pwc-archive/papers-with-abstracts huggingface dataset:
An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text
Authors: Suriyadeepan Ramamoorthy, Selvakumar Murugan
Date: 2018-01-02 00:00:00
Tasks: Event Extraction, General Classification, Machine Reading Comprehension, Question Answering, Reading Comprehension

Adverse reaction caused by drugs is a potentially dangerous problem which may
lead to mortality and morbidity in patients. Adverse Drug Event (ADE)
extraction is a significant problem in biomedical research. We model ADE
extraction as a Question-Answering problem and take inspiration from Machine
Reading Comprehension (MRC) literature, to design our model. Our objective in
designing such a model, is to exploit the local linguistic context in clinical
text and enable intra-sequence interaction, in order to jointly learn to
classify drug and disease entities, and to extract adverse reactions caused by
a given drug. Our model makes use of a self-attention mechanism to facilitate
intra-sequence interaction in a text sequence. This enables us to visualize and
understand how the network makes use of the local and wider context for
classification.
First-spike based visual categorization using reward-modulated STDP
Authors: Milad Mozafari, Saeed Reza Kheradpisheh, Timothée Masquelier, Abbas Nowzari-Dalini, Mohammad Ganjtabesh
Date: 2017-05-25 00:00:00
Tasks: Game of Go, Object Recognition, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has recently regained popularity, with major
achievements such as beating the European game of Go champion. Here, for the
first time, we show that RL can be used efficiently to train a spiking neural
network (SNN) to perform object recognition in natural images without using an
external classifier. We used a feedforward convolutional SNN and a temporal
coding scheme where the most strongly activated neurons fire first, while less
activated ones fire later, or not at all. In the highest layers, each neuron
was assigned to an object category, and it was assumed that the stimulus
category was the category of the first neuron to fire. If this assumption was
correct, the neuron was rewarded, i.e. spike-timing-dependent plasticity (STDP)
was applied, which reinforced the neuron's selectivity. Otherwise, anti-STDP
was applied, which encouraged the neuron to learn something else. As
demonstrated on various image datasets (Caltech, ETH-80, and NORB), this reward
modulated STDP (R-STDP) approach extracted particularly discriminative visual
features, whereas classic unsupervised STDP extracts any feature that
consistently repeats. As a result, R-STDP outperformed STDP on these datasets.
Furthermore, R-STDP is suitable for online learning, and can adapt to drastic
changes such as label permutations. Finally, it is worth mentioning that both
feature extraction and classification were done with spikes, using at most one
spike per neuron. Thus the network is hardware friendly and energy efficient.
Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions
Authors: Ke Li, Renzhi Chen, Dragan Savic, Xin Yao
Date: 2018-01-02 00:00:00
Tasks: Decision Making

Decomposition has become an increasingly popular technique for evolutionary
multi-objective optimization (EMO). A decomposition-based EMO algorithm is
usually designed to approximate a whole Pareto-optimal front (PF). However, in
practice, the decision maker (DM) might only be interested in her/his region of
interest (ROI), i.e., a part of the PF. Solutions outside that might be useless
or even noisy to the decision-making procedure. Furthermore, there is no
guarantee to find the preferred solutions when tackling many-objective
problems. This paper develops an interactive framework for the
decomposition-based EMO algorithm to lead a DM to the preferred solutions of
her/his choice. It consists of three modules, i.e., consultation, preference
elicitation and optimization. Specifically, after every several generations,
the DM is asked to score a few candidate solutions in a consultation session.
Thereafter, an approximated value function, which models the DM's preference
information, is progressively learned from the DM's behavior. In the preference
elicitation session, the preference information learned in the consultation
module is translated into the form that can be used in a decomposition-based
EMO algorithm, i.e., a set of reference points that are biased toward to the
ROI. The optimization module, which can be any decomposition-based EMO
algorithm in principle, utilizes the biased reference points to direct its
search process. Extensive experiments on benchmark problems with three to ten
objectives fully demonstrate the effectiveness of our proposed method for
finding the DM's preferred solutions.
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging
Authors: Afonso M. Teodoro, José M. Bioucas-Dias, Mário A. T. Figueiredo
Date: 2018-01-02 00:00:00
Tasks: Deblurring, Denoising, Image Deblurring, Image Denoising

The recently proposed plug-and-play (PnP) framework allows leveraging recent
developments in image denoising to tackle other, more involved, imaging inverse
problems. In a PnP method, a black-box denoiser is plugged into an iterative
algorithm, taking the place of a formal denoising step that corresponds to the
proximity operator of some convex regularizer. While this approach offers
flexibility and excellent performance, convergence of the resulting algorithm
may be hard to analyze, as most state-of-the-art denoisers lack an explicit
underlying objective function. In this paper, we propose a PnP approach where a
scene-adapted prior (i.e., where the denoiser is targeted to the specific scene
being imaged) is plugged into ADMM (alternating direction method of
multipliers), and prove convergence of the resulting algorithm. Finally, we
apply the proposed framework in two different imaging inverse problems:
hyperspectral sharpening/fusion and image deblurring from blurred/noisy image
pairs.
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
Authors: Kai Qiao, Chi Zhang, Linyuan Wang, Bin Yan, Jian Chen, Lei Zeng, Li Tong
Date: 2018-01-02 00:00:00
Tasks: Open-Ended Question Answering, SSIM

In neuroscience, all kinds of computation models were designed to answer the
open question of how sensory stimuli are encoded by neurons and conversely, how
sensory stimuli can be decoded from neuronal activities. Especially, functional
Magnetic Resonance Imaging (fMRI) studies have made many great achievements
with the rapid development of the deep network computation. However, comparing
with the goal of decoding orientation, position and object category from
activities in visual cortex, accurate reconstruction of image stimuli from
human fMRI is a still challenging work. In this paper, the capsule network
(CapsNet) architecture based visual reconstruction (CNAVR) method is developed
to reconstruct image stimuli. The capsule means containing a group of neurons
to perform the better organization of feature structure and representation,
inspired by the structure of cortical mini column including several hundred
neurons in primates. The high-level capsule features in the CapsNet includes
diverse features of image stimuli such as semantic class, orientation, location
and so on. We used these features to bridge between human fMRI and image
stimuli. We firstly employed the CapsNet to train the nonlinear mapping from
image stimuli to high-level capsule features, and from high-level capsule
features to image stimuli again in an end-to-end manner. After estimating the
serviceability of each voxel by encoding performance to accomplish the
selecting of voxels, we secondly trained the nonlinear mapping from
dimension-decreasing fMRI data to high-level capsule features. Finally, we can
predict the high-level capsule features with fMRI data, and reconstruct image
stimuli with the CapsNet. We evaluated the proposed CNAVR method on the dataset
of handwritten digital images, and exceeded about 10% than the accuracy of all
existing state-of-the-art methods on the structural similarity index (SSIM).
A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials
Authors: Måns Larsson, Anurag Arnab, Fredrik Kahl, Shuai Zheng, Philip Torr
Date: 2017-01-24 00:00:00
Tasks: Segmentation, Semantic Segmentation, Structured Prediction

Are we using the right potential functions in the Conditional Random Field
models that are popular in the Vision community? Semantic segmentation and
other pixel-level labelling tasks have made significant progress recently due
to the deep learning paradigm. However, most state-of-the-art structured
prediction methods also include a random field model with a hand-crafted
Gaussian potential to model spatial priors, label consistencies and
feature-based image conditioning.
  In this paper, we challenge this view by developing a new inference and
learning framework which can learn pairwise CRF potentials restricted only by
their dependence on the image pixel values and the size of the support. Both
standard spatial and high-dimensional bilateral kernels are considered. Our
framework is based on the observation that CRF inference can be achieved via
projected gradient descent and consequently, can easily be integrated in deep
neural networks to allow for end-to-end training. It is empirically
demonstrated that such learned potentials can improve segmentation accuracy and
that certain label class interactions are indeed better modelled by a
non-Gaussian potential. In addition, we compare our inference method to the
commonly used mean-field algorithm. Our framework is evaluated on several
public benchmarks for semantic segmentation with improved performance compared
to previous state-of-the-art CNN+CRF models.
S-Net: From Answer Extraction to Answer Generation for Machine Reading Comprehension
Authors: Chuanqi Tan, Furu Wei, Nan Yang, Bowen Du, Weifeng Lv, Ming Zhou
Date: 2017-06-15 00:00:00
Tasks: Answer Generation, Machine Reading Comprehension, Passage Ranking, Reading Comprehension

In this paper, we present a novel approach to machine reading comprehension
for the MS-MARCO dataset. Unlike the SQuAD dataset that aims to answer a
question with exact text spans in a passage, the MS-MARCO dataset defines the
task as answering a question from multiple passages and the words in the answer
are not necessary in the passages. We therefore develop an
extraction-then-synthesis framework to synthesize answers from extraction
results. Specifically, the answer extraction model is first employed to predict
the most important sub-spans from the passage as evidence, and the answer
synthesis model takes the evidence as additional features along with the
question and passage to further elaborate the final answers. We build the
answer extraction model with state-of-the-art neural networks for single
passage reading comprehension, and propose an additional task of passage
ranking to help answer extraction in multiple passages. The answer synthesis
model is based on the sequence-to-sequence neural networks with extracted
evidences as features. Experiments show that our extraction-then-synthesis
method outperforms state-of-the-art methods.
Did you hear that? Adversarial Examples Against Automatic Speech Recognition
Authors: Moustafa Alzantot, Bharathan Balaji, Mani Srivastava
Date: 2018-01-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), object-detection, Object Detection, speech-recognition, Speech Recognition

Speech is a common and effective way of communication between humans, and
modern consumer devices such as smartphones and home hubs are equipped with
deep learning based accurate automatic speech recognition to enable natural
interaction between humans and machines. Recently, researchers have
demonstrated powerful attacks against machine learning models that can fool
them to produceincorrect results. However, nearly all previous research in
adversarial attacks has focused on image recognition and object detection
models. In this short paper, we present a first of its kind demonstration of
adversarial attacks against speech classification model. Our algorithm performs
targeted attacks with 87% success by adding small background noise without
having to know the underlying model parameter and architecture. Our attack only
changes the least significant bits of a subset of audio clip samples, and the
noise does not change 89% the human listener's perception of the audio clip as
evaluated in our human study.
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Authors: Yu-jia Zhang, Xiaodan Liang, Dingwen Zhang, Min Tan, Eric P. Xing
Date: 2018-01-02 00:00:00
Tasks: Object, Unsupervised Video Summarization, Video Summarization

Unsupervised video summarization plays an important role on digesting,
browsing, and searching the ever-growing videos every day, and the underlying
fine-grained semantic and motion information (i.e., objects of interest and
their key motions) in online videos has been barely touched. In this paper, we
investigate a pioneer research direction towards the fine-grained unsupervised
object-level video summarization. It can be distinguished from existing
pipelines in two aspects: extracting key motions of participated objects, and
learning to summarize in an unsupervised and online manner. To achieve this
goal, we propose a novel online motion Auto-Encoder (online motion-AE)
framework that functions on the super-segmented object motion clips.
Comprehensive experiments on a newly-collected surveillance dataset and public
datasets have demonstrated the effectiveness of our proposed method.
Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task Learning
Authors: Yuhang Song, Main Xu, Songyang Zhang, Liangyu Huo
Date: 2017-10-27 00:00:00
Tasks: Atari Games, Multi-Task Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning (DL) advances state-of-the-art reinforcement learning (RL), by
incorporating deep neural networks in learning representations from the input
to RL. However, the conventional deep neural network architecture is limited in
learning representations for multi-task RL (MT-RL), as multiple tasks can refer
to different kinds of representations. In this paper, we thus propose a novel
deep neural network architecture, namely generalization tower network (GTN),
which can achieve MT-RL within a single learned model. Specifically, the
architecture of GTN is composed of both horizontal and vertical streams. In our
GTN architecture, horizontal streams are used to learn representation shared in
similar tasks. In contrast, the vertical streams are introduced to be more
suitable for handling diverse tasks, which encodes hierarchical shared
knowledge of these tasks. The effectiveness of the introduced vertical stream
is validated by experimental results. Experimental results further verify that
our GTN architecture is able to advance the state-of-the-art MT-RL, via being
tested on 51 Atari games.
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach
Authors: Yuhang Song, Mai Xu, Jianyi Wang, Minglang Qiao, Liangyu Huo, Zulin Wang
Date: 2017-10-30 00:00:00
Tasks: Deep Reinforcement Learning, Position, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects' HM in panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL-based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
Learning Multimodal Word Representation via Dynamic Fusion Methods
Authors: Shaonan Wang, Jiajun Zhang, Cheng-qing Zong
Date: 2018-01-02 00:00:00

Multimodal models have been proven to outperform text-based models on
learning semantic word representations. Almost all previous multimodal models
typically treat the representations from different modalities equally. However,
it is obvious that information from different modalities contributes
differently to the meaning of words. This motivates us to build a multimodal
model that can dynamically fuse the semantic representations from different
modalities according to different types of words. To that end, we propose three
novel dynamic fusion methods to assign importance weights to each modality, in
which weights are learned under the weak supervision of word association pairs.
The extensive experiments have demonstrated that the proposed methods
outperform strong unimodal baselines and state-of-the-art multimodal models.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction
Authors: Dan Xu, Wanli Ouyang, Xavier Alameda-Pineda, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Date: 2018-01-01 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Contour Detection

Recent works have shown that exploiting multi-scale representations deeply
learned via convolutional neural networks (CNN) is of tremendous importance for
accurate contour detection. This paper presents a novel approach for predicting
contours which advances the state of the art in two fundamental aspects, i.e.
multi-scale feature generation and fusion. Different from previous works
directly consider- ing multi-scale feature maps obtained from the inner layers
of a primary CNN architecture, we introduce a hierarchical deep model which
produces more rich and complementary representations. Furthermore, to refine
and robustly fuse the representations learned at different scales, the novel
Attention-Gated Conditional Random Fields (AG-CRFs) are proposed. The
experiments ran on two publicly available datasets (BSDS500 and NYUDv2)
demonstrate the effectiveness of the latent AG-CRF model and of the overall
hierarchical framework.
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
Authors: Dan Xu, Wanli Ouyang, Elisa Ricci, Xiaogang Wang, Nicu Sebe
Date: 2017-04-08 00:00:00
Proceeding: CVPR 2017 7
Tasks: Pedestrian Detection

This paper presents a novel method for detecting pedestrians under adverse
illumination conditions. Our approach relies on a novel cross-modality learning
framework and it is based on two main phases. First, given a multimodal
dataset, a deep convolutional network is employed to learn a non-linear
mapping, modeling the relations between RGB and thermal data. Then, the learned
feature representations are transferred to a second deep network, which
receives as input an RGB image and outputs the detection results. In this way,
features which are both discriminative and robust to bad illumination
conditions are learned. Importantly, at test time, only the second pipeline is
considered and no thermal data are required. Our extensive evaluation
demonstrates that the proposed approach outperforms the state-of- the-art on
the challenging KAIST multispectral pedestrian dataset and it is competitive
with previous methods on the popular Caltech dataset.
Local Causal States and Discrete Coherent Structures
Authors: Adam Rupe, James P. Crutchfield
Date: 2018-01-01 00:00:00

Coherent structures form spontaneously in nonlinear spatiotemporal systems and are found at all spatial scales in natural phenomena from laboratory hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary climate dynamics. Phenomenologically, they appear as key components that organize the macroscopic behaviors in such systems. Despite a century of effort, they have eluded rigorous analysis and empirical prediction, with progress being made only recently. As a step in this, we present a formal theory of coherent structures in fully-discrete dynamical field theories. It builds on the notion of structure introduced by computational mechanics, generalizing it to a local spatiotemporal setting. The analysis' main tool employs the \localstates, which are used to uncover a system's hidden spatiotemporal symmetries and which identify coherent structures as spatially-localized deviations from those symmetries. The approach is behavior-driven in the sense that it does not rely on directly analyzing spatiotemporal equations of motion, rather it considers only the spatiotemporal fields a system generates. As such, it offers an unsupervised approach to discover and describe coherent structures. We illustrate the approach by analyzing coherent structures generated by elementary cellular automata, comparing the results with an earlier, dynamic-invariant-set approach that decomposes fields into domains, particles, and particle interactions.
Depth-Adaptive Computational Policies for Efficient Visual Tracking
Authors: Chris Ying, Katerina Fragkiadaki
Date: 2018-01-01 00:00:00
Tasks: Object, Object Tracking, Video Object Tracking, Visual Tracking

Current convolutional neural networks algorithms for video object tracking
spend the same amount of computation for each object and video frame. However,
it is harder to track an object in some frames than others, due to the varying
amount of clutter, scene complexity, amount of motion, and object's
distinctiveness against its background. We propose a depth-adaptive
convolutional Siamese network that performs video tracking adaptively at
multiple neural network depths. Parametric gating functions are trained to
control the depth of the convolutional feature extractor by minimizing a joint
loss of computational cost and tracking error. Our network achieves accuracy
comparable to the state-of-the-art on the VOT2016 benchmark. Furthermore, our
adaptive depth computation achieves higher accuracy for a given computational
cost than traditional fixed-structure neural networks. The presented framework
extends to other tasks that use convolutional neural networks and enables
trading speed for accuracy at runtime.
MACRO: A Meta-Algorithm for Conditional Risk Minimization
Authors: Alexander Zimin, Christoph Lampert
Date: 2018-01-01 00:00:00

We study conditional risk minimization (CRM), i.e. the problem of learning a
hypothesis of minimal risk for prediction at the next step of sequentially
arriving dependent data. Despite it being a fundamental problem, successful
learning in the CRM sense has so far only been demonstrated using theoretical
algorithms that cannot be used for real problems as they would require storing
all incoming data. In this work, we introduce MACRO, a meta-algorithm for CRM
that does not suffer from this shortcoming, but nevertheless offers learning
guarantees. Instead of storing all data it maintains and iteratively updates a
set of learning subroutines. With suitable approximations, MACRO applied to
real data, yielding improved prediction performance compared to traditional
non-conditional learning.