pwc-archive/papers-with-abstracts huggingface dataset:
Batch Virtual Adversarial Training for Graph Convolutional Networks
Authors: Zhijie Deng, Yinpeng Dong, Jun Zhu
Date: 2019-02-25 00:00:00
Tasks: General Classification, Node Classification

We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs). BVAT addresses the shortcoming of GCNs that do not consider the smoothness of the model's output distribution against local perturbations around the input. We propose two algorithms, sample-based BVAT and optimization-based BVAT, which are suitable to promote the smoothness of the model for graph-structured data by either finding virtual adversarial perturbations for a subset of nodes far from each other or generating virtual adversarial perturbations for all nodes with an optimization process. Extensive experiments on three citation network datasets Cora, Citeseer and Pubmed and a knowledge graph dataset Nell validate the effectiveness of the proposed method, which establishes state-of-the-art results in the semi-supervised node classification tasks.
Improving Neural Response Diversity with Frequency-Aware Cross-Entropy Loss
Authors: Shaojie Jiang, Pengjie Ren, Christof Monz, Maarten de Rijke
Date: 2019-02-25 00:00:00
Tasks: Diversity, Response Generation

Sequence-to-Sequence (Seq2Seq) models have achieved encouraging performance
on the dialogue response generation task. However, existing Seq2Seq-based
response generation methods suffer from a low-diversity problem: they
frequently generate generic responses, which make the conversation less
interesting. In this paper, we address the low-diversity problem by
investigating its connection with model over-confidence reflected in predicted
distributions. Specifically, we first analyze the influence of the commonly
used Cross-Entropy (CE) loss function, and find that the CE loss function
prefers high-frequency tokens, which results in low-diversity responses. We
then propose a Frequency-Aware Cross-Entropy (FACE) loss function that improves
over the CE loss function by incorporating a weighting mechanism conditioned on
token frequency. Extensive experiments on benchmark datasets show that the FACE
loss function is able to substantially improve the diversity of existing
state-of-the-art Seq2Seq response generation methods, in terms of both
automatic and human evaluations.
Towards Corner Case Detection for Autonomous Driving
Authors: Jan-Aike Bolte, Andreas Bär, Daniel Lipinski, Tim Fingscheidt
Date: 2019-02-25 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, BIG-bench Machine Learning

The progress in autonomous driving is also due to the increased availability
of vast amounts of training data for the underlying machine learning
approaches. Machine learning systems are generally known to lack robustness,
e.g., if the training data did rarely or not at all cover critical situations.
The challenging task of corner case detection in video, which is also somehow
related to unusual event or anomaly detection, aims at detecting these unusual
situations, which could become critical, and to communicate this to the
autonomous driving system (online use case). Such a system, however, could be
also used in offline mode to screen vast amounts of data and select only the
relevant situations for storing and (re)training machine learning algorithms.
So far, the approaches for corner case detection have been limited to videos
recorded from a fixed camera, mostly for security surveillance. In this paper,
we provide a formal definition of a corner case and propose a system framework
for both the online and the offline use case that can handle video signals from
front cameras of a naturally moving vehicle and can output a corner case score.
Joint Multi-Domain Learning for Automatic Short Answer Grading
Authors: Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya, Peter Foltz, Renuka Sindhgatta, Bikram Sengupta
Date: 2019-02-25 00:00:00
Tasks: automatic short answer grading, Benchmarking, Domain Adaptation

One of the fundamental challenges towards building any intelligent tutoring
system is its ability to automatically grade short student answers. A typical
automatic short answer grading system (ASAG) grades student answers across
multiple domains (or subjects). Grading student answers requires building a
supervised machine learning model that evaluates the similarity of the student
answer with the reference answer(s). We observe that unlike typical textual
similarity or entailment tasks, the notion of similarity is not universal here.
On one hand, para-phrasal constructs of the language can indicate similarity
independent of the domain. On the other hand, two words, or phrases, that are
not strict synonyms of each other, might mean the same in certain domains.
Building on this observation, we propose JMD-ASAG, the first joint multidomain
deep learning architecture for automatic short answer grading that performs
domain adaptation by learning generic and domain-specific aspects from the
limited domain-wise training data. JMD-ASAG not only learns the domain-specific
characteristics but also overcomes the dependence on a large corpus by learning
the generic characteristics from the task-specific data itself. On a
large-scale industry dataset and a benchmarking dataset, we show that our model
performs significantly better than existing techniques which either learn
domain-specific models or adapt a generic similarity scoring model from a large
corpus. Further, on the benchmarking dataset, we report state-of-the-art
results against all existing non-neural and neural models.
GFCN: A New Graph Convolutional Network Based on Parallel Flows
Authors: Feng Ji, Jielong Yang, Qiang Zhang, Wee Peng Tay
Date: 2019-02-25 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object Recognition

In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory and graph signal processing. In this paper, we study the problem from a completely different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that the our method, which we call GraphFlow, is able to transfer CNN architectures to general graphs. To show the effectiveness of our approach, we test our method on the classical MNIST dataset, synthetic datasets on network information propagation and a news article classification dataset.
Improved Algorithm on Online Clustering of Bandits
Authors: Wei Chen, Shuai Li, Kwong-Sak Leung
Date: 2019-02-25 00:00:00
Tasks: Clustering, Online Clustering

We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments on both synthetic and real datasets consistently show the advantage of the new algorithm over existing methods.
A Survey of Crowdsourcing in Medical Image Analysis
Authors: Silas Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina
Date: 2019-02-25 00:00:00
Tasks: Medical Image Analysis, Survey

Rapid advances in image processing capabilities have been seen across many domains, fostered by the application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.
Deep Bayesian Multi-Target Learning for Recommender Systems
Authors: Qi. Wang, Zhihui Ji, Huasheng Liu, Binqiang Zhao
Date: 2019-02-25 00:00:00
Tasks: Model Selection, Recommendation Systems

With the increasing variety of services that e-commerce platforms provide,
criteria for evaluating their success become also increasingly multi-targeting.
This work introduces a multi-target optimization framework with Bayesian
modeling of the target events, called Deep Bayesian Multi-Target Learning
(DBMTL). In this framework, target events are modeled as forming a Bayesian
network, in which directed links are parameterized by hidden layers, and
learned from training samples. The structure of Bayesian network is determined
by model selection. We applied the framework to Taobao live-streaming
recommendation, to simultaneously optimize (and strike a balance) on targets
including click-through rate, user stay time in live room, purchasing behaviors
and interactions. Significant improvement has been observed for the proposed
method over other MTL frameworks and the non-MTL model. Our practice shows that
with an integrated causality structure, we can effectively make the learning of
a target benefit from other targets, creating significant synergy effects that
improve all targets. The neural network construction guided by DBMTL fits in
with the general probabilistic model connecting features and multiple targets,
taking weaker assumption than the other methods discussed in this paper. This
theoretical generality brings about practical generalization power over various
targets distributions, including sparse targets and continuous-value ones.
DDFlow: Learning Optical Flow with Unlabeled Data Distillation
Authors: Pengpeng Liu, Irwin King, Michael R. Lyu, Jia Xu
Date: 2019-02-25 00:00:00
Tasks: Optical Flow Estimation

We present DDFlow, a data distillation approach to learning optical flow
estimation from unlabeled data. The approach distills reliable predictions from
a teacher network, and uses these predictions as annotations to guide a student
network to learn optical flow. Unlike existing work relying on hand-crafted
energy terms to handle occlusion, our approach is data-driven, and learns
optical flow for occluded pixels. This enables us to train our model with a
much simpler loss function, and achieve a much higher accuracy. We conduct a
rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012
and 2015 benchmarks, and show that our approach significantly outperforms all
existing unsupervised learning methods, while running at real time.
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing
Authors: Longfei Ren, Chengjing Wang, Peipei Tang, Zheng Ma
Date: 2019-02-25 00:00:00
Tasks: Hyperspectral Unmixing

Since sparse unmixing has emerged as a promising approach to hyperspectral unmixing, some spatial-contextual information in the hyperspectral images has been exploited to improve the performance of the unmixing recently. The total variation (TV) has been widely used to promote the spatial homogeneity as well as the smoothness between adjacent pixels. However, the computation task for hyperspectral sparse unmixing with a TV regularization term is heavy. Besides, the convergence of the primal alternating direction method of multipliers (ADMM) for the hyperspectral sparse unmixing with a TV regularization term has not been explained in details. In this paper, we design an efficient and convergent dual symmetric Gauss-Seidel ADMM (sGS-ADMM) for hyperspectral sparse unmixing with a TV regularization term. We also present the global convergence and local linear convergence rate analysis for this algorithm. As demonstrated in numerical experiments, our algorithm can obviously improve the efficiency of the unmixing compared with the state-of-the-art algorithm. More importantly, we can obtain images with higher quality.
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Authors: Chenyang Si, Wentao Chen, Wei Wang, Liang Wang, Tieniu Tan
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based action recognition is an important task that requires the
adequate understanding of movement characteristics of a human action from the
given skeleton sequence. Recent studies have shown that exploring spatial and
temporal features of the skeleton sequence is vital for this task.
Nevertheless, how to effectively extract discriminative spatial and temporal
features is still a challenging problem. In this paper, we propose a novel
Attention Enhanced Graph Convolutional LSTM Network (AGC-LSTM) for human action
recognition from skeleton data. The proposed AGC-LSTM can not only capture
discriminative features in spatial configuration and temporal dynamics but also
explore the co-occurrence relationship between spatial and temporal domains. We
also present a temporal hierarchical architecture to increases temporal
receptive fields of the top AGC-LSTM layer, which boosts the ability to learn
the high-level semantic representation and significantly reduces the
computation cost. Furthermore, to select discriminative spatial information,
the attention mechanism is employed to enhance information of key joints in
each AGC-LSTM layer. Experimental results on two datasets are provided: NTU
RGB+D dataset and Northwestern-UCLA dataset. The comparison results demonstrate
the effectiveness of our approach and show that our approach outperforms the
state-of-the-art methods on both datasets.
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
Authors: Yaniv David, Uri Alon, Eran Yahav
Date: 2019-02-25 00:00:00

We address the problem of reverse engineering of stripped executables, which contain no debug information. This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations. We present a novel approach for predicting procedure names in stripped executables. Our approach combines static analysis with neural models. The main idea is to use static analysis to obtain augmented representations of call sites; encode the structure of these call sites using the control-flow graph (CFG) and finally, generate a target name while attending to these call sites. We use our representation to drive graph-based, LSTM-based and Transformer-based architectures. Our evaluation shows that our models produce predictions that are difficult and time consuming for humans, while improving on existing methods by 28% and by 100% over state-of-the-art neural textual models that do not use any static analysis. Code and data for this evaluation are available at https://github.com/tech-srl/Nero .
Star-Transformer
Authors: Qipeng Guo, Xipeng Qiu, PengFei Liu, Yunfan Shao, xiangyang xue, Zheng Zhang
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Named Entity Recognition (NER), Natural Language Inference, Sentiment Analysis, Text Classification

Although Transformer has achieved great successes on many NLP tasks, its heavy structure with fully-connected attention connections leads to dependencies on large training data. In this paper, we present Star-Transformer, a lightweight alternative by careful sparsification. To reduce model complexity, we replace the fully-connected structure with a star-shaped topology, in which every two non-adjacent nodes are connected through a shared relay node. Thus, complexity is reduced from quadratic to linear, while preserving capacity to capture both local composition and long-range dependency. The experiments on four tasks (22 datasets) show that Star-Transformer achieved significant improvements against the standard Transformer for the modestly sized datasets.
Graph Convolutional Neural Networks via Motif-based Attention
Authors: Hao Peng, Jian-Xin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, Philip S. Yu
Date: 2018-11-11 00:00:00
Tasks: General Classification, Graph Classification

Many real-world problems can be represented as graph-based learning problems.
In this paper, we propose a novel framework for learning spatial and
attentional convolution neural networks on arbitrary graphs. Different from
previous convolutional neural networks on graphs, we first design a
motif-matching guided subgraph normalization method to capture neighborhood
information. Then we implement subgraph-level self-attentional layers to learn
different importances from different subgraphs to solve graph classification
problems. Analogous to image-based attentional convolution networks that
operate on locally connected and weighted regions of the input, we also extend
graph normalization from one-dimensional node sequence to two-dimensional node
grid by leveraging motif-matching, and design self-attentional layers without
requiring any kinds of cost depending on prior knowledge of the graph
structure. Our results on both bioinformatics and social network datasets show
that we can significantly improve graph classification benchmarks over
traditional graph kernel and existing deep models.
Visualization, Discriminability and Applications of Interpretable Saak Features
Authors: Abinaya Manimaran, Thiyagarajan Ramanathan, Suya You, C-C Jay Kuo
Date: 2019-02-25 00:00:00
Tasks: General Classification, image-classification, Image Classification

In this work, we study the power of Saak features as an effort towards
interpretable deep learning. Being inspired by the operations of convolutional
layers of convolutional neural networks, multi-stage Saak transform was
proposed. Based on this foundation, we provide an in-depth examination on Saak
features, which are coefficients of the Saak transform, by analyzing their
properties through visualization and demonstrating their applications in image
classification. Being similar to CNN features, Saak features at later stages
have larger receptive fields, yet they are obtained in a one-pass feedforward
manner without backpropagation. The whole feature extraction process is
transparent and is of extremely low complexity. The discriminant power of Saak
features is demonstrated, and their classification performance in three
well-known datasets (namely, MNIST, CIFAR-10 and STL-10) is shown by
experimental results.
Dynamic Feature Fusion for Semantic Edge Detection
Authors: Yuan Hu, Yunpeng Chen, Xiang Li, Jiashi Feng
Date: 2019-02-25 00:00:00
Tasks: Edge Detection

Features from multiple scales can greatly benefit the semantic edge detection
task if they are well fused. However, the prevalent semantic edge detection
methods apply a fixed weight fusion strategy where images with different
semantics are forced to share the same weights, resulting in universal fusion
weights for all images and locations regardless of their different semantics or
local context. In this work, we propose a novel dynamic feature fusion strategy
that assigns different fusion weights for different input images and locations
adaptively. This is achieved by a proposed weight learner to infer proper
fusion weights over multi-level features for each location of the feature map,
conditioned on the specific input. In this way, the heterogeneity in
contributions made by different locations of feature maps and input images can
be better considered and thus help produce more accurate and sharper edge
predictions. We show that our model with the novel dynamic feature fusion is
superior to fixed weight fusion and also the na\"ive location-invariant weight
fusion methods, via comprehensive experiments on benchmarks Cityscapes and SBD.
In particular, our method outperforms all existing well established methods and
achieves new state-of-the-art.
Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
Authors: Tianwei Shen, Zixin Luo, Lei Zhou, Hanyu Deng, Runze Zhang, Tian Fang, Long Quan
Date: 2019-02-25 00:00:00
Tasks: Camera Pose Estimation, Motion Estimation, Self-Supervised Learning, Simultaneous Localization and Mapping, Visual Odometry

Accurate relative pose is one of the key components in visual odometry (VO)
and simultaneous localization and mapping (SLAM). Recently, the self-supervised
learning framework that jointly optimizes the relative pose and target image
depth has attracted the attention of the community. Previous works rely on the
photometric error generated from depths and poses between adjacent frames,
which contains large systematic error under realistic scenes due to reflective
surfaces and occlusions. In this paper, we bridge the gap between geometric
loss and photometric loss by introducing the matching loss constrained by
epipolar geometry in a self-supervised framework. Evaluated on the KITTI
dataset, our method outperforms the state-of-the-art unsupervised ego-motion
estimation methods by a large margin. The code and data are available at
https://github.com/hlzz/DeepMatchVO.
Marathon Environments: Multi-Agent Continuous Control Benchmarks in a Modern Video Game Engine
Authors: Joe Booth, Jackson Booth
Date: 2019-02-25 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Navigate, Reinforcement Learning, Unity

Recent advances in deep reinforcement learning in the paradigm of locomotion
using continuous control have raised the interest of game makers for the
potential of digital actors using active ragdoll. Currently, the available
options to develop these ideas are either researchers' limited codebase or
proprietary closed systems. We present Marathon Environments, a suite of open
source, continuous control benchmarks implemented on the Unity game engine,
using the Unity ML- Agents Toolkit. We demonstrate through these benchmarks
that continuous control research is transferable to a commercial game engine.
Furthermore, we exhibit the robustness of these environments by reproducing
advanced continuous control research, such as learning to walk, run and
backflip from motion capture data; learning to navigate complex terrains; and
by implementing a video game input control system. We show further robustness
by training with alternative algorithms found in OpenAI.Baselines. Finally, we
share strategies for significantly reducing the training time.