pwc-archive/papers-with-abstracts huggingface dataset:
Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
Authors: Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran
Date: 2018-11-29 00:00:00
Proceeding: NeurIPS 2018 12

Deriving conditional and marginal distributions using conjugacy relationships
can be time consuming and error prone. In this paper, we propose a strategy for
automating such derivations. Unlike previous systems which focus on
relationships between pairs of random variables, our system (which we call
Autoconj) operates directly on Python functions that compute log-joint
distribution functions. Autoconj provides support for conjugacy-exploiting
algorithms in any Python embedded PPL. This paves the way for accelerating
development of novel inference algorithms and structure-exploiting modeling
strategies.
Stochastic Top-$K$ Subset Bandits with Linear Space and Non-Linear Feedback
Authors: Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn, Abhishek K. Umrawal
Date: 2018-11-29 00:00:00
Tasks: Multi-Armed Bandits

Many real-world problems like Social Influence Maximization face the dilemma of choosing the best $K$ out of $N$ options at a given time instant. This setup can be modeled as a combinatorial bandit which chooses $K$ out of $N$ arms at each time, with an aim to achieve an efficient trade-off between exploration and exploitation. This is the first work for combinatorial bandits where the feedback received can be a non-linear function of the chosen $K$ arms. The direct use of multi-armed bandit requires choosing among $N$-choose-$K$ options making the state space large. In this paper, we present a novel algorithm which is computationally efficient and the storage is linear in $N$. The proposed algorithm is a divide-and-conquer based strategy, that we call CMAB-SM. Further, the proposed algorithm achieves a \textit{regret bound} of $\tilde O(K^{\frac{1}{2}}N^{\frac{1}{3}}T^{\frac{2}{3}})$ for a time horizon $T$, which is \textit{sub-linear} in all parameters $T$, $N$, and $K$. %When applied to the problem of Social Influence Maximization, the performance of the proposed algorithm surpasses the UCB algorithm and some more sophisticated domain-specific methods.
Distributed Inference for Linear Support Vector Machine
Authors: Xiaozhou Wang, Zhuoyi Yang, Xi Chen, Weidong Liu
Date: 2018-11-29 00:00:00
Tasks: Binary Classification

The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vast literature on SVM, much less is known about the inferential properties of SVM, especially in a distributed setting. In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM. The proposed estimator is computationally efficient. In particular, it only requires an initial SVM estimator and then successively refines the estimator by solving simple weighted least squares problem. Theoretically, we establish the Bahadur representation of the estimator. Based on the representation, the asymptotic normality is further derived, which shows that the MDL estimator achieves the optimal statistical efficiency, i.e., the same efficiency as the classical linear SVM applying to the entire data set in a single machine setup. Moreover, our asymptotic result avoids the condition on the number of machines or data batches, which is commonly assumed in distributed estimation literature, and allows the case of diverging dimension. We provide simulation studies to demonstrate the performance of the proposed MDL estimator.
Single-view Object Shape Reconstruction Using Deep Shape Prior and Silhouette
Authors: Kejie Li, Ravi Garg, Ming Cai, Ian Reid
Date: 2018-11-29 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Object Reconstruction

3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network. In this paper, we aim to solve this problem via an online optimization framework inspired by traditional methods. Our framework employs a deep autoencoder to learn a set of latent codes of 3D object shapes, which are fitted by a probabilistic shape prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are jointly optimized guided by both image cues and deep shape prior without relying on an initialization from any trained deep nets. Surprisingly, our method achieves comparable performance to state-of-the-art methods even without training an end-to-end network, which shows a promising step in this direction.
Visual Question Answering as Reading Comprehension
Authors: Hui Li, Peng Wang, Chunhua Shen, Anton Van Den Hengel
Date: 2018-11-29 00:00:00
Proceeding: CVPR 2019 6
Tasks: Common Sense Reasoning, General Knowledge, Machine Reading Comprehension, Multiple-choice, Question Answering, Reading Comprehension, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) demands simultaneous comprehension of both
the image visual content and natural language questions. In some cases, the
reasoning needs the help of common sense or general knowledge which usually
appear in the form of text. Current methods jointly embed both the visual
information and the textual feature into the same space. However, how to model
the complex interactions between the two different modalities is not an easy
task. In contrast to struggling on multimodal feature fusion, in this paper, we
propose to unify all the input information by natural language so as to convert
VQA into a machine reading comprehension problem. With this transformation, our
method not only can tackle VQA datasets that focus on observation based
questions, but can also be naturally extended to handle knowledge-based VQA
which requires to explore large-scale external knowledge base. It is a step
towards being able to exploit large volumes of text and natural language
processing techniques to address VQA problem. Two types of models are proposed
to deal with open-ended VQA and multiple-choice VQA respectively. We evaluate
our models on three VQA benchmarks. The comparable performance with the
state-of-the-art demonstrates the effectiveness of the proposed method.
Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System
Authors: Pai Liu, Jingwei Gan, Rajan K. Chakrabarty
Date: 2018-11-29 00:00:00

We introduce a deep learning method to simulate the motion of particles
trapped in a chaotic recirculating flame. The Lagrangian trajectories of
particles, captured using a high-speed camera and subsequently reconstructed in
3-dimensional space, were used to train a variational autoencoder (VAE) which
comprises multiple layers of convolutional neural networks. We show that the
trajectories, which are statistically representative of those determined in
experiments, can be generated using the VAE network. The performance of our
model is evaluated with respect to the accuracy and generalization of the
outputs.
Manifold Coordinates with Physical Meaning
Authors: Samson Koelle, Hanyu Zhang, Marina Meila, Yu-Chia Chen
Date: 2018-11-29 00:00:00
Tasks: Dimensionality Reduction

Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space. One of the aims of dimension reduction is to find intrinsic coordinates that describe the data manifold. The coordinates returned by the embedding algorithm are abstract, and finding their physical or domain-related meaning is not formalized and often left to domain experts. This paper studies the problem of recovering the meaning of the new low-dimensional representation in an automatic, principled fashion. We propose a method to explain embedding coordinates of a manifold as non-linear compositions of functions from a user-defined dictionary. We show that this problem can be set up as a sparse linear Group Lasso recovery problem, find sufficient recovery conditions, and demonstrate its effectiveness on data.
Adversarial Bandits with Knapsacks
Authors: Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins
Date: 2018-11-28 00:00:00
Tasks: Multi-Armed Bandits, Scheduling

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into a limited-size knapsack. The BwK problem is a common generalization of numerous motivating examples, which range from dynamic pricing to repeated auctions to dynamic ad allocation to network routing and scheduling. While the prior work on BwK focused on the stochastic version, we pioneer the other extreme in which the outcomes can be chosen adversarially. This is a considerably harder problem, compared to both the stochastic version and the "classic" adversarial bandits, in that regret minimization is no longer feasible. Instead, the objective is to minimize the competitive ratio: the ratio of the benchmark reward to the algorithm's reward. We design an algorithm with competitive ratio O(log T) relative to the best fixed distribution over actions, where T is the time horizon; we also prove a matching lower bound. The key conceptual contribution is a new perspective on the stochastic version of the problem. We suggest a new algorithm for the stochastic version, which builds on the framework of regret minimization in repeated games and admits a substantially simpler analysis compared to prior work. We then analyze this algorithm for the adversarial version and use it as a subroutine to solve the latter.
Predicting the Computational Cost of Deep Learning Models
Authors: Daniel Justus, John Brennan, Stephen Bonner, Andrew Stephen McGough
Date: 2018-11-28 00:00:00
Tasks: Deep Learning

Deep learning is rapidly becoming a go-to tool for many artificial
intelligence problems due to its ability to outperform other approaches and
even humans at many problems. Despite its popularity we are still unable to
accurately predict the time it will take to train a deep learning network to
solve a given problem. This training time can be seen as the product of the
training time per epoch and the number of epochs which need to be performed to
reach the desired level of accuracy. Some work has been carried out to predict
the training time for an epoch -- most have been based around the assumption
that the training time is linearly related to the number of floating point
operations required. However, this relationship is not true and becomes
exacerbated in cases where other activities start to dominate the execution
time. Such as the time to load data from memory or loss of performance due to
non-optimal parallel execution. In this work we propose an alternative approach
in which we train a deep learning network to predict the execution time for
parts of a deep learning network. Timings for these individual parts can then
be combined to provide a prediction for the whole execution time. This has
advantages over linear approaches as it can model more complex scenarios. But,
also, it has the ability to predict execution times for scenarios unseen in the
training data. Therefore, our approach can be used not only to infer the
execution time for a batch, or entire epoch, but it can also support making a
well-informed choice for the appropriate hardware and model.
Towards Neural Co-Processors for the Brain: Combining Decoding and Encoding in Brain-Computer Interfaces
Authors: Rajesh P. N. Rao
Date: 2018-11-28 00:00:00

The field of brain-computer interfaces is poised to advance from the
traditional goal of controlling prosthetic devices using brain signals to
combining neural decoding and encoding within a single neuroprosthetic device.
Such a device acts as a "co-processor" for the brain, with applications ranging
from inducing Hebbian plasticity for rehabilitation after brain injury to
reanimating paralyzed limbs and enhancing memory. We review recent progress in
simultaneous decoding and encoding for closed-loop control and plasticity
induction. To address the challenge of multi-channel decoding and encoding, we
introduce a unifying framework for developing brain co-processors based on
artificial neural networks and deep learning. These "neural co-processors" can
be used to jointly optimize cost functions with the nervous system to achieve
desired behaviors ranging from targeted neuro-rehabilitation to augmentation of
brain function.
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers
Authors: Nathan Inkawhich, Matthew Inkawhich, Yiran Chen, Hai Li
Date: 2018-11-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Action Recognition, Adversarial Attack, image-classification, Image Classification, Optical Flow Estimation, Temporal Action Localization

The success of deep learning research has catapulted deep models into
production systems that our society is becoming increasingly dependent on,
especially in the image and video domains. However, recent work has shown that
these largely uninterpretable models exhibit glaring security vulnerabilities
in the presence of an adversary. In this work, we develop a powerful untargeted
adversarial attack for action recognition systems in both white-box and
black-box settings. Action recognition models differ from image-classification
models in that their inputs contain a temporal dimension, which we explicitly
target in the attack. Drawing inspiration from image classifier attacks, we
create new attacks which achieve state-of-the-art success rates on a two-stream
classifier trained on the UCF-101 dataset. We find that our attacks can
significantly degrade a model's performance with sparsely and imperceptibly
perturbed examples. We also demonstrate the transferability of our attacks to
black-box action recognition systems.
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology
Authors: Chen Gong, N. Benjamin Erichson, John P. Kelly, Laura Trutoiu, Brian T. Schowengerdt, Steven L. Brunton, Eric J. Seibel
Date: 2018-11-28 00:00:00
Tasks: Dimensionality Reduction, Mixed Reality, Template Matching

Retinal template matching and registration is an important challenge in
teleophthalmology with low-cost imaging devices. However, the images from such
devices generally have a small field of view (FOV) and image quality
degradations, making matching difficult. In this work, we develop an efficient
and accurate retinal matching technique that combines dimension reduction and
mutual information (MI), called RetinaMatch. The dimension reduction
initializes the MI optimization as a coarse localization process, which narrows
the optimization domain and avoids local optima. The effectiveness of
RetinaMatch is demonstrated on the open fundus image database STARE with
simulated reduced FOV and anticipated degradations, and on retinal images
acquired by adapter-based optics attached to a smartphone. RetinaMatch achieves
a success rate over 94\% on human retinal images with the matched target
registration errors below 2 pixels on average, excluding the observer
variability. It outperforms the standard template matching solutions. In the
application of measuring vessel diameter repeatedly, single pixel errors are
expected. In addition, our method can be used in the process of image
mosaicking with area-based registration, providing a robust approach when the
feature based methods fail. To the best of our knowledge, this is the first
template matching algorithm for retina images with small template images from
unconstrained retinal areas. In the context of the emerging mixed reality
market, we envision automated retinal image matching and registration methods
as transformative for advanced teleophthalmology and long-term retinal
monitoring.
Guided patch-wise nonlocal SAR despeckling
Authors: Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa, Luisa Verdoliva, Giovanni Poggi
Date: 2018-11-28 00:00:00
Tasks: Sar Image Despeckling

We propose a new method for SAR image despeckling which leverages information
drawn from co-registered optical imagery. Filtering is performed by plain
patch-wise nonlocal means, operating exclusively on SAR data. However, the
filtering weights are computed by taking into account also the optical guide,
which is much cleaner than the SAR data, and hence more discriminative. To
avoid injecting optical-domain information into the filtered image, a
SAR-domain statistical test is preliminarily performed to reject right away any
risky predictor. Experiments on two SAR-optical datasets prove the proposed
method to suppress very effectively the speckle, preserving structural details,
and without introducing visible filtering artifacts. Overall, the proposed
method compares favourably with all state-of-the-art despeckling filters, and
also with our own previous optical-guided filter.
Joint Correction of Attenuation and Scatter Using Deep Convolutional Neural Networks (DCNN) for Time-of-Flight PET
Authors: Jaewon Yang, Dookun Park, Jae Ho Sohn, Zhen Jane Wang, Grant T. Gullberg, Youngho Seo
Date: 2018-11-28 00:00:00

Deep convolutional neural networks (DCNN) have demonstrated its capability to
convert MR image to pseudo CT for PET attenuation correction in PET/MRI.
Conventionally, attenuated events are corrected in sinogram space using
attenuation maps derived from CT or MR-derived pseudo CT. Separately, scattered
events are iteratively estimated by a 3D model-based simulation using
down-sampled attenuation and emission sinograms. However, no studies have
investigated joint correction of attenuation and scatter using DCNN in image
space. Therefore, we aim to develop and optimize a DCNN model for attenuation
and scatter correction (ASC) simultaneously in PET image space without
additional anatomical imaging or time-consuming iterative scatter simulation.
For the first time, we demonstrated the feasibility of directly producing PET
images corrected for attenuation and scatter using DCNN (PET-DCNN) from
noncorrected PET (PET-NC) images.
Non-Volume Preserving-based Fusion to Group-Level Emotion Recognition on Crowd Videos
Authors: Kha Gia Quach, Ngan Le, Chi Nhan Duong, Ibsa Jalata, Kaushik Roy, Khoa Luu
Date: 2018-11-28 00:00:00
Tasks: Emotion Recognition

Group-level emotion recognition (ER) is a growing research area as the demands for assessing crowds of all sizes are becoming an interest in both the security arena as well as social media. This work extends the earlier ER investigations, which focused on either group-level ER on single images or within a video, by fully investigating group-level expression recognition on crowd videos. In this paper, we propose an effective deep feature level fusion mechanism to model the spatial-temporal information in the crowd videos. In our approach, the fusing process is performed on the deep feature domain by a generative probabilistic model, Non-Volume Preserving Fusion (NVPF), that models spatial information relationships. Furthermore, we extend our proposed spatial NVPF approach to the spatial-temporal NVPF approach to learn the temporal information between frames. To demonstrate the robustness and effectiveness of each component in the proposed approach, three experiments were conducted: (i) evaluation on AffectNet database to benchmark the proposed EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii) examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos (GECV) dataset composed of 627 videos collected from publicly available sources. GECV dataset is a collection of videos containing crowds of people. Each video is labeled with emotion categories at three levels: individual faces, group of people, and the entire video frame.
Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study
Authors: Guoxin Fan, Huaqing Liu, Zhenhua Wu, Yu-Feng Li, Chaobo Feng, Dongdong Wang, Jie Luo, Xiaofei Guan, William M. Wells III, Shisheng He
Date: 2018-11-28 00:00:00
Tasks: Computed Tomography (CT)

Background and objective: Combined evaluation of lumbosacral structures (e.g.
nerves, bone) on multimodal radiographic images is routinely conducted prior to
spinal surgery and interventional procedures. Generally, magnetic resonance
imaging is conducted to differentiate nerves, while computed tomography (CT) is
used to observe bony structures. The aim of this study is to investigate the
feasibility of automatically segmenting lumbosacral structures (e.g. nerves &
bone) on non-contrast CT with deep learning. Methods: a total of 50 cases with
spinal CT were manually labeled for lumbosacral nerves and bone with Slicer
4.8. The ratio of training: validation: testing is 32:8:10. A 3D-Unet is
adopted to build the model SPINECT for automatically segmenting lumbosacral
structures. Pixel accuracy, IoU, and Dice score are used to assess the
segmentation performance of lumbosacral structures. Results: the testing
results reveals successful segmentation of lumbosacral bone and nerve on CT.
The average pixel accuracy is 0.940 for bone and 0.918 for nerve. The average
IoU is 0.897 for bone and 0.827 for nerve. The dice score is 0.945 for bone and
0.905 for nerve. Conclusions: this pilot study indicated that automatic
segmenting lumbosacral structures (nerves and bone) on non-contrast CT is
feasible and may have utility for planning and navigating spinal interventions
and surgery.
Towards Task Understanding in Visual Settings
Authors: Sebastin Santy, Wazeer Zulfikar, Rishabh Mehrotra, Emine Yilmaz
Date: 2018-11-28 00:00:00
Tasks: Image Captioning, Text Generation

We consider the problem of understanding real world tasks depicted in visual
images. While most existing image captioning methods excel in producing natural
language descriptions of visual scenes involving human tasks, there is often
the need for an understanding of the exact task being undertaken rather than a
literal description of the scene. We leverage insights from real world task
understanding systems, and propose a framework composed of convolutional neural
networks, and an external hierarchical task ontology to produce task
descriptions from input images. Detailed experiments highlight the efficacy of
the extracted descriptions, which could potentially find their way in many
applications, including image alt text generation.