pwc-archive/papers-with-abstracts huggingface dataset:
An Online Learning Approach for Dengue Fever Classification
Authors: Siddharth Srivastava, Sumit Soman, Astha Rai
Date: 2019-04-17 00:00:00
Tasks: Classification, Diagnostic, General Classification

This paper introduces a novel approach for dengue fever classification based
on online learning paradigms. The proposed approach is suitable for practical
implementation as it enables learning using only a few training samples. With
time, the proposed approach is capable of learning incrementally from the data
collected without need for retraining the model or redeployment of the
prediction engine. Additionally, we also provide a comprehensive evaluation of
machine learning methods for prediction of dengue fever. The input to the
proposed pipeline comprises of recorded patient symptoms and diagnostic
investigations. Offline classifier models have been employed to obtain baseline
scores to establish that the feature set is optimal for classification of
dengue. The primary benefit of the online detection model presented in the
paper is that it has been established to effectively identify patients with
high likelihood of dengue disease, and experiments on scalability in terms of
number of training and test samples validate the use of the proposed model.
Adversarial Defense Through Network Profiling Based Path Extraction
Authors: Yuxian Qiu, Jingwen Leng, Cong Guo, Quan Chen, Chao Li, Minyi Guo, Yuhao Zhu
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Defense

Recently, researchers have started decomposing deep neural network models according to their semantics or functions. Recent work has shown the effectiveness of decomposed functional blocks for defending adversarial attacks, which add small input perturbation to the input image to fool the DNN models. This work proposes a profiling-based method to decompose the DNN models to different functional blocks, which lead to the effective path as a new approach to exploring DNNs' internal organization. Specifically, the per-image effective path can be aggregated to the class-level effective path, through which we observe that adversarial images activate effective path different from normal images. We propose an effective path similarity-based method to detect adversarial images with an interpretable model, which achieve better accuracy and broader applicability than the state-of-the-art technique.
General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation
Authors: L. Nanni, S. Brahnam, S. Ghidoni, G. Maguolo
Date: 2019-04-17 00:00:00
Tasks: Data Augmentation, General Classification

Bioimage classification plays a crucial role in many biological problems. In this work, we present a new General Purpose (GenP) ensemble that boosts performance by combining local features, dense sampling features, and deep learning approaches. First, we introduce three new methods for data augmentation based on PCA/DCT; second, we show that different data augmentation approaches can boost the performance of an ensemble of CNNs; and, finally, we propose a set of handcrafted/learned descriptors that are highly generalizable. Each handcrafted descriptor is used to train a different Support Vector Machine (SVM), and the different SVMs are combined with the ensemble of CNNs. Our method is evaluated on a diverse set of bioimage classification problems. Results demonstrate that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of parameters (thus avoiding the risk of overfitting/overtraining).
Self-Attention Graph Pooling
Authors: Junhyun Lee, Inyeop Lee, Jaewoo Kang
Date: 2019-04-17 00:00:00
Tasks: Graph Classification

Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs. The method of generalizing the convolution operation to graphs has been proven to improve performance and is widely used. However, the method of applying downsampling to graphs is still difficult to perform and has room for improvement. In this paper, we propose a graph pooling method based on self-attention. Self-attention using graph convolution allows our pooling method to consider both node features and graph topology. To ensure a fair comparison, the same training procedures and model architectures were used for the existing pooling methods and our method. The experimental results demonstrate that our method achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters.
Bottleneck potentials in Markov Random Fields
Authors: Ahmed Abbas, Paul Swoboda
Date: 2019-04-17 00:00:00
Proceeding: ICCV 2019 10
Tasks: Combinatorial Optimization

We consider general discrete Markov Random Fields(MRFs) with additional bottleneck potentials which penalize the maximum (instead of the sum) over local potential value taken by the MRF-assignment. Bottleneck potentials or analogous constructions have been considered in (i) combinatorial optimization (e.g. bottleneck shortest path problem, the minimum bottleneck spanning tree problem, bottleneck function minimization in greedoids), (ii) inverse problems with $L_{\infty}$-norm regularization, and (iii) valued constraint satisfaction on the $(\min,\max)$-pre-semirings. Bottleneck potentials for general discrete MRFs are a natural generalization of the above direction of modeling work to Maximum-A-Posteriori (MAP) inference in MRFs. To this end, we propose MRFs whose objective consists of two parts: terms that factorize according to (i) $(\min,+)$, i.e. potentials as in plain MRFs, and (ii) $(\min,\max)$, i.e. bottleneck potentials. To solve the ensuing inference problem, we propose high-quality relaxations and efficient algorithms for solving them. We empirically show efficacy of our approach on large scale seismic horizon tracking problems.
End-to-End Speech Translation with Knowledge Distillation
Authors: Yuchen Liu, Hao Xiong, Zhongjun He, Jiajun Zhang, Hua Wu, Haifeng Wang, Cheng-qing Zong
Date: 2019-04-17 00:00:00
Tasks: Knowledge Distillation, speech-recognition, Speech Recognition, Translation

End-to-end speech translation (ST), which directly translates from source
language speech into target language text, has attracted intensive attentions
in recent years. Compared to conventional pipeline systems, end-to-end ST
models have advantages of lower latency, smaller model size and less error
propagation. However, the combination of speech recognition and text
translation in one model is more difficult than each of these two tasks. In
this paper, we propose a knowledge distillation approach to improve ST model by
transferring the knowledge from text translation model. Specifically, we first
train a text translation model, regarded as a teacher model, and then ST model
is trained to learn output probabilities from teacher model through knowledge
distillation. Experiments on English- French Augmented LibriSpeech and
English-Chinese TED corpus show that end-to-end ST is possible to implement on
both similar and dissimilar language pairs. In addition, with the instruction
of teacher model, end-to-end ST model can gain significant improvements by over
3.5 BLEU points.
Text Classification Algorithms: A Survey
Authors: Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura E. Barnes, Donald E. Brown
Date: 2019-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Dimensionality Reduction, General Classification, Survey, text-classification, Text Classification

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.
Collaboration Analysis Using Deep Learning
Authors: Zhang Guo, Kevin Yu, Rebecca Pearlman, Nassir Navab, Roghayeh Barmaki
Date: 2019-04-17 00:00:00
Tasks: Anatomy, Deep Learning, Object Recognition

The analysis of the collaborative learning process is one of the growing
fields of education research, which has many different analytic solutions. In
this paper, we provided a new solution to improve automated collaborative
learning analyses using deep neural networks. Instead of using self-reported
questionnaires, which are subject to bias and noise, we automatically extract
group-working information by object recognition results using Mask R-CNN
method. This process is based on detecting the people and other objects from
pictures and video clips of the collaborative learning process, then evaluate
the mobile learning performance using the collaborative indicators. We tested
our approach to automatically evaluate the group-work collaboration in a
controlled study of thirty-three dyads while performing an anatomy body
painting intervention. The results indicate that our approach recognizes the
differences of collaborations among teams of treatment and control groups in
the case study. This work introduces new methods for automated quality
prediction of collaborations among human-human interactions using computer
vision techniques.
Forecasting with time series imaging
Authors: Xixi Li, Yanfei Kang, Feng Li
Date: 2019-04-17 00:00:00
Tasks: Model Selection, Time Series, Time Series Analysis

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset.
Reinforcement Learning Based Emotional Editing Constraint Conversation Generation
Authors: Jia Li, Xiao Sun, Xing Wei, Changliang Li, Jian-Hua Tao
Date: 2019-04-17 00:00:00
Tasks: Multi-Task Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, the generation of conversation content based on deep neural
networks has attracted many researchers. However, traditional neural language
models tend to generate general replies, lacking logical and emotional factors.
This paper proposes a conversation content generation model that combines
reinforcement learning with emotional editing constraints to generate more
meaningful and customizable emotional replies. The model divides the replies
into three clauses based on pre-generated keywords and uses the emotional
editor to further optimize the final reply. The model combines multi-task
learning with multiple indicator rewards to comprehensively optimize the
quality of replies. Experiments shows that our model can not only improve the
fluency of the replies, but also significantly enhance the logical relevance
and emotional relevance of the replies.
Deep Fusion Network for Image Completion
Authors: Xin Hong, Pengfei Xiong, Renhe Ji, Haoqiang Fan
Date: 2019-04-17 00:00:00
Tasks: Decoder, Image Inpainting

Deep image completion usually fails to harmonically blend the restored image
into existing content, especially in the boundary area. This paper handles with
this problem from a new perspective of creating a smooth transition and
proposes a concise Deep Fusion Network (DFNet). Firstly, a fusion block is
introduced to generate a flexible alpha composition map for combining known and
unknown regions. The fusion block not only provides a smooth fusion between
restored and existing content, but also provides an attention map to make
network focus more on the unknown pixels. In this way, it builds a bridge for
structural and texture information, so that information can be naturally
propagated from known region into completion. Furthermore, fusion blocks are
embedded into several decoder layers of the network. Accompanied by the
adjustable loss constraints on each layer, more accurate structure information
are achieved. We qualitatively and quantitatively compare our method with other
state-of-the-art methods on Places2 and CelebA datasets. The results show the
superior performance of DFNet, especially in the aspects of harmonious texture
transition, texture detail and semantic structural consistency. Our source code
will be avaiable at: \url{https://github.com/hughplay/DFNet}
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Authors: Lei Liu, Jie Jiang, Wenjing Jia, Saeed Amirgholipour, Michelle Zeibots, Xiangjian He
Date: 2019-04-17 00:00:00
Tasks: Decoder

Counting people or objects with significantly varying scales and densities
has attracted much interest from the research community and yet it remains an
open problem. In this paper, we propose a simple but an efficient and effective
network, named DENet, which is composed of two components, i.e., a detection
network (DNet) and an encoder-decoder estimation network (ENet). We first run
DNet on an input image to detect and count individuals who can be segmented
clearly. Then, ENet is utilized to estimate the density maps of the remaining
areas, where the numbers of individuals cannot be detected. We propose a
modified Xception as an encoder for feature extraction and a combination of
dilated convolution and transposed convolution as a decoder. In the
ShanghaiTech Part A, UCF and WorldExpo'10 datasets, our DENet achieves lower
Mean Absolute Error (MAE) than those of the state-of-the-art methods.
Posterior-regularized REINFORCE for Instance Selection in Distant Supervision
Authors: Qi Zhang, Siliang Tang, Xiang Ren, Fei Wu, ShiLiang Pu, Yueting Zhuang
Date: 2019-04-17 00:00:00
Proceeding: NAACL 2019 6
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

This paper provides a new way to improve the efficiency of the REINFORCE
training process. We apply it to the task of instance selection in distant
supervision. Modeling the instance selection in one bag as a sequential
decision process, a reinforcement learning agent is trained to determine
whether an instance is valuable or not and construct a new bag with less noisy
instances. However unbiased methods, such as REINFORCE, could usually take much
time to train. This paper adopts posterior regularization (PR) to integrate
some domain-specific rules in instance selection using REINFORCE. As the
experiment results show, this method remarkably improves the performance of the
relation classifier trained on cleaned distant supervision dataset as well as
the efficiency of the REINFORCE training.
Sparseout: Controlling Sparsity in Deep Networks
Authors: Najeeb Khan, Ian Stavness
Date: 2019-04-17 00:00:00
Tasks: General Classification, image-classification, Image Classification, Language Modelling

Dropout is commonly used to help reduce overfitting in deep neural networks.
Sparsity is a potentially important property of neural networks, but is not
explicitly controlled by Dropout-based regularization. In this work, we propose
Sparseout a simple and efficient variant of Dropout that can be used to control
the sparsity of the activations in a neural network. We theoretically prove
that Sparseout is equivalent to an $L_q$ penalty on the features of a
generalized linear model and that Dropout is a special case of Sparseout for
neural networks. We empirically demonstrate that Sparseout is computationally
inexpensive and is able to control the desired level of sparsity in the
activations. We evaluated Sparseout on image classification and language
modelling tasks to see the effect of sparsity on these tasks. We found that
sparsity of the activations is favorable for language modelling performance
while image classification benefits from denser activations. Sparseout provides
a way to investigate sparsity in state-of-the-art deep learning models. Source
code for Sparseout could be found at
\url{https://github.com/najeebkhan/sparseout}.
Neural Message Passing for Multi-Label Classification
Authors: Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi
Date: 2019-04-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification (MLC) is the task of assigning a set of target
labels for a given sample. Modeling the combinatorial label interactions in MLC
has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural
Networks to efficiently model the joint prediction of multiple labels. LaMP
treats labels as nodes on a label-interaction graph and computes the hidden
representation of each label node conditioned on the input using
attention-based neural message passing. Attention enables LaMP to assign
different importance to neighbor nodes per label, learning how labels interact
(implicitly). The proposed models are simple, accurate, interpretable,
structure-agnostic, and applicable for predicting dense labels since LaMP is
incredibly parallelizable. We validate the benefits of LaMP on seven real-world
MLC datasets, covering a broad spectrum of input/output types and outperforming
the state-of-the-art results. Notably, LaMP enables intuitive interpretation of
how classifying each label depends on the elements of a sample and at the same
time rely on its interaction with other labels. We provide our code and
datasets at https://github.com/QData/LaMP
Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities
Authors: Xin Wen, Zhizhong Han, Xinyu Yin, Yu-Shen Liu
Date: 2019-04-17 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

Cross-modal retrieval aims to retrieve relevant data across different
modalities (e.g., texts vs. images). The common strategy is to apply
element-wise constraints between manually labeled pair-wise items to guide the
generators to learn the semantic relationships between the modalities, so that
the similar items can be projected close to each other in the common
representation subspace. However, such constraints often fail to preserve the
semantic structure between unpaired but semantically similar items (e.g. the
unpaired items with the same class label are more similar than items with
different labels). To address the above problem, we propose a novel cross-modal
similarity transferring (CMST) method to learn and preserve the semantic
relationships between unpaired items in an unsupervised way. The key idea is to
learn the quantitative similarities in single-modal representation subspace,
and then transfer them to the common representation subspace to establish the
semantic relationships between unpaired items across modalities. Experiments
show that our method outperforms the state-of-the-art approaches both in the
class-based and pair-based retrieval tasks.
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition
Authors: Jiabin Xue, Jiqing Han, Tieran Zheng, Xiang Gao, Jiaxing Guo
Date: 2019-04-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Lifelong learning, Multi-Task Learning, speech-recognition, Speech Recognition, Transfer Learning

Recently, data-driven based Automatic Speech Recognition (ASR) systems have
achieved state-of-the-art results. And transfer learning is often used when
those existing systems are adapted to the target domain, e.g., fine-tuning,
retraining. However, in the processes, the system parameters may well deviate
too much from the previously learned parameters. Thus, it is difficult for the
system training process to learn knowledge from target domains meanwhile not
forgetting knowledge from the previous learning process, which is called as
catastrophic forgetting (CF). In this paper, we attempt to solve the CF problem
with the lifelong learning and propose a novel multi-task learning (MTL)
training framework for ASR. It considers reserving original knowledge and
learning new knowledge as two independent tasks, respectively. On the one hand,
we constrain the new parameters not to deviate too far from the original
parameters and punish the new system when forgetting original knowledge. On the
other hand, we force the new system to solve new knowledge quickly. Then, a MTL
mechanism is employed to get the balance between the two tasks. We applied our
method to an End2End ASR task and obtained the best performance in both target
and original datasets.
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
Authors: Binxuan Huang, Kathleen M. Carley
Date: 2019-04-17 00:00:00
Tasks: Graph Neural Network, Graph Representation Learning, Representation Learning

In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units to capture the long-term dependency across layers, our methods can successfully identify important information during recursive neighborhood expansion. In our experiments, we show that our model class achieves state-of-the-art results on three benchmarks: the Pubmed, Reddit, and PPI network datasets. Our in-depth analyses also demonstrate that incorporating recurrent units is a simple yet effective method to prevent noisy information in graphs, which enables a deeper graph neural network.
People infer recursive visual concepts from just a few examples
Authors: Brenden M. Lake, Steven T. Piantadosi
Date: 2019-04-17 00:00:00
Tasks: Object Recognition

Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer causal "programs" -- latent generating processes with nontrivial algorithmic properties -- from one, two, or three visual examples. People were asked to extrapolate the programs in several ways, for both classifying and generating new examples. As a theory of these inductive abilities, we present a Bayesian program learning model that searches the space of programs for the best explanation of the observations. Although variable, people's judgments are broadly consistent with the model and inconsistent with several alternatives, including a pre-trained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmic abstractions from sparse input data.