pwc-archive/papers-with-abstracts huggingface dataset:
Random Temporal Skipping for Multirate Video Analysis
Authors: Yi Zhu, Shawn Newsam
Date: 2018-10-30 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization, Video Understanding

Current state-of-the-art approaches to video understanding adopt temporal
jittering to simulate analyzing the video at varying frame rates. However, this
does not work well for multirate videos, in which actions or subactions occur
at different speeds. The frame sampling rate should vary in accordance with the
different motion speeds. In this work, we propose a simple yet effective
strategy, termed random temporal skipping, to address this situation. This
strategy effectively handles multirate videos by randomizing the sampling rate
during training. It is an exhaustive approach, which can potentially cover all
motion speed variations. Furthermore, due to the large temporal skipping, our
network can see video clips that originally cover over 100 frames. Such a time
range is enough to analyze most actions/events. We also introduce an
occlusion-aware optical flow learning method that generates improved motion
maps for human action recognition. Our framework is end-to-end trainable, runs
in real-time, and achieves state-of-the-art performance on six widely adopted
video benchmarks.
Gated Transfer Network for Transfer Learning
Authors: Yi Zhu, Jia Xue, Shawn Newsam
Date: 2018-10-30 00:00:00
Tasks: feature selection, Transfer Learning

Deep neural networks have led to a series of breakthroughs in computer vision
given sufficient annotated training datasets. For novel tasks with limited
labeled data, the prevalent approach is to transfer the knowledge learned in
the pre-trained models to the new tasks by fine-tuning. Classic model
fine-tuning utilizes the fact that well trained neural networks appear to learn
cross domain features. These features are treated equally during transfer
learning. In this paper, we explore the impact of feature selection in model
fine-tuning by introducing a transfer module, which assigns weights to features
extracted from pre-trained models. The proposed transfer module proves the
importance of feature selection for transferring models from source to target
domains. It is shown to significantly improve upon fine-tuning results with
only marginal extra computational cost. We also incorporate an auxiliary
classifier as an extra regularizer to avoid over-fitting. Finally, we build a
Gated Transfer Network (GTN) based on our transfer module and achieve
state-of-the-art results on six different tasks.
DeepGRU: Deep Gesture Recognition Utility
Authors: Mehran Maghoumi, Joseph J. LaViola Jr
Date: 2018-10-30 00:00:00
Tasks: Action Recognition, CPU, Gesture Recognition, Skeleton Based Action Recognition, Temporal Action Localization

We propose DeepGRU, a novel end-to-end deep network model informed by recent developments in deep learning for gesture and action recognition, that is streamlined and device-agnostic. DeepGRU, which uses only raw skeleton, pose or vector data is quickly understood, implemented, and trained, and yet achieves state-of-the-art results on challenging datasets. At the heart of our method lies a set of stacked gated recurrent units (GRU), two fully-connected layers and a novel global attention model. We evaluate our method on seven publicly available datasets, containing various number of samples and spanning over a broad range of interactions (full-body, multi-actor, hand gestures, etc.). In all but one case we outperform the state-of-the-art pose-based methods. For instance, we achieve a recognition accuracy of 84.9% and 92.3% on cross-subject and cross-view tests of the NTU RGB+D dataset respectively, and also 100% recognition accuracy on the UT-Kinect dataset. While DeepGRU works well on large datasets with many training samples, we show that even in the absence of a large number of training data, and with as little as four samples per class, DeepGRU can beat traditional methods specifically designed for small training sets. Lastly, we demonstrate that even without powerful hardware, and using only the CPU, our method can still be trained in under 10 minutes on small-scale datasets, making it an enticing choice for rapid application prototyping and development.
Weak-supervision for Deep Representation Learning under Class Imbalance
Authors: Shin Ando
Date: 2018-10-30 00:00:00
Tasks: Classification, Deep Learning, General Classification, image-classification, Image Classification, Representation Learning

Class imbalance is a pervasive issue among classification models including
deep learning, whose capacity to extract task-specific features is affected in
imbalanced settings. However, the challenges of handling imbalance among a
large number of classes, commonly addressed by deep learning, have not received
a significant amount of attention in previous studies. In this paper, we
propose an extension of the deep over-sampling framework, to exploit
automatically-generated abstract-labels, i.e., a type of side-information used
in weak-label learning, to enhance deep representation learning against class
imbalance. We attempt to exploit the labels to guide the deep representation of
instances towards different subspaces, to induce a soft-separation of inherent
subtasks of the classification problem. Our empirical study shows that the
proposed framework achieves a substantial improvement on image classification
benchmarks with imbalanced among large and small numbers of classes.
A Framework for Probabilistic Generic Traffic Scene Prediction
Authors: Yeping Hu, Wei Zhan, Masayoshi Tomizuka
Date: 2018-10-30 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Motion Planning, Prediction

In a given scenario, simultaneously and accurately predicting every possible
interaction of traffic participants is an important capability for autonomous
vehicles. The majority of current researches focused on the prediction of an
single entity without incorporating the environment information. Although some
approaches aimed to predict multiple vehicles, they either predicted each
vehicle independently with no considerations on possible interaction with
surrounding entities or generated discretized joint motions which cannot be
directly used in decision making and motion planning for autonomous vehicle. In
this paper, we present a probabilistic framework that is able to jointly
predict continuous motions for multiple interacting road participants under any
driving scenarios and is capable of forecasting the duration of each
interaction, which can enhance the prediction performance and efficiency. The
proposed traffic scene prediction framework contains two hierarchical modules:
the upper module and the lower module. The upper module forecasts the intention
of the predicted vehicle, while the lower module predicts motions for
interacting scene entities. An exemplar real-world scenario is used to
implement and examine the proposed framework.
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
Authors: Yiming Lei, Yukun Tian, Hongming Shan, Junping Zhang, Ge Wang, Mannudeep Kalra
Date: 2018-10-30 00:00:00
Tasks: Data Augmentation, General Classification, Lung Nodule Classification, Mapping Of Lung Nodules In Low-Dose Ct Images, Specificity

A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class activation mapping (CAM) and gradient-based CAM (Grad-CAM) are tailored for interpreting localization and classification tasks while they ignored fine-grained features. Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN. In this paper, we first develop a soft activation mapping (SAM) to enable fine-grained lung nodule shape \& margin (LNSM) feature analysis with a CNN so that it can access rich discrete features. Secondly, by combining high-level convolutional features with SAM, we further propose a high-level feature enhancement scheme (HESAM) to localize LNSM features. Experiments on the LIDC-IDRI dataset indicate that 1) SAM captures more fine-grained and discrete attention regions than existing methods, 2) HESAM localizes more accurately on LNSM features and obtains the state-of-the-art predictive performance, reducing the false positive rate, and 3) we design and conduct a visually matching experiment which incorporates radiologists study to increase the confidence level of applying our method to clinical diagnosis.
DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks
Authors: Mohammed Almukaynizi, Ericsson Marin, Eric Nunes, Paulo Shakarian, Gerardo I. Simari, Dipsy Kapoor, Timothy Siedlecki
Date: 2018-10-30 00:00:00

Recent incidents of data breaches call for organizations to proactively
identify cyber attacks on their systems. Darkweb/Deepweb (D2web) forums and
marketplaces provide environments where hackers anonymously discuss existing
vulnerabilities and commercialize malicious software to exploit those
vulnerabilities. These platforms offer security practitioners a threat
intelligence environment that allows to mine for patterns related to
organization-targeted cyber attacks. In this paper, we describe a system
(called DARKMENTION) that learns association rules correlating indicators of
attacks from D2web to real-world cyber incidents. Using the learned rules,
DARKMENTION generates and submits warnings to a Security Operations Center
(SOC) prior to attacks. Our goal was to design a system that automatically
generates enterprise-targeted warnings that are timely, actionable, accurate,
and transparent. We show that DARKMENTION meets our goal. In particular, we
show that it outperforms baseline systems that attempt to generate warnings of
cyber attacks related to two enterprises with an average increase in F1 score
of about 45% and 57%. Additionally, DARKMENTION was deployed as part of a
larger system that is built under a contract with the IARPA Cyber-attack
Automated Unconventional Sensor Environment (CAUSE) program. It is actively
producing warnings that precede attacks by an average of 3 days.
Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
Authors: Yen-Chang Hsu, Yen-Cheng Liu, Anita Ramasamy, Zsolt Kira
Date: 2018-10-30 00:00:00
Tasks: Continual Learning, L2 Regularization

Continual learning has received a great deal of attention recently with
several approaches being proposed. However, evaluations involve a diverse set
of scenarios making meaningful comparison difficult. This work provides a
systematic categorization of the scenarios and evaluates them within a
consistent framework including strong baselines and state-of-the-art methods.
The results provide an understanding of the relative difficulty of the
scenarios and that simple baselines (Adagrad, L2 regularization, and naive
rehearsal strategies) can surprisingly achieve similar performance to current
mainstream methods. We conclude with several suggestions for creating harder
evaluation scenarios and future research directions. The code is available at
https://github.com/GT-RIPL/Continual-Learning-Benchmark
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
Authors: Thomas Gabor, Lenz Belzner, Thomy Phan, Kyrill Schmid
Date: 2018-10-30 00:00:00
Tasks: Diversity, Evolutionary Algorithms

As automatic optimization techniques find their way into industrial
applications, the behavior of many complex systems is determined by some form
of planner picking the right actions to optimize a given objective function. In
many cases, the mapping of plans to objective reward may change due to
unforeseen events or circumstances in the real world. In those cases, the
planner usually needs some additional effort to adjust to the changed situation
and reach its previous level of performance. Whenever we still need to continue
polling the planner even during re-planning, it oftentimes exhibits severely
lacking performance. In order to improve the planner's resilience to unforeseen
change, we argue that maintaining a certain level of diversity amongst the
considered plans at all times should be added to the planner's objective.
Effectively, we encourage the planner to keep alternative plans to its
currently best solution. As an example case, we implement a diversity-aware
genetic algorithm using two different metrics for diversity (differing in their
generality) and show that the blow in performance due to unexpected change can
be severely lessened in the average case. We also analyze the parameter
settings necessary for these techniques in order to gain an intuition how they
can be incorporated into larger frameworks or process models for software and
systems engineering.
Using Large Ensembles of Control Variates for Variational Inference
Authors: Tomas Geffner, Justin Domke
Date: 2018-10-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Stochastic Optimization, Variational Inference

Variational inference is increasingly being addressed with stochastic optimization. In this setting, the gradient's variance plays a crucial role in the optimization procedure, since high variance gradients lead to poor convergence. A popular approach used to reduce gradient's variance involves the use of control variates. Despite the good results obtained, control variates developed for variational inference are typically looked at in isolation. In this paper we clarify the large number of control variates that are available by giving a systematic view of how they are derived. We also present a Bayesian risk minimization framework in which the quality of a procedure for combining control variates is quantified by its effect on optimization convergence rates, which leads to a very simple combination rule. Results show that combining a large number of control variates this way significantly improves the convergence of inference over using the typical gradient estimators or a reduced number of control variates.
Generating new pictures in complex datasets with a simple neural network
Authors: Galin Georgiev
Date: 2018-10-30 00:00:00

We introduce a version of a variational auto-encoder (VAE), which can generate good perturbations of images, when trained on a complex dataset (in our experiments, CIFAR-10). The net is using only two latent generative dimensions per class, with uni-modal probability density. The price one has to pay for good generation is that not all training images are well reconstructed. An additional classifier is required to determine which training image is well reconstructed and generally the weights of training images. Only training images which are well reconstructed, can be perturbed. For good perturbations, we use the tentative empirical drifts of well reconstructed images. The construct is not predictive in the usual statistical sense.
A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction
Authors: Roberto Souza, Richard Frayne
Date: 2018-10-30 00:00:00
Tasks: compressed sensing, Image Reconstruction

Decreasing magnetic resonance (MR) image acquisition times can potentially
reduce procedural cost and make MR examinations more accessible. Compressed
sensing (CS)-based image reconstruction methods, for example, decrease MR
acquisition time by reconstructing high-quality images from data that were
originally sampled at rates inferior to the Nyquist-Shannon sampling theorem.
In this work we propose a hybrid architecture that works both in the k-space
(or frequency-domain) and the image (or spatial) domains. Our network is
composed of a complex-valued residual U-net in the k-space domain, an inverse
Fast Fourier Transform (iFFT) operation, and a real-valued U-net in the image
domain. Our experiments demonstrated, using MR raw k-space data, that the
proposed hybrid approach can potentially improve CS reconstruction compared to
deep-learning networks that operate only in the image domain. In this study we
compare our method with four previously published deep neural networks and
examine their ability to reconstruct images that are subsequently used to
generate regional volume estimates. We evaluated undersampling ratios of 75%
and 80%. Our technique was ranked second in the quantitative analysis, but
qualitative analysis indicated that our reconstruction performed the best in
hard to reconstruct regions, such as the cerebellum. All images reconstructed
with our method were successfully post-processed, and showed good volumetry
agreement compared with the fully sampled reconstruction measures.
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Authors: Thomas Gabor, Lenz Belzner, Claudia Linnhoff-Popien
Date: 2018-10-30 00:00:00
Tasks: Diversity, Evolutionary Algorithms, global-optimization

Diversity is an important factor in evolutionary algorithms to prevent
premature convergence towards a single local optimum. In order to maintain
diversity throughout the process of evolution, various means exist in
literature. We analyze approaches to diversity that (a) have an explicit and
quantifiable influence on fitness at the individual level and (b) require no
(or very little) additional domain knowledge such as domain-specific distance
functions. We also introduce the concept of genealogical diversity in a broader
study. We show that employing these approaches can help evolutionary algorithms
for global optimization in many cases.
Differentiable Greedy Networks
Authors: Thomas Powers, Rasool Fakoor, Siamak Shakeri, Abhinav Sethy, Amanjit Kainth, Abdel-rahman Mohamed, Ruhi Sarikaya
Date: 2018-10-30 00:00:00
Tasks: Claim Verification, Combinatorial Optimization, Informativeness

Optimal selection of a subset of items from a given set is a hard problem
that requires combinatorial optimization. In this paper, we propose a subset
selection algorithm that is trainable with gradient-based methods yet achieves
near-optimal performance via submodular optimization. We focus on the task of
identifying a relevant set of sentences for claim verification in the context
of the FEVER task. Conventional methods for this task look at sentences on
their individual merit and thus do not optimize the informativeness of
sentences as a set. We show that our proposed method which builds on the idea
of unfolding a greedy algorithm into a computational graph allows both
interpretability and gradient-based training. The proposed differentiable
greedy network (DGN) outperforms discrete optimization algorithms as well as
other baseline methods in terms of precision and recall.
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure
Authors: Ashkan Esmaeili, Farokh Marvasti
Date: 2018-10-29 00:00:00
Tasks: Matrix Completion, Quantization

In this paper, we introduce a novel and robust approach to Quantized Matrix
Completion (QMC). First, we propose a rank minimization problem with
constraints induced by quantization bounds. Next, we form an unconstrained
optimization problem by regularizing the rank function with Huber loss. Huber
loss is leveraged to control the violation from quantization bounds due to two
properties: 1- It is differentiable, 2- It is less sensitive to outliers than
the quadratic loss. A Smooth Rank Approximation is utilized to endorse lower
rank on the genuine data matrix. Thus, an unconstrained optimization problem
with differentiable objective function is obtained allowing us to advantage
from Gradient Descent (GD) technique. Novel and firm theoretical analysis on
problem model and convergence of our algorithm to the global solution are
provided. Another contribution of our work is that our method does not require
projections or initial rank estimation unlike the state- of-the-art. In the
Numerical Experiments Section, the noticeable outperformance of our proposed
method in learning accuracy and computational complexity compared to those of
the state-of- the-art literature methods is illustrated as the main
contribution.
Distributed Convex Optimization With Limited Communications
Authors: Milind Rao, Stefano Rini, Andrea Goldsmith
Date: 2018-10-29 00:00:00
Tasks: Distributed Optimization, valid

In this paper, a distributed convex optimization algorithm, termed
\emph{distributed coordinate dual averaging} (DCDA) algorithm, is proposed. The
DCDA algorithm addresses the scenario of a large distributed optimization
problem with limited communication among nodes in the network. Currently known
distributed subgradient methods, such as the distributed dual averaging or the
distributed alternating direction method of multipliers algorithms, assume that
nodes can exchange messages of large cardinality. Such network communication
capabilities are not valid in many scenarios of practical relevance. In the
DCDA algorithm, on the other hand, communication of each coordinate of the
optimization variable is restricted over time. For the proposed algorithm, we
bound the rate of convergence under different communication protocols and
network architectures. We also consider the extensions to the case of imperfect
gradient knowledge and the case in which transmitted messages are corrupted by
additive noise or are quantized. Relevant numerical simulations are also
provided.
A Simple Recurrent Unit with Reduced Tensor Product Representations
Authors: Shuai Tang, Paul Smolensky, Virginia R. de Sa
Date: 2018-10-29 00:00:00
Tasks: Natural Language Inference

idely used recurrent units, including Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting reduced Tensor Product Representations (TPRs) --- distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions --- we propose the TPRU, a simple recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. A gradient analysis of our proposed TPRU is conducted to support our model design, and its performance on multiple datasets shows the effectiveness of our design choices. Furthermore, observations on a linguistically grounded study demonstrate the interpretability of our TPRU.