncertain). To explore the relationships between
questions and answers, we use the interactive attention network (IAN) model and
propose an improved structure called semi-interactive attention network
(Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several
conventional text classification models for comparison, and experimental
results indicate the promising performance of our proposed models.
Automatic classification of geologic units in seismic images using partially interpreted examples
Authors: Bas Peters, Justin Granek, Eldad Haber
Date: 2019-01-12 00:00:00
Tasks: General Classification, Segmentation, Seismic Interpretation, Semantic Segmentation

Geologic interpretation of large seismic stacked or migrated seismic images
can be a time-consuming task for seismic interpreters. Neural network based
semantic segmentation provides fast and automatic interpretations, provided a
sufficient number of example interpretations are available. Networks that map
from image-to-image emerged recently as powerful tools for automatic
segmentation, but standard implementations require fully interpreted examples.
Generating training labels for large images manually is time consuming. We
introduce a partial loss-function and labeling strategies such that networks
can learn from partially interpreted seismic images. This strategy requires
only a small number of annotated pixels per seismic image. Tests on seismic
images and interpretation information from the Sea of Ireland show that we
obtain high-quality predicted interpretations from a small number of large
seismic images. The combination of a partial-loss function, a multi-resolution
network that explicitly takes small and large-scale geological features into
account, and new labeling strategies make neural networks a more practical tool
for automatic seismic interpretation.
DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
Authors: Jun Gao, Chengcheng Tang, Vignesh Ganapathi-Subramanian, Jiahui Huang, Hao Su, Leonidas J. Guibas
Date: 2019-01-12 00:00:00

Reconstruction of geometry based on different input modes, such as images or
point clouds, has been instrumental in the development of computer aided design
and computer graphics. Optimal implementations of these applications have
traditionally involved the use of spline-based representations at their core.
Most such methods attempt to solve optimization problems that minimize an
output-target mismatch. However, these optimization techniques require an
initialization that is close enough, as they are local methods by nature. We
propose a deep learning architecture that adapts to perform spline fitting
tasks accordingly, providing complementary results to the aforementioned
traditional methods. We showcase the performance of our approach, by
reconstructing spline curves and surfaces based on input images or point
clouds.
Creative AI Through Evolutionary Computation
Authors: Risto Miikkulainen
Date: 2019-01-12 00:00:00

The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques are also well positioned to take advantage of large-scale parallel computing resources, making creative AI through evolutionary computation the likely "next deep learning".
Input Prioritization for Testing Neural Networks
Authors: Taejoon Byun, Vaibhav Sharma, Abhishek Vijayakumar, Sanjai Rayadurgam, Darren Cofer
Date: 2019-01-11 00:00:00
Tasks: Self-Driving Cars

Deep neural networks (DNNs) are increasingly being adopted for sensing and
control functions in a variety of safety and mission-critical systems such as
self-driving cars, autonomous air vehicles, medical diagnostics, and industrial
robotics. Failures of such systems can lead to loss of life or property, which
necessitates stringent verification and validation for providing high
assurance. Though formal verification approaches are being investigated,
testing remains the primary technique for assessing the dependability of such
systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining
test oracle data---the expected output, a.k.a. label, for a given input---is
high, which significantly impacts the amount and quality of testing that can be
performed. Thus, prioritizing input data for testing DNNs in meaningful ways to
reduce the cost of labeling can go a long way in increasing testing efficacy.
This paper proposes using gauges of the DNN's sentiment derived from the
computation performed by the model, as a means to identify inputs that are
likely to reveal weaknesses. We empirically assessed the efficacy of three such
sentiment measures for prioritization---confidence, uncertainty, and
surprise---and compare their effectiveness in terms of their fault-revealing
capability and retraining effectiveness. The results indicate that sentiment
measures can effectively flag inputs that expose unacceptable DNN behavior. For
MNIST models, the average percentage of inputs correctly flagged ranged from
88% to 94.8%.
Using Scene Graph Context to Improve Image Generation
Authors: Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang
Date: 2019-01-11 00:00:00
Tasks: Image Generation, Image Generation from Scene Graphs, Open-Ended Question Answering, Relation

Generating realistic images from scene graphs asks neural networks to be able
to reason about object relationships and compositionality. As a relatively new
task, how to properly ensure the generated images comply with scene graphs or
how to measure task performance remains an open question. In this paper, we
propose to harness scene graph context to improve image generation from scene
graphs. We introduce a scene graph context network that pools features
generated by a graph convolutional neural network that are then provided to
both the image generation network and the adversarial loss. With the context
network, our model is trained to not only generate realistic looking images,
but also to better preserve non-spatial object relationships. We also define
two novel evaluation metrics, the relation score and the mean opinion relation
score, for this task that directly evaluate scene graph compliance. We use both
quantitative and qualitative studies to demonstrate that our pro-posed model
outperforms the state-of-the-art on this challenging task.
Residual Pyramid FCN for Robust Follicle Segmentation
Authors: Zhewei Wang, Weizhen Cai, Charles D. Smith, Noriko Kantake, Thomas J. Rosol, Jundong Liu
Date: 2019-01-11 00:00:00
Tasks: Segmentation

In this paper, we propose a pyramid network structure to improve the
FCN-based segmentation solutions and apply it to label thyroid follicles in
histology images. Our design is based on the notion that a hierarchical
updating scheme, if properly implemented, can help FCNs capture the major
objects, as well as structure details in an image. To this end, we devise a
residual module to be mounted on consecutive network layers, through which
pixel labels would be propagated from the coarsest layer towards the finest
layer in a bottom-up fashion. We add five residual units along the decoding
path of a modified U-Net to make our segmentation network, Res-Seg-Net.
Experiments demonstrate that the multi-resolution set-up in our model is
effective in producing segmentations with improved accuracy and robustness.
The Deeper, the Better: Analysis of Person Attributes Recognition
Authors: Esube Bekele, Wallace Lawson
Date: 2019-01-11 00:00:00
Tasks: Attribute, Classification, General Classification

In person attributes recognition, we describe a person in terms of their
appearance. Typically, this includes a wide range of traits including age,
gender, clothing, and footwear. Although this could be used in a wide variety
of scenarios, it generally is applied to video surveillance, where attribute
recognition is impacted by low resolution, and other issues such as variable
pose, occlusion and shadow. Recent approaches have used deep convolutional
neural networks (CNNs) to improve the accuracy in person attribute recognition.
However, many of these networks are relatively shallow and it is unclear to
what extent they use contextual cues to improve classification accuracy. In
this paper, we propose deeper methods for person attribute recognition.
Interpreting the reasons behind the classification is highly important, as it
can provide insight into how the classifier is making decisions. Interpretation
suggests that deeper networks generally take more contextual information into
consideration, which helps improve classification accuracy and
generalizability. We present experimental analysis and results for whole body
attributes using the PA-100K and PETA datasets and facial attributes using the
CelebA dataset.
Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
Authors: Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine, Kristofer S. J. Pister
Date: 2019-01-11 00:00:00
Tasks: GPU, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this paper, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at <50Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a GPU-enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 seconds with 3 minutes of experimental training data.
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Authors: Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, Eduard Hovy
Date: 2019-01-11 00:00:00
Proceeding: CONLL 2019 11
Tasks: Natural Language Inference, Natural Language Understanding

Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle. We present EQUATE (Evaluating Quantitative Understanding Aptitude in Textual Entailment), a new framework for quantitative reasoning in textual entailment. We benchmark the performance of 9 published NLI models on EQUATE, and find that on average, state-of-the-art methods do not achieve an absolute improvement over a majority-class baseline, suggesting that they do not implicitly learn to reason with quantities. We establish a new baseline Q-REAS that manipulates quantities symbolically. In comparison to the best performing NLI model, it achieves success on numerical reasoning tests (+24.2%), but has limited verbal reasoning capabilities (-8.1%). We hope our evaluation framework will support the development of models of quantitative reasoning in language understanding.
Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
Authors: Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, Mark Riedl
Date: 2019-01-11 00:00:00
Tasks: Explanation Generation

Automated rationale generation is an approach for real-time explanation
generation whereby a computational model learns to translate an autonomous
agent's internal state and action data representations into natural language.
Training on human explanation data can enable agents to learn to generate
human-like explanations for their behavior. In this paper, using the context of
an agent that plays Frogger, we describe (a) how to collect a corpus of
explanations, (b) how to train a neural rationale generator to produce
different styles of rationales, and (c) how people perceive these rationales.
We conducted two user studies. The first study establishes the plausibility of
each type of generated rationale and situates their user perceptions along the
dimensions of confidence, humanlike-ness, adequate justification, and
understandability. The second study further explores user preferences between
the generated rationales with regard to confidence in the autonomous agent,
communicating failure and unexpected behavior. Overall, we find alignment
between the intended differences in features of the generated rationales and
the perceived differences by users. Moreover, context permitting, participants
preferred detailed rationales to form a stable mental model of the agent's
behavior.
Anticipation and next action forecasting in video: an end-to-end model with memory
Authors: Fiora Pirri, Lorenzo Mauro, Edoardo Alati, Valsamis Ntouskos, Mahdieh Izadpanahkakhk, Elham Omrani
Date: 2019-01-11 00:00:00
Tasks: Action Anticipation

Action anticipation and forecasting in videos do not require a hat-trick, as
far as there are signs in the context to foresee how actions are going to be
deployed. Capturing these signs is hard because the context includes the past.
We propose an end-to-end network for action anticipation and forecasting with
memory, to both anticipate the current action and foresee the next one.
Experiments on action sequence datasets show excellent results indicating that
training on histories with a dynamic memory can significantly improve
forecasting performance.
Non-Parametric Inference Adaptive to Intrinsic Dimension
Authors: Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis
Date: 2019-01-11 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation

We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation and inference is feasible as long as the distribution of the conditioning variable has small intrinsic dimension $d$, as measured by locally low doubling measures. Our estimation is based on a sub-sampled ensemble of the $k$-nearest neighbors ($k$-NN) $Z$-estimator. We show that if the intrinsic dimension of the covariate distribution is equal to $d$, then the finite sample estimation error of our estimator is of order $n^{-1/(d+2)}$ and our estimate is $n^{1/(d+2)}$-asymptotically normal, irrespective of $D$. The sub-sampling size required for achieving these results depends on the unknown intrinsic dimension $d$. We propose an adaptive data-driven approach for choosing this parameter and prove that it achieves the desired rates. We discuss extensions and applications to heterogeneous treatment effect estimation.
Large-scale Collaborative Filtering with Product Embeddings
Authors: Thom Lake, Sinead A. Williamson, Alexander T. Hawk, Christopher C. Johnson, Benjamin P. Wing
Date: 2019-01-11 00:00:00
Tasks: Collaborative Filtering, Representation Learning

The application of machine learning techniques to large-scale personalized
recommendation problems is a challenging task. Such systems must make sense of
enormous amounts of implicit feedback in order to understand user preferences
across numerous product categories. This paper presents a deep learning based
solution to this problem within the collaborative filtering with implicit
feedback framework. Our approach combines neural attention mechanisms, which
allow for context dependent weighting of past behavioral signals, with
representation learning techniques to produce models which obtain extremely
high coverage, can easily incorporate new information as it becomes available,
and are computationally efficient. Offline experiments demonstrate significant
performance improvements when compared to several alternative methods from the
literature. Results from an online setting show that the approach compares
favorably with current production techniques used to produce personalized
product recommendations.
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
Authors: Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting
Date: 2019-01-11 00:00:00

We introduce SPFlow, an open-source Python library providing a simple
interface to inference, learning and manipulation routines for deep and
tractable probabilistic models called Sum-Product Networks (SPNs). The library
allows one to quickly create SPNs both from data and through a domain specific
language (DSL). It efficiently implements several probabilistic inference
routines like computing marginals, conditionals and (approximate) most probable
explanations (MPEs) along with sampling as well as utilities for serializing,
plotting and structure statistics on an SPN. Moreover, many of the algorithms
proposed in the literature to learn the structure and parameters of SPNs are
readily available in SPFlow. Furthermore, SPFlow is extremely extensible and
customizable, allowing users to promptly distill new inference and learning
routines by injecting custom code into a lightweight functional-oriented API
framework. This is achieved in SPFlow by keeping an internal Python
representation of the graph structure that also enables practical compilation
of an SPN into a TensorFlow graph, C, CUDA or FPGA custom code, significantly
speeding-up computations.
The statistical Minkowski distances: Closed-form formula for Gaussian Mixture Models
Authors: Frank Nielsen
Date: 2019-01-09 00:00:00
Tasks: Diversity, Form

The traditional Minkowski distances are induced by the corresponding
Minkowski norms in real-valued vector spaces. In this work, we propose novel
statistical symmetric distances based on the Minkowski's inequality for
probability densities belonging to Lebesgue spaces. These statistical Minkowski
distances admit closed-form formula for Gaussian mixture models when
parameterized by integer exponents. This result extends to arbitrary mixtures
of exponential families with natural parameter spaces being cones: This
includes the binomial, the multinomial, the zero-centered Laplacian, the
Gaussian and the Wishart mixtures, among others. We also derive a Minkowski's
diversity index of a normalized weighted set of probability distributions from
Minkowski's inequality.
Translating SAR to Optical Images for Assisted Interpretation
Authors: Shilei Fu, Feng Xu, Ya-Qiu Jin
Date: 2019-01-08 00:00:00
Tasks: Translation

Despite the advantages of all-weather and all-day high-resolution imaging,
SAR remote sensing images are much less viewed and used by general people
because human vision is not adapted to microwave scattering phenomenon.
However, expert interpreters can be trained by compare side-by-side SAR and
optical images to learn the translation rules from SAR to optical. This paper
attempts to develop machine intelligence that are trainable with large-volume
co-registered SAR and optical images to translate SAR image to optical version
for assisted SAR interpretation. A novel reciprocal GAN scheme is proposed for
this translation task. It is trained and tested on both spaceborne GF-3 and
airborne UAVSAR images. Comparisons and analyses are presented for datasets of
different resolutions and polarizations. Results show that the proposed
translation network works well under many scenarios and it could potentially be
used for assisted SAR interpretation.
A Framework for Searching in Graphs in the Presence of Errors
Authors: Dariusz Dereniowski, Stefan Tiegel, Przemysław Uznański, Daniel Wolleb-Graf
Date: 2018-04-05 00:00:00

We consider the problem of searching for an unknown target vertex $t$ in a (possibly edge-weighted) graph. Each \emph{vertex-query} points to a vertex $v$ and the response either admits $v$ is the target or provides any neighbor $s\not=v$ that lies on a shortest path from $v$ to $t$. This model has been introduced for trees by Onak and Parys [FOCS 2006] and for general graphs by Emamjomeh-Zadeh et al. [STOC 2016]. In the latter, the authors provide algorithms for the error-less case and for the independent noise model (where each query independently receives an erroneous answer with known probability $p<1/2$ and a correct one with probability $1-p$). We study this problem in both adversarial errors and independent noise models. First, we show an algorithm that needs $\frac{\log_2 n}{1 - H(r)}$ queries against \emph{adversarial} errors, where adversary is bounded with its rate of errors by a known constant $r<1/2$. Our algorithm is in fact a simplification of previous work, and our refinement lies in invoking amortization argument. We then show that our algorithm coupled with Chernoff bound argument leads to an algorithm for independent noise that is simpler and with a query complexity that is both simpler and asymptotically better to one of Emamjomeh-Zadeh et al. [STOC 2016]. Our approach has a wide range of applications. First, it improves and simplifies Robust Interactive Learning framework proposed by Emamjomeh-Zadeh et al. [NIPS 2017]. Secondly, performing analogous analysis for \emph{edge-queries} (where query to edge $e$ returns its endpoint that is closer to target) we actually recover (as a special case) noisy binary search algorithm that is asymptotically optimal, matching the complexity of Feige et al. [SIAM J. Comput. 1994]. Thirdly, we improve and simplify upon existing algorithm for searching of \emph{unbounded} domains due to Aslam and Dhagat [STOC 1991].