pwc-archive/papers-with-abstracts huggingface dataset:
Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
Authors: Sebastian Castaño-Candamil, Andreas Meinel, Michael Tangermann
Date: 2017-11-22 00:00:00
Tasks: Benchmarking, EEG, Electroencephalogram (EEG)

Many cognitive, sensory and motor processes have correlates in oscillatory
neural sources, which are embedded as a subspace into the recorded brain
signals. Decoding such processes from noisy
magnetoencephalogram/electroencephalogram (M/EEG) signals usually requires the
use of data-driven analysis methods. The objective evaluation of such decoding
algorithms on experimental raw signals, however, is a challenge: the amount of
available M/EEG data typically is limited, labels can be unreliable, and raw
signals often are contaminated with artifacts. The latter is specifically
problematic, if the artifacts stem from behavioral confounds of the oscillatory
neural processes of interest.
  To overcome some of these problems, simulation frameworks have been
introduced for benchmarking decoding methods. Generating artificial brain
signals, however, most simulation frameworks make strong and partially
unrealistic assumptions about brain activity, which limits the generalization
of obtained results to real-world conditions.
  In the present contribution, we thrive to remove many shortcomings of current
simulation frameworks and propose a versatile alternative, that allows for
objective evaluation and benchmarking of novel data-driven decoding methods for
neural signals. Its central idea is to utilize post-hoc labelings of arbitrary
M/EEG recordings. This strategy makes it paradigm-agnostic and allows to
generate comparatively large datasets with noiseless labels. Source code and
data of the novel simulation approach are made available for facilitating its
adoption.
Temporal 3D ConvNets: New Architecture and Transfer Learning for Video Classification
Authors: Ali Diba, Mohsen Fayyaz, Vivek Sharma, Amir Hossein Karami, Mohammad Mahdi Arzani, Rahman Yousefzadeh, Luc van Gool
Date: 2017-11-22 00:00:00
Tasks: Action Recognition, General Classification, Temporal Action Localization, Transfer Learning, Video Classification

The work in this paper is driven by the question how to exploit the temporal
cues available in videos for their accurate classification, and for human
action recognition in particular? Thus far, the vision community has focused on
spatio-temporal approaches with fixed temporal convolution kernel depths. We
introduce a new temporal layer that models variable temporal convolution kernel
depths. We embed this new temporal layer in our proposed 3D CNN. We extend the
DenseNet architecture - which normally is 2D - with 3D filters and pooling
kernels. We name our proposed video convolutional network `Temporal 3D
ConvNet'~(T3D) and its new temporal layer `Temporal Transition Layer'~(TTL).
Our experiments show that T3D outperforms the current state-of-the-art methods
on the HMDB51, UCF101 and Kinetics datasets.
  The other issue in training 3D ConvNets is about training them from scratch
with a huge labeled dataset to get a reasonable performance. So the knowledge
learned in 2D ConvNets is completely ignored. Another contribution in this work
is a simple and effective technique to transfer knowledge from a pre-trained 2D
CNN to a randomly initialized 3D CNN for a stable weight initialization. This
allows us to significantly reduce the number of training samples for 3D CNNs.
Thus, by finetuning this network, we beat the performance of generic and recent
methods in 3D CNNs, which were trained on large video datasets, e.g. Sports-1M,
and finetuned on the target datasets, e.g. HMDB51/UCF101. The T3D codes will be
released
Video Semantic Object Segmentation by Self-Adaptation of DCNN
Authors: Seong-Jin Park, Ki-Sang Hong
Date: 2017-11-22 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper proposes a new framework for semantic segmentation of objects in
videos. We address the label inconsistency problem of deep convolutional neural
networks (DCNNs) by exploiting the fact that videos have multiple frames; in a
few frames the object is confidently-estimated (CE) and we use the information
in them to improve labels of the other frames. Given the semantic segmentation
results of each frame obtained from DCNN, we sample several CE frames to adapt
the DCNN model to the input video by focusing on specific instances in the
video rather than general objects in various circumstances. We propose offline
and online approaches under different supervision levels. In experiments our
method achieved great improvement over the original model and previous
state-of-the-art methods.
Reinforced stochastic gradient descent for deep neural network learning
Authors: Haiping Huang, Taro Toyoizumi
Date: 2017-01-27 00:00:00

Stochastic gradient descent (SGD) is a standard optimization method to
minimize a training error with respect to network parameters in modern neural
network learning. However, it typically suffers from proliferation of saddle
points in the high-dimensional parameter space. Therefore, it is highly
desirable to design an efficient algorithm to escape from these saddle points
and reach a parameter region of better generalization capabilities. Here, we
propose a simple extension of SGD, namely reinforced SGD, which simply adds
previous first-order gradients in a stochastic manner with a probability that
increases with learning time. As verified in a simple synthetic dataset, this
method significantly accelerates learning compared with the original SGD.
Surprisingly, it dramatically reduces over-fitting effects, even compared with
state-of-the-art adaptive learning algorithm---Adam. For a benchmark
handwritten digits dataset, the learning performance is comparable to Adam, yet
with an extra advantage of requiring one-fold less computer memory. The
reinforced SGD is also compared with SGD with fixed or adaptive momentum
parameter and Nesterov's momentum, which shows that the proposed framework is
able to reach a similar generalization accuracy with less computational costs.
Overall, our method introduces stochastic memory into gradients, which plays an
important role in understanding how gradient-based training algorithms can work
and its relationship with generalization abilities of deep networks.
Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers
Authors: Yifan Chen, Yuejiao Sun, Wotao Yin
Date: 2017-11-22 00:00:00

Many optimization algorithms converge to stationary points. When the
underlying problem is nonconvex, they may get trapped at local minimizers and
occasionally stagnate near saddle points. We propose the Run-and-Inspect
Method, which adds an "inspect" phase to existing algorithms that helps escape
from non-global stationary points. The inspection samples a set of points in a
radius $R$ around the current point. When a sample point yields a sufficient
decrease in the objective, we move there and resume an existing algorithm. If
no sufficient decrease is found, the current point is called an approximate
$R$-local minimizer. We show that an $R$-local minimizer is globally optimal,
up to a specific error depending on $R$, if the objective function can be
implicitly decomposed into a smooth convex function plus a restricted function
that is possibly nonconvex, nonsmooth. For high-dimensional problems, we
introduce blockwise inspections to overcome the curse of dimensionality while
still maintaining optimality bounds up to a factor equal to the number of
blocks. Our method performs well on a set of artificial and realistic nonconvex
problems by coupling with gradient descent, coordinate descent, EM, and
prox-linear algorithms.
Hypergraph $p$-Laplacian: A Differential Geometry View
Authors: Shota Saito, Danilo P. Mandic, Hideyuki Suzuki
Date: 2017-11-22 00:00:00

The graph Laplacian plays key roles in information processing of relational
data, and has analogies with the Laplacian in differential geometry. In this
paper, we generalize the analogy between graph Laplacian and differential
geometry to the hypergraph setting, and propose a novel hypergraph
$p$-Laplacian. Unlike the existing two-node graph Laplacians, this
generalization makes it possible to analyze hypergraphs, where the edges are
allowed to connect any number of nodes. Moreover, we propose a semi-supervised
learning method based on the proposed hypergraph $p$-Laplacian, and formalize
them as the analogue to the Dirichlet problem, which often appears in physics.
We further explore theoretical connections to normalized hypergraph cut on a
hypergraph, and propose normalized cut corresponding to hypergraph
$p$-Laplacian. The proposed $p$-Laplacian is shown to outperform standard
hypergraph Laplacians in the experiment on a hypergraph semi-supervised
learning and normalized cut setting.
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
Authors: Alex Tank, Ian Cover, Nicholas J. Foti, Ali Shojaie, Emily B. Fox
Date: 2017-11-22 00:00:00
Tasks: Time Series, Time Series Analysis

While most classical approaches to Granger causality detection repose upon
linear time series assumptions, many interactions in neuroscience and economics
applications are nonlinear. We develop an approach to nonlinear Granger
causality detection using multilayer perceptrons where the input to the network
is the past time lags of all series and the output is the future value of a
single series. A sufficient condition for Granger non-causality in this setting
is that all of the outgoing weights of the input data, the past lags of a
series, to the first hidden layer are zero. For estimation, we utilize a group
lasso penalty to shrink groups of input weights to zero. We also propose a
hierarchical penalty for simultaneous Granger causality and lag estimation. We
validate our approach on simulated data from both a sparse linear
autoregressive model and the sparse and nonlinear Lorenz-96 model.
A Face Fairness Framework for 3D Meshes
Authors: Sk. Mohammadul Haque, Venu Madhav Govindu
Date: 2017-11-22 00:00:00
Tasks: Denoising, Fairness

In this paper, we present a face fairness framework for 3D meshes that
preserves the regular shape of faces and is applicable to a variety of 3D mesh
restoration tasks. Specifically, we present a number of desirable properties
for any mesh restoration method and show that our framework satisfies them. We
then apply our framework to two different tasks --- mesh-denoising and
mesh-refinement, and present comparative results for these two tasks showing
improvement over other relevant methods in the literature.
Face Attention Network: An Effective Face Detector for the Occluded Faces
Authors: Jianfeng Wang, Ye Yuan, Gang Yu
Date: 2017-11-20 00:00:00
Tasks: Data Augmentation, Face Detection, Occluded Face Detection

The performance of face detection has been largely improved with the
development of convolutional neural network. However, the occlusion issue due
to mask and sunglasses, is still a challenging problem. The improvement on the
recall of these occluded cases usually brings the risk of high false positives.
In this paper, we present a novel face detector called Face Attention Network
(FAN), which can significantly improve the recall of the face detection problem
in the occluded case without compromising the speed. More specifically, we
propose a new anchor-level attention, which will highlight the features from
the face region. Integrated with our anchor assign strategy and data
augmentation techniques, we obtain state-of-art results on public face
detection benchmarks like WiderFace and MAFA. The code will be released for
reproduction.
Multiagent Simple Temporal Problem: The Arc-Consistency Approach
Authors: Shufeng Kong, Jae Hee Lee, Sanjiang Li
Date: 2017-11-22 00:00:00
Tasks: ARC

The Simple Temporal Problem (STP) is a fundamental temporal reasoning problem
and has recently been extended to the Multiagent Simple Temporal Problem
(MaSTP). In this paper we present a novel approach that is based on enforcing
arc-consistency (AC) on the input (multiagent) simple temporal network. We show
that the AC-based approach is sufficient for solving both the STP and MaSTP and
provide efficient algorithms for them. As our AC-based approach does not impose
new constraints between agents, it does not violate the privacy of the agents
and is superior to the state-of-the-art approach to MaSTP. Empirical
evaluations on diverse benchmark datasets also show that our AC-based
algorithms for STP and MaSTP are significantly more efficient than existing
approaches.
Learning Populations of Parameters
Authors: Kevin Tian, Weihao Kong, Gregory Valiant
Date: 2017-09-08 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Sports Analytics

Consider the following estimation problem: there are $n$ entities, each with
an unknown parameter $p_i \in [0,1]$, and we observe $n$ independent random
variables, $X_1,\ldots,X_n$, with $X_i \sim $ Binomial$(t, p_i)$. How
accurately can one recover the "histogram" (i.e. cumulative density function)
of the $p_i$'s? While the empirical estimates would recover the histogram to
earth mover distance $\Theta(\frac{1}{\sqrt{t}})$ (equivalently, $\ell_1$
distance between the CDFs), we show that, provided $n$ is sufficiently large,
we can achieve error $O(\frac{1}{t})$ which is information theoretically
optimal. We also extend our results to the multi-dimensional parameter case,
capturing settings where each member of the population has multiple associated
parameters. Beyond the theoretical results, we demonstrate that the recovery
algorithm performs well in practice on a variety of datasets, providing
illuminating insights into several domains, including politics, sports
analytics, and variation in the gender ratio of offspring.
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
Authors: Yuhang Lu, Jun Zhou, Jing Wang, Jun Chen, Karen Smith, Colin Wilder, Song Wang
Date: 2017-11-07 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Motivated by the important archaeological application of exploring cultural
heritage objects, in this paper we study the challenging problem of
automatically segmenting curve structures that are very weakly stamped or
carved on an object surface in the form of a highly noisy depth map. Different
from most classical low-level image segmentation methods that are known to be
very sensitive to the noise and occlusions, we propose a new supervised
learning algorithm based on Convolutional Neural Network (CNN) to implicitly
learn and utilize more curve geometry and pattern information for addressing
this challenging problem. More specifically, we first propose a Fully
Convolutional Network (FCN) to estimate the skeleton of curve structures and at
each skeleton pixel, a scale value is estimated to reflect the local curve
width. Then we propose a dense prediction network to refine the estimated curve
skeletons. Based on the estimated scale values, we finally develop an adaptive
thresholding algorithm to achieve the final segmentation of curve structures.
In the experiment, we validate the performance of the proposed method on a
dataset of depth images scanned from unearthed pottery sherds dating to the
Woodland period of Southeastern North America.
Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling
Authors: Chaitanya Ahuja, Louis-Philippe Morency
Date: 2017-10-06 00:00:00

Recurrent neural networks have shown remarkable success in modeling
sequences. However low resource situations still adversely affect the
generalizability of these models. We introduce a new family of models, called
Lattice Recurrent Units (LRU), to address the challenge of learning deep
multi-layer recurrent models with limited resources. LRU models achieve this
goal by creating distinct (but coupled) flow of information inside the units: a
first flow along time dimension and a second flow along depth dimension. It
also offers a symmetry in how information can flow horizontally and vertically.
We analyze the effects of decoupling three different components of our LRU
model: Reset Gate, Update Gate and Projected State. We evaluate this family on
new LRU models on computational convergence rates and statistical efficiency.
Our experiments are performed on four publicly-available datasets, comparing
with Grid-LSTM and Recurrent Highway networks. Our results show that LRU has
better empirical computational convergence rates and statistical efficiency
values, along with learning more accurate language models.
Contracting Nonlinear Observers: Convex Optimization and Learning from Data
Authors: Ian R. Manchester
Date: 2017-11-22 00:00:00
Tasks: State Estimation

A new approach to design of nonlinear observers (state estimators) is
proposed. The main idea is to (i) construct a convex set of dynamical systems
which are contracting observers for a particular system, and (ii) optimize over
this set for one which minimizes a bound on state-estimation error on a
simulated noisy data set. We construct convex sets of continuous-time and
discrete-time observers, as well as contracting sampled-data observers for
continuous-time systems. Convex bounds for learning are constructed using
Lagrangian relaxation. The utility of the proposed methods are verified using
numerical simulation.
Locally Smoothed Neural Networks
Authors: Liang Pang, Yanyan Lan, Jun Xu, Jiafeng Guo, Xue-Qi Cheng
Date: 2017-11-22 00:00:00
Tasks: Face Verification, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Convolutional Neural Networks (CNN) and the locally connected layer are
limited in capturing the importance and relations of different local receptive
fields, which are often crucial for tasks such as face verification, visual
question answering, and word sequence prediction. To tackle the issue, we
propose a novel locally smoothed neural network (LSNN) in this paper. The main
idea is to represent the weight matrix of the locally connected layer as the
product of the kernel and the smoother, where the kernel is shared over
different local receptive fields, and the smoother is for determining the
importance and relations of different local receptive fields. Specifically, a
multi-variate Gaussian function is utilized to generate the smoother, for
modeling the location relations among different local receptive fields.
Furthermore, the content information can also be leveraged by setting the mean
and precision of the Gaussian function according to the content. Experiments on
some variant of MNIST clearly show our advantages over CNN and locally
connected layer.
Depth Structure Preserving Scene Image Generation
Authors: Wendong Zhang, Bingbing Ni, Yichao Yan, Jingwei Xu, Xiaokang Yang
Date: 2017-06-01 00:00:00
Tasks: Image Generation, Scene Generation

Key to automatically generate natural scene images is to properly arrange
among various spatial elements, especially in the depth direction. To this end,
we introduce a novel depth structure preserving scene image generation network
(DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the
purpose of depth structure preserving scene generation. The main trunk of the
proposed infrastructure is built on a Hawkes point process that models the
spatial dependency between different depth layers. Within each layer generative
adversarial sub-networks are trained collaboratively to generate realistic
scene components, conditioned on the layer information produced by the point
process. We experiment our model on a sub-set of SUNdataset with annotated
scene images and demonstrate that our models are capable of generating
depth-realistic natural scene image.
Semantic Document Distance Measures and Unsupervised Document Revision Detection
Authors: Xiaofeng Zhu, Diego Klabjan, Patrick Bless
Date: 2017-09-05 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Dynamic Time Warping

In this paper, we model the document revision detection problem as a minimum
cost branching problem that relies on computing document distances.
Furthermore, we propose two new document distance measures, word vector-based
Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED).
Our revision detection system is designed for a large scale corpus and
implemented in Apache Spark. We demonstrate that our system can more precisely
detect revisions than state-of-the-art methods by utilizing the Wikipedia
revision dumps https://snap.stanford.edu/data/wiki-meta.html and simulated data
sets.
Interactively Transferring CNN Patterns for Part Localization
Authors: Quanshi Zhang, Ruiming Cao, Shengming Zhang, Mark Redmonds, Ying Nian Wu, Song-Chun Zhu
Date: 2017-08-05 00:00:00

In the scenario of one/multi-shot learning, conventional end-to-end learning
strategies without sufficient supervision are usually not powerful enough to
learn correct patterns from noisy signals. Thus, given a CNN pre-trained for
object classification, this paper proposes a method that first summarizes the
knowledge hidden inside the CNN into a dictionary of latent activation
patterns, and then builds a new model for part localization by manually
assembling latent patterns related to the target part via human interactions.
We use very few (e.g., three) annotations of a semantic object part to retrieve
certain latent patterns from conv-layers to represent the target part. We then
visualize these latent patterns and ask users to further remove incorrect
patterns, in order to refine part representation. With the guidance of human
interactions, our method exhibited superior performance of part localization in
experiments.
Dilated FCN for Multi-Agent 2D/3D Medical Image Registration
Authors: Shun Miao, Sebastien Piat, Peter Fischer, Ahmet Tuysuzoglu, Philip Mewes, Tommaso Mansi, Rui Liao
Date: 2017-11-22 00:00:00
Tasks: Image Registration, Medical Image Registration

2D/3D image registration to align a 3D volume and 2D X-ray images is a
challenging problem due to its ill-posed nature and various artifacts presented
in 2D X-ray images. In this paper, we propose a multi-agent system with an auto
attention mechanism for robust and efficient 2D/3D image registration.
Specifically, an individual agent is trained with dilated Fully Convolutional
Network (FCN) to perform registration in a Markov Decision Process (MDP) by
observing a local region, and the final action is then taken based on the
proposals from multiple agents and weighted by their corresponding confidence
levels. The contributions of this paper are threefold. First, we formulate
2D/3D registration as a MDP with observations, actions, and rewards properly
defined with respect to X-ray imaging systems. Second, to handle various
artifacts in 2D X-ray images, multiple local agents are employed efficiently
via FCN-based structures, and an auto attention mechanism is proposed to favor
the proposals from regions with more reliable visual cues. Third, a dilated
FCN-based training mechanism is proposed to significantly reduce the Degree of
Freedom in the simulation of registration environment, and drastically improve
training efficiency by an order of magnitude compared to standard CNN-based
training method. We demonstrate that the proposed method achieves high
robustness on both spine cone beam Computed Tomography data with a low
signal-to-noise ratio and data from minimally invasive spine surgery where
severe image artifacts and occlusions are presented due to metal screws and
guide wires, outperforming other state-of-the-art methods (single agent-based
and optimization-based) by a large margin.