pwc-archive/papers-with-abstracts huggingface dataset:
Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model
Authors: Minkyu Choi, Takazumi Matsumoto, Minju Jung, Jun Tani
Date: 2018-03-07 00:00:00

The current paper presents how a predictive coding type deep recurrent neural
networks can generate vision-based goal-directed plans based on prior learning
experience by examining experiment results using a real arm robot. The proposed
deep recurrent neural network learns to predict visuo-proprioceptive sequences
by extracting an adequate predictive model from various visuomotor experiences
related to object-directed behaviors. The predictive model was developed in
terms of mapping from intention state space to expected visuo-proprioceptive
sequences space through iterative learning. Our arm robot experiments adopted
with three different tasks with different levels of difficulty showed that the
error minimization principle in the predictive coding framework applied to
inference of the optimal intention states for given goal states can generate
goal-directed plans even for unlearned goal states with generalization. It was,
however, shown that sufficient generalization requires relatively large number
of learning trajectories. The paper discusses possible countermeasure to
overcome this problem.
Attention Based Fully Convolutional Network for Speech Emotion Recognition
Authors: Yuanyuan Zhang, Jun Du, Zi-Rui Wang, Jianshu Zhang
Date: 2018-06-05 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, Transfer Learning

Speech emotion recognition is a challenging task for three main reasons: 1)
human emotion is abstract, which means it is hard to distinguish; 2) in
general, human emotion can only be detected in some specific moments during a
long utterance; 3) speech data with emotional labeling is usually limited. In
this paper, we present a novel attention based fully convolutional network for
speech emotion recognition. We employ fully convolutional network as it is able
to handle variable-length speech, free of the demand of segmentation to keep
critical information not lost. The proposed attention mechanism can make our
model be aware of which time-frequency region of speech spectrogram is more
emotion-relevant. Considering limited data, the transfer learning is also
adapted to improve the accuracy. Especially, it's interesting to observe
obvious improvement obtained with natural scene image based pre-trained model.
Validated on the publicly available IEMOCAP corpus, the proposed model
outperformed the state-of-the-art methods with a weighted accuracy of 70.4% and
an unweighted accuracy of 63.9% respectively.
Boredom-driven curious learning by Homeo-Heterostatic Value Gradients
Authors: Yen Yu, Acer Y. C. Chang, Ryota Kanai
Date: 2018-06-05 00:00:00

This paper presents the Homeo-Heterostatic Value Gradients (HHVG) algorithm
as a formal account on the constructive interplay between boredom and curiosity
which gives rise to effective exploration and superior forward model learning.
We envisaged actions as instrumental in agent's own epistemic disclosure. This
motivated two central algorithmic ingredients: devaluation and devaluation
progress, both underpin agent's cognition concerning intrinsically generated
rewards. The two serve as an instantiation of homeostatic and heterostatic
intrinsic motivation. A key insight from our algorithm is that the two
seemingly opposite motivations can be reconciled---without which exploration
and information-gathering cannot be effectively carried out. We supported this
claim with empirical evidence, showing that boredom-enabled agents consistently
outperformed other curious or explorative agent variants in model building
benchmarks based on self-assisted experience accumulation.
Information Aggregation via Dynamic Routing for Sequence Encoding
Authors: Jingjing Gong, Xipeng Qiu, Shaojing Wang, Xuanjing Huang
Date: 2018-06-05 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentiment Analysis, text-classification, Text Classification

While much progress has been made in how to encode a text sequence into a
sequence of vectors, less attention has been paid to how to aggregate these
preceding vectors (outputs of RNN/CNN) into fixed-size encoding vector.
Usually, a simple max or average pooling is used, which is a bottom-up and
passive way of aggregation and lack of guidance by task information. In this
paper, we propose an aggregation mechanism to obtain a fixed-size encoding with
a dynamic routing policy. The dynamic routing policy is dynamically deciding
that what and how much information need be transferred from each word to the
final encoding of the text sequence. Following the work of Capsule Network, we
design two dynamic routing policies to aggregate the outputs of RNN/CNN
encoding layer into a final encoding vector. Compared to the other aggregation
methods, dynamic routing can refine the messages according to the state of
final encoding vector. Experimental results on five text classification tasks
show that our method outperforms other aggregating models by a significant
margin. Related source code is released on our github page.
Fusion Graph Convolutional Networks
Authors: Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran
Date: 2018-05-31 00:00:00
Tasks: General Classification, Node Classification

Semi-supervised node classification in attributed graphs, i.e., graphs with
node features, involves learning to classify unlabeled nodes given a partially
labeled graph. Label predictions are made by jointly modeling the node and its'
neighborhood features. State-of-the-art models for node classification on such
attributed graphs use differentiable recursive functions that enable
aggregation and filtering of neighborhood information from multiple hops. In
this work, we analyze the representation capacity of these models to regulate
information from multiple hops independently. From our analysis, we conclude
that these models despite being powerful, have limited representation capacity
to capture multi-hop neighborhood information effectively. Further, we also
propose a mathematically motivated, yet simple extension to existing graph
convolutional networks (GCNs) which has improved representation capacity. We
extensively evaluate the proposed model, F-GCN on eight popular datasets from
different domains. F-GCN outperforms the state-of-the-art models for
semi-supervised learning on six datasets while being extremely competitive on
the other two.
Deep Image Compression via End-to-End Learning
Authors: Haojie Liu, Tong Chen, Qiu Shen, Tao Yue, Zhan Ma
Date: 2018-06-05 00:00:00
Tasks: Image Compression, MS-SSIM, Quantization, SSIM, Transfer Learning

We present a lossy image compression method based on deep convolutional
neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and
JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same
bit rate. Currently, most of the CNNs based approaches train the network using
a L2 loss between the reconstructions and the ground-truths in the pixel
domain, which leads to over-smoothing results and visual quality degradation
especially at a very low bit rate. Therefore, we improve the subjective quality
with the combination of a perception loss and an adversarial loss additionally.
To achieve better rate-distortion optimization (RDO), we also introduce an
easy-to-hard transfer learning when adding quantization error and rate
constraint. Finally, we evaluate our method on public Kodak and the Test
Dataset P/M released by the Computer Vision Lab of ETH Zurich, resulting in
averaged 7.81% and 19.1% BD-rate reduction over BPG, respectively.
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Authors: Yuankai Huo, Zhoubing Xu, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
Date: 2018-06-01 00:00:00
Tasks: Brain Segmentation, GPU, Segmentation

Whole brain segmentation on a structural magnetic resonance imaging (MRI) is
essential in non-invasive investigation for neuroanatomy. Historically,
multi-atlas segmentation (MAS) has been regarded as the de facto standard
method for whole brain segmentation. Recently, deep neural network approaches
have been applied to whole brain segmentation by learning random patches or 2D
slices. Yet, few previous efforts have been made on detailed whole brain
segmentation using 3D networks due to the following challenges: (1) fitting
entire whole brain volume into 3D networks is restricted by the current GPU
memory, and (2) the large number of targeting labels (e.g., > 100 labels) with
limited number of training 3D volumes (e.g., < 50 scans). In this paper, we
propose the spatially localized atlas network tiles (SLANT) method to
distribute multiple independent 3D fully convolutional networks to cover
overlapped sub-spaces in a standard atlas space. This strategy simplifies the
whole brain learning task to localized sub-tasks, which was enabled by combing
canonical registration and label fusion techniques with deep learning. To
address the second challenge, auxiliary labels on 5111 initially unlabeled
scans were created by MAS for pre-training. From empirical validation, the
state-of-the-art MAS method achieved mean Dice value of 0.76, 0.71, and 0.68,
while the proposed method achieved 0.78, 0.73, and 0.71 on three validation
cohorts. Moreover, the computational time reduced from > 30 hours using MAS to
~15 minutes using the proposed method. The source code is available online
https://github.com/MASILab/SLANTbrainSeg
SEGEN: Sample-Ensemble Genetic Evolutional Network Model
Authors: Jiawei Zhang, Limeng Cui, Fisher B. Gouza
Date: 2018-03-23 00:00:00
Tasks: Deep Learning, Ensemble Learning, model, Representation Learning

Deep learning, a rebranding of deep neural network research works, has
achieved a remarkable success in recent years. With multiple hidden layers,
deep learning models aim at computing the hierarchical feature representations
of the observational data. Meanwhile, due to its severe disadvantages in data
consumption, computational resources, parameter tuning costs and the lack of
result explainability, deep learning has also suffered from lots of criticism.
In this paper, we will introduce a new representation learning model, namely
"Sample-Ensemble Genetic Evolutionary Network" (SEGEN), which can serve as an
alternative approach to deep learning models. Instead of building one single
deep model, based on a set of sampled sub-instances, SEGEN adopts a
genetic-evolutionary learning strategy to build a group of unit models
generations by generations. The unit models incorporated in SEGEN can be either
traditional machine learning models or the recent deep learning models with a
much "narrower" and "shallower" architecture. The learning results of each
instance at the final generation will be effectively combined from each unit
model via diffusive propagation and ensemble learning strategies. From the
computational perspective, SEGEN requires far less data, fewer computational
resources and parameter tuning efforts, but has sound theoretic
interpretability of the learning process and results. Extensive experiments
have been done on several different real-world benchmark datasets, and the
experimental results obtained by SEGEN have demonstrated its advantages over
the state-of-the-art representation learning models.
Super-resolution estimation of cyclic arrival rates
Authors: Ningyuan Chen, Donald K. K. Lee, Sahand Negahban
Date: 2016-10-30 00:00:00
Tasks: Super-Resolution

Exploiting the fact that most arrival processes exhibit cyclic behaviour, we
propose a simple procedure for estimating the intensity of a nonhomogeneous
Poisson process. The estimator is the super-resolution analogue to Shao 2010
and Shao & Lii 2011, which is a sum of $p$ sinusoids where $p$ and the
frequency, amplitude, and phase of each wave are not known and need to be
estimated. This results in an interpretable yet flexible specification that is
suitable for use in modelling as well as in high resolution simulations.
  Our estimation procedure sits in between classic periodogram methods and
atomic/total variation norm thresholding. Through a novel use of window
functions in the point process domain, our approach attains super-resolution
without semidefinite programming. Under suitable conditions, finite sample
guarantees can be derived for our procedure. These resolve some open questions
and expand existing results in spectral estimation literature.
Onsets and Frames: Dual-Objective Piano Transcription
Authors: Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, Douglas Eck
Date: 2017-10-30 00:00:00
Tasks: Music Transcription

We advance the state of the art in polyphonic piano music transcription by
using a deep convolutional and recurrent neural network which is trained to
jointly predict onsets and frames. Our model predicts pitch onset events and
then uses those predictions to condition framewise pitch predictions. During
inference, we restrict the predictions from the framewise detector by not
allowing a new note to start unless the onset detector also agrees that an
onset for that pitch is present in the frame. We focus on improving onsets and
offsets together instead of either in isolation as we believe this correlates
better with human musical perception. Our approach results in over a 100%
relative improvement in note F1 score (with offsets) on the MAPS dataset.
Furthermore, we extend the model to predict relative velocities of normalized
audio which results in more natural-sounding transcriptions.
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
Authors: Shupeng Gui, Xiangliang Zhang, Shuang Qiu, Mingrui Wu, Jieping Ye, Ji Liu
Date: 2018-05-28 00:00:00
Tasks: Graph Embedding, Graph Representation Learning, Representation Learning

Graph embedding is a central problem in social network analysis and many
other applications, aiming to learn the vector representation for each node.
While most existing approaches need to specify the neighborhood and the
dependence form to the neighborhood, which may significantly degrades the
flexibility of representation, we propose a novel graph node embedding method
(namely GESF) via the set function technique. Our method can 1) learn an
arbitrary form of representation function from neighborhood, 2) automatically
decide the significance of neighbors at different distances, and 3) be applied
to heterogeneous graph embedding, which may contain multiple types of nodes.
Theoretical guarantee for the representation capability of our method has been
proved for general homogeneous and heterogeneous graphs and evaluation results
on benchmark data sets show that the proposed GESF outperforms the
state-of-the-art approaches on producing node vectors for classification tasks.
A Primer on Causal Analysis
Authors: Finnian Lattimore, Cheng Soon Ong
Date: 2018-06-05 00:00:00
Tasks: Navigate

We provide a conceptual map to navigate causal analysis problems. Focusing on
the case of discrete random variables, we consider the case of causal effect
estimation from observational data. The presented approaches apply also to
continuous variables, but the issue of estimation becomes more complex. We then
introduce the four schools of thought for causal analysis
Forecasting Crime with Deep Learning
Authors: Alexander Stec, Diego Klabjan
Date: 2018-06-05 00:00:00
Tasks: Crime Prediction, Deep Learning

The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The crime counts are broken into 10 bins and our model predicts
the most likely bin for a each spatial region at a daily level. We train this
data using increasingly complex neural network structures, including variations
that are suited to the spatial and temporal aspects of the crime prediction
problem. With our best model we are able to predict the correct bin for overall
crime count with 75.6% and 65.3% accuracy for Chicago and Portland,
respectively. The results show the efficacy of neural networks for the
prediction problem and the value of using external datasets in addition to
standard crime data.
Long-time predictive modeling of nonlinear dynamical systems using neural networks
Authors: Shaowu Pan, Karthik Duraisamy
Date: 2018-05-31 00:00:00
Tasks: Data Augmentation

We study the use of feedforward neural networks (FNN) to develop models of
nonlinear dynamical systems from data. Emphasis is placed on predictions at
long times, with limited data availability. Inspired by global stability
analysis, and the observation of the strong correlation between the local error
and the maximum singular value of the Jacobian of the ANN, we introduce
Jacobian regularization in the loss function. This regularization suppresses
the sensitivity of the prediction to the local error and is shown to improve
accuracy and robustness. Comparison between the proposed approach and sparse
polynomial regression is presented in numerical examples ranging from simple
ODE systems to nonlinear PDE systems including vortex shedding behind a
cylinder, and instability-driven buoyant mixing flow. Furthermore, limitations
of feedforward neural networks are highlighted, especially when the training
data does not include a low dimensional attractor. Strategies of data
augmentation are presented as remedies to address these issues to a certain
extent.
r-Instance Learning for Missing People Tweets Identification
Authors: Yang Yang, Haoyan Liu, Xia Hu, Jiawei Zhang, Xiao-Ming Zhang, Zhoujun Li, Philip S. Yu
Date: 2018-05-28 00:00:00

The number of missing people (i.e., people who get lost) greatly increases in
recent years. It is a serious worldwide problem, and finding the missing people
consumes a large amount of social resources. In tracking and finding these
missing people, timely data gathering and analysis actually play an important
role. With the development of social media, information about missing people
can get propagated through the web very quickly, which provides a promising way
to solve the problem. The information in online social media is usually of
heterogeneous categories, involving both complex social interactions and
textual data of diverse structures. Effective fusion of these different types
of information for addressing the missing people identification problem can be
a great challenge. Motivated by the multi-instance learning problem and
existing social science theory of "homophily", in this paper, we propose a
novel r-instance (RI) learning model.
3D Human Pose Estimation with 2D Marginal Heatmaps
Authors: Aiden Nibali, Zhen He, Stuart Morgan, Luke Prendergast
Date: 2018-06-05 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Automatically determining three-dimensional human pose from monocular RGB
image data is a challenging problem. The two-dimensional nature of the input
results in intrinsic ambiguities which make inferring depth particularly
difficult. Recently, researchers have demonstrated that the flexible
statistical modelling capabilities of deep neural networks are sufficient to
make such inferences with reasonable accuracy. However, many of these models
use coordinate output techniques which are memory-intensive, not
differentiable, and/or do not spatially generalise well. We propose
improvements to 3D coordinate prediction which avoid the aforementioned
undesirable traits by predicting 2D marginal heatmaps under an augmented
soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent
heatmaps whilst maintaining differentiability. We are also able to achieve
state-of-the-art accuracy on publicly available 3D human pose estimation data.
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Authors: Hongru Liang, Haozheng Wang, Jun Wang, ShaoDi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang
Date: 2018-06-05 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Recommendation Systems, Retrieval

Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Authors: Amir Sadeghian, Vineet Kosaraju, Ali Sadeghian, Noriaki Hirose, S. Hamid Rezatofighi, Silvio Savarese
Date: 2018-06-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network, Self-Driving Cars, Trajectory Forecasting, Trajectory Prediction

This paper addresses the problem of path prediction for multiple interacting
agents in a scene, which is a crucial step for many autonomous platforms such
as self-driving cars and social robots. We present \textit{SoPhie}; an
interpretable framework based on Generative Adversarial Network (GAN), which
leverages two sources of information, the path history of all the agents in a
scene, and the scene context information, using images of the scene. To predict
a future path for an agent, both physical and social information must be
leveraged. Previous work has not been successful to jointly model physical and
social interactions. Our approach blends a social attention mechanism with a
physical attention that helps the model to learn where to look in a large scene
and extract the most salient parts of the image relevant to the path. Whereas,
the social attention component aggregates information across the different
agent interactions and extracts the most important trajectory information from
the surrounding neighbors. SoPhie also takes advantage of GAN to generates more
realistic samples and to capture the uncertain nature of the future paths by
modeling its distribution. All these mechanisms enable our approach to predict
socially and physically plausible paths for the agents and to achieve
state-of-the-art performance on several different trajectory forecasting
benchmarks.