pwc-archive/papers-with-abstracts huggingface dataset:
Goal-based Course Recommendation
Authors: Weijie Jiang, Zachary A. Pardos, Qiang Wei
Date: 2018-12-25 00:00:00
Tasks: Decision Making

With cross-disciplinary academic interests increasing and academic advising
resources over capacity, the importance of exploring data-assisted methods to
support student decision making has never been higher. We build on the findings
and methodologies of a quickly developing literature around prediction and
recommendation in higher education and develop a novel recurrent neural
network-based recommendation system for suggesting courses to help students
prepare for target courses of interest, personalized to their estimated prior
knowledge background and zone of proximal development. We validate the model
using tests of grade prediction and the ability to recover prerequisite
relationships articulated by the university. In the third validation, we run
the fully personalized recommendation for students the semester before taking a
historically difficult course and observe differential overlap with our
would-be suggestions. While not proof of causal effectiveness, these three
evaluation perspectives on the performance of the goal-based model build
confidence and bring us one step closer to deployment of this personalized
course preparation affordance in the wild.
Coupled Recurrent Network (CRN)
Authors: Lin Sun, Kui Jia, Yuejia Shen, Silvio Savarese, Dit Yan Yeung, Bertram E. Shi
Date: 2018-12-25 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Multi-Person Pose Estimation, Optical Flow Estimation, Pose Estimation, Temporal Action Localization

Many semantic video analysis tasks can benefit from multiple, heterogenous
signals. For example, in addition to the original RGB input sequences,
sequences of optical flow are usually used to boost the performance of human
action recognition in videos. To learn from these heterogenous input sources,
existing methods reply on two-stream architectural designs that contain
independent, parallel streams of Recurrent Neural Networks (RNNs). However,
two-stream RNNs do not fully exploit the reciprocal information contained in
the multiple signals, let alone exploit it in a recurrent manner. To this end,
we propose in this paper a novel recurrent architecture, termed Coupled
Recurrent Network (CRN), to deal with multiple input sources. In CRN, the
parallel streams of RNNs are coupled together. Key design of CRN is a Recurrent
Interpretation Block (RIB) that supports learning of reciprocal feature
representations from multiple signals in a recurrent manner. Different from
RNNs which stack the training loss at each time step or the last time step, we
propose an effective and efficient training strategy for CRN. Experiments show
the efficacy of the proposed CRN. In particular, we achieve the new state of
the art on the benchmark datasets of human action recognition and multi-person
pose estimation.
Learning based Facial Image Compression with Semantic Fidelity Metric
Authors: Zhibo Chen, Tianyu He
Date: 2018-12-25 00:00:00
Tasks: Face Recognition, Face Verification, Generative Adversarial Network, Image Compression

Surveillance and security scenarios usually require high efficient facial
image compression scheme for face recognition and identification. While either
traditional general image codecs or special facial image compression schemes
only heuristically refine codec separately according to face verification
accuracy metric. We propose a Learning based Facial Image Compression (LFIC)
framework with a novel Regionally Adaptive Pooling (RAP) module whose
parameters can be automatically optimized according to gradient feedback from
an integrated hybrid semantic fidelity metric, including a successfully
exploration to apply Generative Adversarial Network (GAN) as metric directly in
image compression scheme. The experimental results verify the framework's
efficiency by demonstrating performance improvement of 71.41%, 48.28% and
52.67% bitrate saving separately over JPEG2000, WebP and neural network-based
codecs under the same face verification accuracy distortion metric. We also
evaluate LFIC's superior performance gain compared with latest specific facial
image codecs. Visual experiments also show some interesting insight on how LFIC
can automatically capture the information in critical areas based on semantic
distortion metrics for optimized compression, which is quite different from the
heuristic way of optimization in traditional image compression algorithms.
Selectivity or Invariance: Boundary-aware Salient Object Detection
Authors: Jinming Su, Jia Li, Yu Zhang, Changqun Xia, Yonghong Tian
Date: 2018-12-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Typically, a salient object detection (SOD) model faces opposite requirements in processing object interiors and boundaries. The features of interiors should be invariant to strong appearance change so as to pop-out the salient object as a whole, while the features of boundaries should be selective to slight appearance change to distinguish salient objects and background. To address this selectivity-invariance dilemma, we propose a novel boundary-aware network with successive dilation for image-based SOD. In this network, the feature selectivity at boundaries is enhanced by incorporating a boundary localization stream, while the feature invariance at interiors is guaranteed with a complex interior perception stream. Moreover, a transition compensation stream is adopted to amend the probable failures in transitional regions between interiors and boundaries. In particular, an integrated successive dilation module is proposed to enhance the feature invariance at interiors and transitional regions. Extensive experiments on six datasets show that the proposed approach outperforms 16 state-of-the-art methods.
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition
Authors: Krishan Rajaratnam, Jugal Kalita
Date: 2018-12-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Neural models enjoy widespread use across a variety of tasks and have grown
to become crucial components of many industrial systems. Despite their
effectiveness and extensive popularity, they are not without their exploitable
flaws. Initially applied to computer vision systems, the generation of
adversarial examples is a process in which seemingly imperceptible
perturbations are made to an image, with the purpose of inducing a deep
learning based classifier to misclassify the image. Due to recent trends in
speech processing, this has become a noticeable issue in speech recognition
models. In late 2017, an attack was shown to be quite effective against the
Speech Commands classification model. Limited-vocabulary speech classifiers,
such as the Speech Commands model, are used quite frequently in a variety of
applications, particularly in managing automated attendants in telephony
contexts. As such, adversarial examples produced by this attack could have
real-world consequences. While previous work in defending against these
adversarial examples has investigated using audio preprocessing to reduce or
distort adversarial noise, this work explores the idea of flooding particular
frequency bands of an audio signal with random noise in order to detect
adversarial examples. This technique of flooding, which does not require
retraining or modifying the model, is inspired by work done in computer vision
and builds on the idea that speech classifiers are relatively robust to natural
noise. A combined defense incorporating 5 different frequency bands for
flooding the signal with noise outperformed other existing defenses in the
audio space, detecting adversarial examples with 91.8% precision and 93.5%
recall.
PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
Authors: Mehdi Jafarnia-Jahromi, Tasmin Chowdhury, Hsin-Tai Wu, Sayandev Mukherjee
Date: 2018-12-25 00:00:00
Tasks: Adversarial Defense, Deep Learning, General Classification

Deep neural networks have demonstrated cutting edge performance on various tasks including classification. However, it is well known that adversarially designed imperceptible perturbation of the input can mislead advanced classifiers. In this paper, Permutation Phase Defense (PPD), is proposed as a novel method to resist adversarial attacks. PPD combines random permutation of the image with phase component of its Fourier transform. The basic idea behind this approach is to turn adversarial defense problems analogously into symmetric cryptography, which relies solely on safekeeping of the keys for security. In PPD, safe keeping of the selected permutation ensures effectiveness against adversarial attacks. Testing PPD on MNIST and CIFAR-10 datasets yielded state-of-the-art robustness against the most powerful adversarial attacks currently available.
Parallel Clustering of Single Cell Transcriptomic Data with Split-Merge Sampling on Dirichlet Process Mixtures
Authors: Tiehang Duan, José P. Pinto, Xiaohui Xie
Date: 2018-12-25 00:00:00
Tasks: Clustering

Motivation: With the development of droplet based systems, massive single
cell transcriptome data has become available, which enables analysis of
cellular and molecular processes at single cell resolution and is instrumental
to understanding many biological processes. While state-of-the-art clustering
methods have been applied to the data, they face challenges in the following
aspects: (1) the clustering quality still needs to be improved; (2) most models
need prior knowledge on number of clusters, which is not always available; (3)
there is a demand for faster computational speed. Results: We propose to tackle
these challenges with Parallel Split Merge Sampling on Dirichlet Process
Mixture Model (the Para-DPMM model). Unlike classic DPMM methods that perform
sampling on each single data point, the split merge mechanism samples on the
cluster level, which significantly improves convergence and optimality of the
result. The model is highly parallelized and can utilize the computing power of
high performance computing (HPC) clusters, enabling massive clustering on huge
datasets. Experiment results show the model outperforms current widely used
models in both clustering quality and computational speed. Availability: Source
code is publicly available on
https://github.com/tiehangd/Para_DPMM/tree/master/Para_DPMM_package
Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach
Authors: Satoshi Takabe, Masayuki Imanishi, Tadashi Wadayama, Ryo Hayakawa, Kazunori Hayashi
Date: 2018-12-25 00:00:00
Tasks: parameter estimation

This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$. Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named the trainable projected gradient-detector (TPG-detector). The trainable internal parameters, such as the step-size parameter, can be optimized with standard deep learning techniques, i.e., the back propagation and stochastic gradient descent algorithms. This approach is referred to as data-driven tuning, and ensures fast convergence during parameter estimation in the proposed scheme. The TPG-detector mainly consists of matrix-vector product operations whose computational cost is proportional to $m n$ for each iteration. In addition, the number of trainable parameters in the TPG-detector is independent of the number of antennas. These features of the TPG-detector result in a fast and stable training process and reasonable scalability for large systems. Numerical simulations show that the proposed detector achieves a comparable detection performance to those of existing algorithms for massive overloaded MIMO channels, e.g., the state-of-the-art IW-SOAV detector, with a lower computation cost.
Building a Neural Semantic Parser from a Domain Ontology
Authors: Jianpeng Cheng, Siva Reddy, Mirella Lapata
Date: 2018-12-25 00:00:00
Tasks: Semantic Parsing

Semantic parsing is the task of converting natural language utterances into
machine interpretable meaning representations which can be executed against a
real-world environment such as a database. Scaling semantic parsing to
arbitrary domains faces two interrelated challenges: obtaining broad coverage
training data effectively and cheaply; and developing a model that generalizes
to compositional utterances and complex intentions. We address these challenges
with a framework which allows to elicit training data from a domain ontology
and bootstrap a neural parser which recursively builds derivations of logical
forms. In our framework meaning representations are described by sequences of
natural language templates, where each template corresponds to a decomposed
fragment of the underlying meaning representation. Although artificial,
templates can be understood and paraphrased by humans to create natural
utterances, resulting in parallel triples of utterances, meaning
representations, and their decompositions. These allow us to train a neural
semantic parser which learns to compose rules in deriving meaning
representations. We crowdsource training data on six domains, covering both
single-turn utterances which exhibit rich compositionality, and sequential
utterances where a complex task is procedurally performed in steps. We then
develop neural semantic parsers which perform such compositional tasks. In
general, our approach allows to deploy neural semantic parsers quickly and
cheaply from a given domain ontology.
MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma
Authors: Huai Chen, Yuxiao Qi, Yong Yin, Tengxiang Li, Xiaoqing Liu, Xiuli Li, Guanzhong Gong, Lisheng Wang
Date: 2018-12-25 00:00:00
Tasks: Decoder, Segmentation

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one single decoder to fuse them and obtain high-level features for NPC segmentation. A fusion block is presented to effectively fuse features from multi-modality MRI. It firstly recalibrates low-level features captured from modality-specific encoders to highlight both informative features and regions of interest, then fuses weighted features by a residual fusion block to keep balance between fused ones and high-level features from decoder. Moreover, a training strategy named self-transfer, which utilizes pre-trained modality-specific encoders to initialize multi-encoder-based network, is proposed to make full mining of information from different modalities of MRI. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments.
Attention Branch Network: Learning of Attention Mechanism for Visual Explanation
Authors: Hiroshi Fukui, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
Date: 2018-12-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decision Making, image-classification, Image Classification

Visual explanation enables human to understand the decision making of Deep
Convolutional Neural Network (CNN), but it is insufficient to contribute the
performance improvement. In this paper, we focus on the attention map for
visual explanation, which represents high response value as the important
region in image recognition. This region significantly improves the performance
of CNN by introducing an attention mechanism that focuses on a specific region
in an image. In this work, we propose Attention Branch Network (ABN), which
extends the top-down visual explanation model by introducing a branch structure
with an attention mechanism. ABN can be applicable to several image recognition
tasks by introducing a branch for attention mechanism and is trainable for the
visual explanation and image recognition in end-to-end manner. We evaluate ABN
on several image recognition tasks such as image classification, fine-grained
recognition, and multiple facial attributes recognition. Experimental results
show that ABN can outperform the accuracy of baseline models on these image
recognition tasks while generating an attention map for visual explanation. Our
code is available at
https://github.com/machine-perception-robotics-group/attention_branch_network.
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
Authors: Kai Wang, Yimin Lin, Luowei Wang, Liming Han, Minjie Hua, Xiang Wang, Shiguo Lian, Bill Huang
Date: 2018-12-25 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper presents a novel framework for simultaneously implementing
localization and segmentation, which are two of the most important vision-based
tasks for robotics. While the goals and techniques used for them were
considered to be different previously, we show that by making use of the
intermediate results of the two modules, their performance can be enhanced at
the same time. Our framework is able to handle both the instantaneous motion
and long-term changes of instances in localization with the help of the
segmentation result, which also benefits from the refined 3D pose information.
We conduct experiments on various datasets, and prove that our framework works
effectively on improving the precision and robustness of the two tasks and
outperforms existing localization and segmentation algorithms.
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multi-view Learning
Authors: Hong Tao, Chenping Hou, Dongyun Yi, Jubo Zhu, Dewen Hu
Date: 2018-12-25 00:00:00
Tasks: Clustering, MULTI-VIEW LEARNING

In real-world applications, not all instances in multi-view data are fully represented. To deal with incomplete data, Incomplete Multi-view Learning (IML) rises. In this paper, we propose the Joint Embedding Learning and Low-Rank Approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation based complete multi-view methods can be adapted to IML directly with the guidance of the framework. Thus, the JELLA framework improves the efficiency of processing incomplete multi-view data, and bridges the gap between complete multi-view learning and IML. Moreover, the JELLA framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose the Incomplete Multi-view Learning with Block Diagonal Representation (IML-BDR) method. Assuming that the sampled examples have approximate linear subspace structure, IML-BDR uses the block diagonal structure prior to learn the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the Successive Over-Relaxation optimization technique is devised for optimization. Experimental results on various datasets demonstrate the effectiveness of IML-BDR.
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?
Authors: Samet Oymak, Mahdi Soltanolkotabi
Date: 2018-12-25 00:00:00

Many modern learning tasks involve fitting nonlinear models to data which are
trained in an overparameterized regime where the parameters of the model exceed
the size of the training dataset. Due to this overparameterization, the
training loss may have infinitely many global minima and it is critical to
understand the properties of the solutions found by first-order optimization
schemes such as (stochastic) gradient descent starting from different
initializations. In this paper we demonstrate that when the loss has certain
properties over a minimally small neighborhood of the initial point, first
order methods such as (stochastic) gradient descent have a few intriguing
properties: (1) the iterates converge at a geometric rate to a global optima
even when the loss is nonconvex, (2) among all global optima of the loss the
iterates converge to one with a near minimal distance to the initial point, (3)
the iterates take a near direct route from the initial point to this global
optima. As part of our proof technique, we introduce a new potential function
which captures the precise tradeoff between the loss function and the distance
to the initial point as the iterations progress. For Stochastic Gradient
Descent (SGD), we develop novel martingale techniques that guarantee SGD never
leaves a small neighborhood of the initialization, even with rather large
learning rates. We demonstrate the utility of our general theory for a variety
of problem domains spanning low-rank matrix recovery to neural network
training. Underlying our analysis are novel insights that may have implications
for training and generalization of more sophisticated learning problems
including those involving deep neural network architectures.
Similarity R-C3D for Few-shot Temporal Activity Detection
Authors: Huijuan Xu, Bingyi Kang, Ximeng Sun, Jiashi Feng, Kate Saenko, Trevor Darrell
Date: 2018-12-25 00:00:00
Tasks: Action Detection, Activity Detection

Many activities of interest are rare events, with only a few labeled examples
available. Therefore models for temporal activity detection which are able to
learn from a few examples are desirable. In this paper, we present a
conceptually simple and general yet novel framework for few-shot temporal
activity detection which detects the start and end time of the few-shot input
activities in an untrimmed video. Our model is end-to-end trainable and can
benefit from more few-shot examples. At test time, each proposal is assigned
the label of the few-shot activity class corresponding to the maximum
similarity score. Our Similarity R-C3D method outperforms previous work on
three large-scale benchmarks for temporal activity detection (THUMOS14,
ActivityNet1.2, and ActivityNet1.3 datasets) in the few-shot setting. Our code
will be made available.
Study of Robust Diffusion Recursive Least Squares Algorithms with Side Information for Networked Agents
Authors: Y. Yu, R. C. de Lamare, Y. Zakharov
Date: 2018-12-24 00:00:00

This work develops a robust diffusion recursive least squares algorithm to
mitigate the performance degradation often experienced in networks of agents in
the presence of impulsive noise. This algorithm minimizes an exponentially
weighted least-squares cost function subject to a time-dependent constraint on
the squared norm of the intermediate estimate update at each node. With the
help of side information, the constraint is recursively updated in a diffusion
strategy. Moreover, a control strategy for resetting the constraint is also
proposed to retain good tracking capability when the estimated parameters
suddenly change. Simulations show the superiority of the proposed algorithm
over previously reported techniques in various impulsive noise scenarios.