pwc-archive/papers-with-abstracts huggingface dataset:
Learning Shape Abstractions by Assembling Volumetric Primitives
Authors: Shubham Tulsiani, Hao Su, Leonidas J. Guibas, Alexei A. Efros, Jitendra Malik
Date: 2016-12-01 00:00:00
Proceeding: CVPR 2017 7

We present a learning framework for abstracting complex shapes by learning to
assemble objects using 3D volumetric primitives. In addition to generating
simple and geometrically interpretable explanations of 3D objects, our
framework also allows us to automatically discover and exploit consistent
structure in the data. We demonstrate that using our method allows predicting
shape representations which can be leveraged for obtaining a consistent parsing
across the instances of a shape collection and constructing an interpretable
shape similarity measure. We also examine applications for image-based
prediction as well as shape manipulation.
Easy over Hard: A Case Study on Deep Learning
Authors: Wei Fu, Tim Menzies
Date: 2017-03-01 00:00:00
Tasks: Deep Learning

While deep learning is an exciting new technique, the benefits of this method
need to be assessed with respect to its computational cost. This is
particularly important for deep learning since these learners need hours (to
weeks) to train the model. Such long training time limits the ability of (a)~a
researcher to test the stability of their conclusion via repeated runs with
different random seeds; and (b)~other researchers to repeat, improve, or even
refute that original work.
  For example, recently, deep learning was used to find which questions in the
Stack Overflow programmer discussion forum can be linked together. That deep
learning system took 14 hours to execute. We show here that applying a very
simple optimizer called DE to fine tune SVM, it can achieve similar (and
sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84
times faster hours than deep learning method.
  We offer these results as a cautionary tale to the software analytics
community and suggest that not every new innovation should be applied without
critical analysis. If researchers deploy some new and expensive process, that
work should be baselined against some simpler and faster alternatives.
Methods for Interpreting and Understanding Deep Neural Networks
Authors: Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller
Date: 2017-06-24 00:00:00

This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.
Irregular Convolutional Neural Networks
Authors: Jiabin Ma, Wei Wang, Liang Wang
Date: 2017-06-24 00:00:00
Tasks: Semantic Segmentation

Convolutional kernels are basic and vital components of deep Convolutional
Neural Networks (CNN). In this paper, we equip convolutional kernels with shape
attributes to generate the deep Irregular Convolutional Neural Networks (ICNN).
Compared to traditional CNN applying regular convolutional kernels like
${3\times3}$, our approach trains irregular kernel shapes to better fit the
geometric variations of input features. In other words, shapes are learnable
parameters in addition to weights. The kernel shapes and weights are learned
simultaneously during end-to-end training with the standard back-propagation
algorithm. Experiments for semantic segmentation are implemented to validate
the effectiveness of our proposed ICNN.
Cross-modal Common Representation Learning by Hybrid Transfer Network
Authors: Xin Huang, Yuxin Peng, Mingkuan Yuan
Date: 2017-06-01 00:00:00
Tasks: Cross-Modal Retrieval, Representation Learning, Retrieval

DNN-based cross-modal retrieval is a research hotspot to retrieve across
different modalities as image and text, but existing methods often face the
challenge of insufficient cross-modal training data. In single-modal scenario,
similar problem is usually relieved by transferring knowledge from large-scale
auxiliary datasets (as ImageNet). Knowledge from such single-modal datasets is
also very useful for cross-modal retrieval, which can provide rich general
semantic information that can be shared across different modalities. However,
it is challenging to transfer useful knowledge from single-modal (as image)
source domain to cross-modal (as image/text) target domain. Knowledge in source
domain cannot be directly transferred to both two different modalities in
target domain, and the inherent cross-modal correlation contained in target
domain provides key hints for cross-modal retrieval which should be preserved
during transfer process. This paper proposes Cross-modal Hybrid Transfer
Network (CHTN) with two subnetworks: Modal-sharing transfer subnetwork utilizes
the modality in both source and target domains as a bridge, for transferring
knowledge to both two modalities simultaneously; Layer-sharing correlation
subnetwork preserves the inherent cross-modal semantic correlation to further
adapt to cross-modal retrieval task. Cross-modal data can be converted to
common representation by CHTN for retrieval, and comprehensive experiment on 3
datasets shows its effectiveness.
A Variational EM Method for Pole-Zero Modeling of Speech with Mixed Block Sparse and Gaussian Excitation
Authors: Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen, Mads Græsbøll Christensen
Date: 2017-06-24 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis

The modeling of speech can be used for speech synthesis and speech
recognition. We present a speech analysis method based on pole-zero modeling of
speech with mixed block sparse and Gaussian excitation. By using a pole-zero
model, instead of the all-pole model, a better spectral fitting can be
expected. Moreover, motivated by the block sparse glottal flow excitation
during voiced speech and the white noise excitation for unvoiced speech, we
model the excitation sequence as a combination of block sparse signals and
white noise. A variational EM (VEM) method is proposed for estimating the
posterior PDFs of the block sparse residuals and point estimates of mod- elling
parameters within a sparse Bayesian learning framework. Compared to
conventional pole-zero and all-pole based methods, experimental results show
that the proposed method has lower spectral distortion and good performance in
reconstructing of the block sparse excitation.
A Semi-supervised Framework for Image Captioning
Authors: Wenhu Chen, Aurelien Lucchi, Thomas Hofmann
Date: 2016-11-16 00:00:00
Tasks: Decoder, Image Captioning, Word Embeddings

State-of-the-art approaches for image captioning require supervised training
data consisting of captions with paired image data. These methods are typically
unable to use unsupervised data such as textual data with no corresponding
images, which is a much more abundant commodity. We here propose a novel way of
using such textual data by artificially generating missing visual information.
We evaluate this learning approach on a newly designed model that detects
visual concepts present in an image and feed them to a reviewer-decoder
architecture with an attention mechanism. Unlike previous approaches that
encode visual concepts using word embeddings, we instead suggest using regional
image features which capture more intrinsic information. The main benefit of
this architecture is that it synthesizes meaningful thought vectors that
capture salient image properties and then applies a soft attentive decoder to
decode the thought vectors and generate image captions. We evaluate our model
on both Microsoft COCO and Flickr30K datasets and demonstrate that this model
combined with our semi-supervised learning method can largely improve
performance and help the model to generate more accurate and diverse captions.
Semi-supervised Text Categorization Using Recursive K-means Clustering
Authors: Harsha S. Gowda, Mahamad Suhil, D. S. Guru, Lavanya Narayana Raju
Date: 2017-06-24 00:00:00
Tasks: Clustering, General Classification, Text Categorization

In this paper, we present a semi-supervised learning algorithm for
classification of text documents. A method of labeling unlabeled text documents
is presented. The presented method is based on the principle of divide and
conquer strategy. It uses recursive K-means algorithm for partitioning both
labeled and unlabeled data collection. The K-means algorithm is applied
recursively on each partition till a desired level partition is achieved such
that each partition contains labeled documents of a single class. Once the
desired clusters are obtained, the respective cluster centroids are considered
as representatives of the clusters and the nearest neighbor rule is used for
classifying an unknown text document. Series of experiments have been conducted
to bring out the superiority of the proposed model over other recent state of
the art models on 20Newsgroups dataset.
Cluster Based Symbolic Representation for Skewed Text Categorization
Authors: Lavanya Narayana Raju, Mahamad Suhil, D. S. Guru, Harsha S. Gowda
Date: 2017-06-24 00:00:00
Tasks: Clustering, Text Categorization

In this work, a problem associated with imbalanced text corpora is addressed.
A method of converting an imbalanced text corpus into a balanced one is
presented. The presented method employs a clustering algorithm for conversion.
Initially to avoid curse of dimensionality, an effective representation scheme
based on term class relevancy measure is adapted, which drastically reduces the
dimension to the number of classes in the corpus. Subsequently, the samples of
larger sized classes are grouped into a number of subclasses of smaller sizes
to make the entire corpus balanced. Each subclass is then given a single
symbolic vector representation by the use of interval valued features. This
symbolic representation in addition to being compact helps in reducing the
space requirement and also the classification time. The proposed model has been
empirically demonstrated for its superiority on bench marking datasets viz.,
Reuters 21578 and TDT2. Further, it has been compared against several other
existing contemporary models including model based on support vector machine.
The comparative analysis indicates that the proposed model outperforms the
other existing models.
Encoder-Decoder Shift-Reduce Syntactic Parsing
Authors: Jiangming Liu, Yue Zhang
Date: 2017-06-24 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Dependency Parsing, NMT

Starting from NMT, encoder-decoder neu- ral networks have been used for many
NLP problems. Graph-based models and transition-based models borrowing the en-
coder components achieve state-of-the-art performance on dependency parsing and
constituent parsing, respectively. How- ever, there has not been work
empirically studying the encoder-decoder neural net- works for transition-based
parsing. We apply a simple encoder-decoder to this end, achieving comparable
results to the parser of Dyer et al. (2015) on standard de- pendency parsing,
and outperforming the parser of Vinyals et al. (2015) on con- stituent parsing.
Deep Mixture of Diverse Experts for Large-Scale Visual Recognition
Authors: Tianyi Zhao, Jun Yu, Zhenzhong Kuang, Wei zhang, Jianping Fan
Date: 2017-06-24 00:00:00
Tasks: Multi-Task Learning, Object, Object Recognition

In this paper, a deep mixture of diverse experts algorithm is developed for
seamlessly combining a set of base deep CNNs (convolutional neural networks)
with diverse outputs (task spaces), e.g., such base deep CNNs are trained to
recognize different subsets of tens of thousands of atomic object classes.
First, a two-layer (category layer and object class layer) ontology is
constructed to achieve more effective solution for task group generation, e.g.,
assigning the semantically-related atomic object classes at the sibling leaf
nodes into the same task group because they may share similar learning
complexities. Second, one particular base deep CNNs with $M+1$ ($M \leq 1,000$)
outputs is learned for each task group to recognize its $M$ atomic object
classes effectively and identify one special class of "not-in-group"
automatically, and the network structure (numbers of layers and units in each
layer) of the well-designed AlexNet is directly used to configure such base
deep CNNs. A deep multi-task learning algorithm is developed to leverage the
inter-class visual similarities to learn more discriminative base deep CNNs and
multi-task softmax for enhancing the separability of the atomic object classes
in the same task group. Finally, all these base deep CNNs with diverse outputs
(task spaces) are seamlessly combined to form a deep mixture of diverse experts
for recognizing tens of thousands of atomic object classes. Our experimental
results have demonstrated that our deep mixture of diverse experts algorithm
can achieve very competitive results on large-scale visual recognition.
Adversarial Neural Machine Translation
Authors: Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jian-Huang Lai, Tie-Yan Liu
Date: 2017-04-20 00:00:00
Tasks: Machine Translation, NMT, Translation

In this paper, we study a new learning paradigm for Neural Machine
Translation (NMT). Instead of maximizing the likelihood of the human
translation as in previous works, we minimize the distinction between human
translation and the translation given by an NMT model. To achieve this goal,
inspired by the recent success of generative adversarial networks (GANs), we
employ an adversarial training architecture and name it as Adversarial-NMT. In
Adversarial-NMT, the training of the NMT model is assisted by an adversary,
which is an elaborately designed Convolutional Neural Network (CNN). The goal
of the adversary is to differentiate the translation result generated by the
NMT model from that by human. The goal of the NMT model is to produce high
quality translations so as to cheat the adversary. A policy gradient method is
leveraged to co-train the NMT model and the adversary. Experimental results on
English$\rightarrow$French and German$\rightarrow$English translation tasks
show that Adversarial-NMT can achieve significantly better translation quality
than several strong baselines.
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Authors: Amirhossein Tavanaei, Anthony S. Maida
Date: 2016-11-09 00:00:00

Hierarchical feature discovery using non-spiking convolutional neural
networks (CNNs) has attracted much recent interest in machine learning and
computer vision. However, it is still not well understood how to create a
biologically plausible network of brain-like, spiking neurons with multi-layer,
unsupervised learning. This paper explores a novel bio-inspired spiking CNN
that is trained in a greedy, layer-wise fashion. The proposed network consists
of a spiking convolutional-pooling layer followed by a feature discovery layer
extracting independent visual features. Kernels for the convolutional layer are
trained using local learning. The learning is implemented using a sparse,
spiking auto-encoder representing primary visual features. The feature
discovery layer extracts independent features by probabilistic, leaky
integrate-and-fire (LIF) neurons that are sparsely active in response to
stimuli. The layer of the probabilistic, LIF neurons implicitly provides
lateral inhibitions to extract sparse and independent features. Experimental
results show that the convolutional layer is stack-admissible, enabling it to
support a multi-layer learning. The visual features obtained from the proposed
probabilistic LIF neurons in the feature discovery layer are utilized for
training a classifier. Classification results contribute to the independent and
informative visual features extracted in a hierarchy of convolutional and
feature discovery layers. The proposed model is evaluated on the MNIST digit
dataset using clean and noisy images. The recognition performance for clean
images is above 98%. The performance loss for recognizing the noisy images is
in the range 0.1% to 8.5% depending on noise types and densities. This level of
performance loss indicates that the network is robust to additive noise.
Reservoir Computing on the Hypersphere
Authors: M. Andrecut
Date: 2017-06-24 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs)
framework, frequently used for sequence learning and time series prediction.
The RC system consists of a random fixed-weight RNN (the input-hidden reservoir
layer) and a classifier (the hidden-output readout layer). Here we focus on the
sequence learning problem, and we explore a different approach to RC. More
specifically, we remove the non-linear neural activation function, and we
consider an orthogonal reservoir acting on normalized states on the unit
hypersphere. Surprisingly, our numerical results show that the system's memory
capacity exceeds the dimensionality of the reservoir, which is the upper bound
for the typical RC approach based on Echo State Networks (ESNs). We also show
how the proposed system can be applied to symmetric cryptography problems, and
we include a numerical implementation.
Fundamental Matrix Estimation: A Study of Error Criteria
Authors: Mohammed E. Fathy, Ashraf S. Hussein, Mohammed F. Tolba
Date: 2017-06-24 00:00:00

The fundamental matrix (FM) describes the geometric relations that exist
between two images of the same scene. Different error criteria are used for
estimating FMs from an input set of correspondences. In this paper, the
accuracy and efficiency aspects of the different error criteria were studied.
We mathematically and experimentally proved that the most popular error
criterion, the symmetric epipolar distance, is biased. It was also shown that
despite the similarity between the algebraic expressions of the symmetric
epipolar distance and Sampson distance, they have different accuracy
properties. In addition, a new error criterion, Kanatani distance, was proposed
and was proved to be the most effective for use during the outlier removal
phase from accuracy and efficiency perspectives. To thoroughly test the
accuracy of the different error criteria, we proposed a randomized algorithm
for Reprojection Error-based Correspondence Generation (RE-CG). As input, RE-CG
takes an FM and a desired reprojection error value $d$. As output, RE-CG
generates a random correspondence having that error value. Mathematical
analysis of this algorithm revealed that the success probability for any given
trial is 1 - (2/3)^2 at best and is 1 - (6/7)^2 at worst while experiments
demonstrated that the algorithm often succeeds after only one trial.
Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions
Authors: Daniel Lopez Martinez, Ognjen Rudovic, Rosalind Picard
Date: 2017-06-22 00:00:00

Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In this paper, we propose a novel two-stage learning approach for
VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs)
to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels
from face images. The estimated scores are then fed into the personalized
Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by
each person. Personalization of the model is performed using a newly introduced
facial expressiveness score, unique for each person. To the best of our
knowledge, this is the first approach to automatically estimate VAS from face
images. We show the benefits of the proposed personalized over traditional
non-personalized approach on a benchmark dataset for pain analysis from face
images.
On Sampling Strategies for Neural Network-based Collaborative Filtering
Authors: Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong
Date: 2017-06-23 00:00:00
Tasks: Collaborative Filtering

Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
$\times 30$ times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.
Collaborative Deep Learning in Fixed Topology Networks
Authors: Zhanhong Jiang, Aditya Balu, Chinmay Hegde, Soumik Sarkar
Date: 2017-06-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deep Learning

There is significant recent interest to parallelize deep learning algorithms
in order to handle the enormous growth in data and model sizes. While most
advances focus on model parallelization and engaging multiple computing agents
via using a central parameter server, aspect of data parallelization along with
decentralized computation has not been explored sufficiently. In this context,
this paper presents a new consensus-based distributed SGD (CDSGD) (and its
momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed
topology networks that enables data parallelization as well as decentralized
computation. Such a framework can be extremely useful for learning agents with
access to only local/private data in a communication constrained environment.
We analyze the convergence properties of the proposed algorithm with strongly
convex and nonconvex objective functions with fixed and diminishing step sizes
using concepts of Lyapunov function construction. We demonstrate the efficacy
of our algorithms in comparison with the baseline centralized SGD and the
recently proposed federated averaging algorithm (that also enables data
parallelism) based on benchmark datasets such as MNIST, CIFAR-10 and CIFAR-100.