pwc-archive/papers-with-abstracts huggingface dataset:
SemTK: An Ontology-first, Open Source Semantic Toolkit for Managing and Querying Knowledge Graphs
Authors: Paul Cuddihy, Justin McHugh, Jenny Weisenberg Williams, Varish Mulwad, Kareem S. Aggour
Date: 2017-10-31 00:00:00
Tasks: Knowledge Graphs

The relatively recent adoption of Knowledge Graphs as an enabling technology
in multiple high-profile artificial intelligence and cognitive applications has
led to growing interest in the Semantic Web technology stack. Many
semantics-related tools, however, are focused on serving experts with a deep
understanding of semantic technologies. For example, triplification of
relational data is available but there is no open source tool that allows a
user unfamiliar with OWL/RDF to import data into a semantic triple store in an
intuitive manner. Further, many tools require users to have a working
understanding of SPARQL to query data. Casual users interested in benefiting
from the power of Knowledge Graphs have few tools available for exploring,
querying, and managing semantic data. We present SemTK, the Semantics Toolkit,
a user-friendly suite of tools that allow both expert and non-expert semantics
users convenient ingestion of relational data, simplified query generation, and
more. The exploration of ontologies and instance data is performed through
SPARQLgraph, an intuitive web-based user interface in SemTK understandable and
navigable by a lay user. The open source version of SemTK is available at
http://semtk.research.ge.com
Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks
Authors: Emilien Dupont, Tuanfeng Zhang, Peter Tilke, Lin Liang, William Bailey
Date: 2018-02-08 00:00:00
Tasks: Spatial Interpolation

An important problem in geostatistics is to build models of the subsurface of
the Earth given physical measurements at sparse spatial locations. Typically,
this is done using spatial interpolation methods or by reproducing patterns
from a reference image. However, these algorithms fail to produce realistic
patterns and do not exhibit the wide range of uncertainty inherent in the
prediction of geology. In this paper, we show how semantic inpainting with
Generative Adversarial Networks can be used to generate varied realizations of
geology which honor physical measurements while matching the expected
geological patterns. In contrast to other algorithms, our method scales well
with the number of data points and mimics a distribution of patterns as opposed
to a single pattern or image. The generated conditional samples are state of
the art.
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps
Authors: Simon S. Du, Surbhi Goel
Date: 2018-05-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: regression

We propose a new algorithm to learn a one-hidden-layer convolutional neural
network where both the convolutional weights and the outputs weights are
parameters to be learned. Our algorithm works for a general class of
(potentially overlapping) patches, including commonly used structures for
computer vision tasks. Our algorithm draws ideas from (1) isotonic regression
for learning neural networks and (2) landscape analysis of non-convex matrix
factorization problems. We believe these findings may inspire further
development in designing provable algorithms for learning neural networks and
other complex models.
CubeSLAM: Monocular 3D Object SLAM
Authors: Shichao Yang, Sebastian Scherer
Date: 2018-06-01 00:00:00
Tasks: 3D Object Detection, Camera Pose Estimation, Object, object-detection, Object Detection, Object SLAM, Pose Estimation

We present a method for single image 3D cuboid object detection and
multi-view object SLAM in both static and dynamic environments, and demonstrate
that the two parts can improve each other. Firstly for single image object
detection, we generate high-quality cuboid proposals from 2D bounding boxes and
vanishing points sampling. The proposals are further scored and selected based
on the alignment with image edges. Secondly, multi-view bundle adjustment with
new object measurements is proposed to jointly optimize poses of cameras,
objects and points. Objects can provide long-range geometric and scale
constraints to improve camera pose estimation and reduce monocular drift.
Instead of treating dynamic regions as outliers, we utilize object
representation and motion model constraints to improve the camera pose
estimation. The 3D detection experiments on SUN RGBD and KITTI show better
accuracy and robustness over existing approaches. On the public TUM, KITTI
odometry and our own collected datasets, our SLAM method achieves the
state-of-the-art monocular camera pose estimation and at the same time,
improves the 3D object detection accuracy.
Intrinsic Isometric Manifold Learning with Application to Localization
Authors: Ariel Schwartz, Ronen Talmon
Date: 2018-06-01 00:00:00
Tasks: Indoor Localization

Data living on manifolds commonly appear in many applications. Often this
results from an inherently latent low-dimensional system being observed through
higher dimensional measurements. We show that under certain conditions, it is
possible to construct an intrinsic and isometric data representation, which
respects an underlying latent intrinsic geometry. Namely, we view the observed
data only as a proxy and learn the structure of a latent unobserved intrinsic
manifold, whereas common practice is to learn the manifold of the observed
data. For this purpose, we build a new metric and propose a method for its
robust estimation by assuming mild statistical priors and by using artificial
neural networks as a mechanism for metric regularization and parametrization.
We show successful application to unsupervised indoor localization in ad-hoc
sensor networks. Specifically, we show that our proposed method facilitates
accurate localization of a moving agent from imaging data it collects.
Importantly, our method is applied in the same way to two different imaging
modalities, thereby demonstrating its intrinsic and modality-invariant
capabilities.
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
Authors: Christopher Stanton, Jeff Clune
Date: 2018-06-01 00:00:00
Tasks: Deep Reinforcement Learning, Montezuma's Revenge, Reinforcement Learning

Traditional exploration methods in RL require agents to perform random
actions to find rewards. But these approaches struggle on sparse-reward domains
like Montezuma's Revenge where the probability that any random action sequence
leads to reward is extremely low. Recent algorithms have performed well on such
tasks by encouraging agents to visit new states or perform new actions in
relation to all prior training episodes (which we call across-training
novelty). But such algorithms do not consider whether an agent exhibits
intra-life novelty: doing something new within the current episode, regardless
of whether those behaviors have been performed in previous episodes. We
hypothesize that across-training novelty might discourage agents from
revisiting initially non-rewarding states that could become important stepping
stones later in training. We introduce Deep Curiosity Search (DeepCS), which
encourages intra-life exploration by rewarding agents for visiting as many
different states as possible within each episode, and show that DeepCS matches
the performance of current state-of-the-art methods on Montezuma's Revenge. We
further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and
Tutankham (many of which are hard exploration games). Surprisingly, DeepCS
doubles A2C performance on Seaquest, a game we would not have expected to
benefit from intra-life exploration because the arena is small and already
easily navigated by naive exploration techniques. In one run, DeepCS achieves a
maximum training score of 80,000 points on Seaquest, higher than any methods
other than Ape-X. The strong performance of DeepCS on these sparse- and
dense-reward tasks suggests that encouraging intra-life novelty is an
interesting, new approach for improving performance in Deep RL and motivates
further research into hybridizing across-training and intra-life exploration
methods.
Bayesian approach to model-based extrapolation of nuclear observables
Authors: Léo Neufcourt, Yuchen Cao, Witold Nazarewicz, Frederi Viens
Date: 2018-06-01 00:00:00
Tasks: Gaussian Processes, Uncertainty Quantification

The mass, or binding energy, is the basis property of the atomic nucleus. It
determines its stability, and reaction and decay rates. Quantifying the nuclear
binding is important for understanding the origin of elements in the universe.
The astrophysical processes responsible for the nucleosynthesis in stars often
take place far from the valley of stability, where experimental masses are not
known. In such cases, missing nuclear information must be provided by
theoretical predictions using extreme extrapolations. Bayesian machine learning
techniques can be applied to improve predictions by taking full advantage of
the information contained in the deviations between experimental and calculated
masses. We consider 10 global models based on nuclear Density Functional Theory
as well as two more phenomenological mass models. The emulators of S2n
residuals and credibility intervals defining theoretical error bars are
constructed using Bayesian Gaussian processes and Bayesian neural networks. We
consider a large training dataset pertaining to nuclei whose masses were
measured before 2003. For the testing datasets, we considered those exotic
nuclei whose masses have been determined after 2003. We then carried out
extrapolations towards the 2n dripline. While both Gaussian processes and
Bayesian neural networks reduce the rms deviation from experiment
significantly, GP offers a better and much more stable performance. The
increase in the predictive power is quite astonishing: the resulting rms
deviations from experiment on the testing dataset are similar to those of more
phenomenological models. The empirical coverage probability curves we obtain
match very well the reference values which is highly desirable to ensure
honesty of uncertainty quantification, and the estimated credibility intervals
on predictions make it possible to evaluate predictive power of individual
models.
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
Authors: Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman
Date: 2018-06-01 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning a generative model is a key component of model-based reinforcement
learning. Though learning a good model in the tabular setting is a simple task,
learning a useful model in the approximate setting is challenging. In this
context, an important question is the loss function used for model learning as
varying the loss function can have a remarkable impact on effectiveness of
planning. Recently Farahmand et al. (2017) proposed a value-aware model
learning (VAML) objective that captures the structure of value function during
model learning. Using tools from Asadi et al. (2018), we show that minimizing
the VAML objective is in fact equivalent to minimizing the Wasserstein metric.
This equivalence improves our understanding of value-aware models, and also
creates a theoretical foundation for applications of Wasserstein in model-based
reinforcement~learning.
Neural Proximal Gradient Descent for Compressive Imaging
Authors: Morteza Mardani, Qingyun Sun, Shreyas Vasawanala, Vardan Papyan, Hatef Monajemi, John Pauly, David Donoho
Date: 2018-06-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: compressed sensing

Recovering high-resolution images from limited sensory data typically leads
to a serious ill-posed inverse problem, demanding inversion algorithms that
effectively capture the prior information. Learning a good inverse mapping from
training data faces severe challenges, including: (i) scarcity of training
data; (ii) need for plausible reconstructions that are physically feasible;
(iii) need for fast reconstruction, especially in real-time applications. We
develop a successful system solving all these challenges, using as basic
architecture the recurrent application of proximal gradient algorithm. We learn
a proximal map that works well with real images based on residual networks.
Contraction of the resulting map is analyzed, and incoherence conditions are
investigated that drive the convergence of the iterates. Extensive experiments
are carried out under different settings: (a) reconstructing abdominal MRI of
pediatric patients from highly undersampled Fourier-space data and (b)
superresolving natural face images. Our key findings include: 1. a recurrent
ResNet with a single residual block unrolled from an iterative algorithm yields
an effective proximal which accurately reveals MR image details. 2. Our
architecture significantly outperforms conventional non-recurrent deep ResNets
by 2dB SNR; it is also trained much more rapidly. 3. It outperforms
state-of-the-art compressed-sensing Wavelet-based methods by 4dB SNR, with 100x
speedups in reconstruction time.
Dependent Gated Reading for Cloze-Style Question Answering
Authors: Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi, Prasad Tadepalli
Date: 2018-05-26 00:00:00
Proceeding: COLING 2018 8
Tasks: Decision Making, Question Answering, Reading Comprehension

We present a novel deep learning architecture to address the cloze-style
question answering task. Existing approaches employ reading mechanisms that do
not fully exploit the interdependency between the document and the query. In
this paper, we propose a novel \emph{dependent gated reading} bidirectional GRU
network (DGR) to efficiently model the relationship between the document and
the query during encoding and decision making. Our evaluation shows that DGR
obtains highly competitive performance on well-known machine comprehension
benchmarks such as the Children's Book Test (CBT-NE and CBT-CN) and Who DiD
What (WDW, Strict and Relaxed). Finally, we extensively analyze and validate
our model by ablation and attention studies.
Network Enhancement: a general method to denoise weighted biological networks
Authors: Bo Wang, Armin Pourshafeie, Marinka Zitnik, Junjie Zhu, Carlos D. Bustamante, Serafim Batzoglou, Jure Leskovec
Date: 2018-05-09 00:00:00
Tasks: Denoising

Networks are ubiquitous in biology where they encode connectivity patterns at
all scales of organization, from molecular to the biome. However, biological
networks are noisy due to the limitations of measurement technology and
inherent natural variation, which can hamper discovery of network patterns and
dynamics. We propose Network Enhancement (NE), a method for improving the
signal-to-noise ratio of undirected, weighted networks. NE uses a doubly
stochastic matrix operator that induces sparsity and provides a closed-form
solution that increases spectral eigengap of the input network. As a result, NE
removes weak edges, enhances real connections, and leads to better downstream
performance. Experiments show that NE improves gene function prediction by
denoising tissue-specific interaction networks, alleviates interpretation of
noisy Hi-C contact maps from the human genome, and boosts fine-grained
identification accuracy of species. Our results indicate that NE is widely
applicable for denoising biological networks.
The Externalities of Exploration and How Data Diversity Helps Exploitation
Authors: Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Date: 2018-06-01 00:00:00
Tasks: Diversity, Multi-Armed Bandits

Online learning algorithms, widely used to power search and content
optimization on the web, must balance exploration and exploitation, potentially
sacrificing the experience of current users for information that will lead to
better decisions in the future. Recently, concerns have been raised about
whether the process of exploration could be viewed as unfair, placing too much
burden on certain individuals or groups. Motivated by these concerns, we
initiate the study of the externalities of exploration - the undesirable side
effects that the presence of one party may impose on another - under the linear
contextual bandits model. We introduce the notion of a group externality,
measuring the extent to which the presence of one population of users impacts
the rewards of another. We show that this impact can in some cases be negative,
and that, in a certain sense, no algorithm can avoid it. We then study
externalities at the individual level, interpreting the act of exploration as
an externality imposed on the current user of a system by future users. This
drives us to ask under what conditions inherent diversity in the data makes
explicit exploration unnecessary. We build on a recent line of work on the
smoothed analysis of the greedy algorithm that always chooses the action that
currently looks optimal, improving on prior results to show that a greedy
approach almost matches the best possible Bayesian regret rate of any other
algorithm on the same problem instance whenever the diversity conditions hold,
and that this regret is at most $\tilde{O}(T^{1/3})$. Returning to group-level
effects, we show that under the same conditions, negative group externalities
essentially vanish under the greedy algorithm. Together, our results uncover a
sharp contrast between the high externalities that exist in the worst case, and
the ability to remove all externalities if the data is sufficiently diverse.
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling
Authors: Kenny J. Young, Richard S. Sutton, Shuo Yang
Date: 2018-06-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Episodic memory is a psychology term which refers to the ability to recall
specific events from the past. We suggest one advantage of this particular type
of memory is the ability to easily assign credit to a specific state when
remembered information is found to be useful. Inspired by this idea, and the
increasing popularity of external memory mechanisms to handle long-term
dependencies in deep learning systems, we propose a novel algorithm which uses
a reservoir sampling procedure to maintain an external memory consisting of a
fixed number of past states. The algorithm allows a deep reinforcement learning
agent to learn online to preferentially remember those states which are found
to be useful to recall later on. Critically this method allows for efficient
online computation of gradient estimates with respect to the write process of
the external memory. Thus unlike most prior mechanisms for external memory it
is feasible to use in an online reinforcement learning setting.
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
Authors: Tayo Ajayi, David Mildebrath, Anastasios Kyrillidis, Shashanka Ubaru, Georgios Kollias, Kristofer Bouchard
Date: 2018-06-01 00:00:00
Tasks: Quantum State Tomography

We present theoretical results on the convergence of \emph{non-convex} accelerated gradient descent in matrix factorization models with $\ell_2$-norm loss. The purpose of this work is to study the effects of acceleration in non-convex settings, where provable convergence with acceleration should not be considered a \emph{de facto} property. The technique is applied to matrix sensing problems, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. Our contributions can be summarized as follows. $i)$ We show that acceleration in factored gradient descent converges at a linear rate; this fact is novel for non-convex matrix factorization settings, under common assumptions. $ii)$ Our proof technique requires the acceleration parameter to be carefully selected, based on the properties of the problem, such as the condition number of $X^\star$ and the condition number of objective function. $iii)$ Currently, our proof leads to the same dependence on the condition number(s) in the contraction parameter, similar to recent results on non-accelerated algorithms. $iv)$ Acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators.
Audio Visual Scene-Aware Dialog (AVSD) Challenge at DSTC7
Authors: Huda Alamri, Vincent Cartillier, Raphael Gontijo Lopes, Abhishek Das, Jue Wang, Irfan Essa, Dhruv Batra, Devi Parikh, Anoop Cherian, Tim K. Marks, Chiori Hori
Date: 2018-06-01 00:00:00
Tasks: Video Description, Visual Dialog

Scene-aware dialog systems will be able to have conversations with users
about the objects and events around them. Progress on such systems can be made
by integrating state-of-the-art technologies from multiple research areas
including end-to-end dialog systems visual dialog, and video description. We
introduce the Audio Visual Scene Aware Dialog (AVSD) challenge and dataset. In
this challenge, which is one track of the 7th Dialog System Technology
Challenges (DSTC7) workshop1, the task is to build a system that generates
responses in a dialog about an input video
Targeted Kernel Networks: Faster Convolutions with Attentive Regularization
Authors: Kashyap Chitta
Date: 2018-06-01 00:00:00

We propose Attentive Regularization (AR), a method to constrain the
activation maps of kernels in Convolutional Neural Networks (CNNs) to specific
regions of interest (ROIs). Each kernel learns a location of specialization
along with its weights through standard backpropagation. A differentiable
attention mechanism requiring no additional supervision is used to optimize the
ROIs. Traditional CNNs of different types and structures can be modified with
this idea into equivalent Targeted Kernel Networks (TKNs), while keeping the
network size nearly identical. By restricting kernel ROIs, we reduce the number
of sliding convolutional operations performed throughout the network in its
forward pass, speeding up both training and inference. We evaluate our proposed
architecture on both synthetic and natural tasks across multiple domains. TKNs
obtain significant improvements over baselines, requiring less computation
(around an order of magnitude) while achieving superior performance.