pwc-archive/papers-with-abstracts huggingface dataset:
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Authors: Aryan Mokhtari, Alejandro Ribeiro
Date: 2017-09-02 00:00:00
Proceeding: NeurIPS 2017 12

This paper studies empirical risk minimization (ERM) problems for large-scale
datasets and incorporates the idea of adaptive sample size methods to improve
the guaranteed convergence bounds for first-order stochastic and deterministic
methods. In contrast to traditional methods that attempt to solve the ERM
problem corresponding to the full dataset directly, adaptive sample size
schemes start with a small number of samples and solve the corresponding ERM
problem to its statistical accuracy. The sample size is then grown
geometrically -- e.g., scaling by a factor of two -- and use the solution of
the previous ERM as a warm start for the new ERM. Theoretical analyses show
that the use of adaptive sample size methods reduces the overall computational
cost of achieving the statistical accuracy of the whole dataset for a broad
range of deterministic and stochastic first-order methods. The gains are
specific to the choice of method. When particularized to, e.g., accelerated
gradient descent and stochastic variance reduce gradient, the computational
cost advantage is a logarithm of the number of training samples. Numerical
experiments on various datasets confirm theoretical claims and showcase the
gains of using the proposed adaptive sample size scheme.
Video Captioning with Guidance of Multimodal Latent Topics
Authors: Shizhe Chen, Jia Chen, Qin Jin, Alexander Hauptmann
Date: 2017-08-31 00:00:00
Tasks: Caption Generation, Decoder, Multi-Task Learning, Prediction, Video Captioning

The topic diversity of open-domain videos leads to various vocabularies and linguistic expressions in describing video contents, and therefore, makes the video captioning task even more challenging. In this paper, we propose an unified caption framework, M&M TGM, which mines multimodal topics in unsupervised fashion from data and guides the caption decoder with these topics. Compared to pre-defined topics, the mined multimodal topics are more semantically and visually coherent and can reflect the topic distribution of videos better. We formulate the topic-aware caption generation as a multi-task learning problem, in which we add a parallel task, topic prediction, in addition to the caption task. For the topic prediction task, we use the mined topics as the teacher to train a student topic prediction model, which learns to predict the latent topics from multimodal contents of videos. The topic prediction provides intermediate supervision to the learning process. As for the caption task, we propose a novel topic-aware decoder to generate more accurate and detailed video descriptions with the guidance from latent topics. The entire learning procedure is end-to-end and it optimizes both tasks simultaneously. The results from extensive experiments conducted on the MSR-VTT and Youtube2Text datasets demonstrate the effectiveness of our proposed model. M&M TGM not only outperforms prior state-of-the-art methods on multiple evaluation metrics and on both benchmark datasets, but also achieves better generalization ability.
A Survey of Efficient Regression of General-Activity Human Poses from Depth Images
Authors: Wenye He
Date: 2017-09-02 00:00:00
Tasks: Pose Estimation, Position, regression

This paper presents a comprehensive review on regression-based method for
human pose estimation. The problem of human pose estimation has been
intensively studied and enabled many application from entertainment to
training. Traditional methods often rely on color image only which cannot
completely ambiguity of joint 3D position, especially in the complex context.
With the popularity of depth sensors, the precision of 3D estimation has
significant improvement. In this paper, we give a detailed analysis of
state-of-the-art on human pose estimation, including depth image based and
RGB-D based approaches. The experimental results demonstrate their advantages
and limitation for different scenarios.
Deep Learning-Guided Image Reconstruction from Incomplete Data
Authors: Brendan Kelly, Thomas P. Matthews, Mark A. Anastasio
Date: 2017-09-02 00:00:00
Tasks: Deep Learning, Image Reconstruction

An approach to incorporate deep learning within an iterative image
reconstruction framework to reconstruct images from severely incomplete
measurement data is presented. Specifically, we utilize a convolutional neural
network (CNN) as a quasi-projection operator within a least squares
minimization procedure. The CNN is trained to encode high level information
about the class of images being imaged; this information is utilized to
mitigate artifacts in intermediate images produced by use of an iterative
method. The structure of the method was inspired by the proximal gradient
descent method, where the proximal operator is replaced by a deep CNN and the
gradient descent step is generalized by use of a linear reconstruction
operator. It is demonstrated that this approach improves image quality for
several cases of limited-view image reconstruction and that using a CNN in an
iterative method increases performance compared to conventional image
reconstruction approaches. We test our method on several limited-view image
reconstruction problems. Qualitative and quantitative results demonstrate
state-of-the-art performance.
Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible to Various Temporal Codes
Authors: Chaofei Hong
Date: 2017-09-02 00:00:00

Conventional modeling approaches have found limitations in matching the
increasingly detailed neural network structures and dynamics recorded in
experiments to the diverse brain functionalities. On another approach, studies
have demonstrated to train spiking neural networks for simple functions using
supervised learning. Here, we introduce a modified SpikeProp learning
algorithm, which achieved better learning stability in different activity
states. In addition, we show biological realistic features such as lateral
connections and sparse activities can be included in the network. We
demonstrate the versatility of this framework by implementing three well-known
temporal codes for different types of cognitive tasks, which are MNIST digits
recognition, spatial coordinate transformation, and motor sequence generation.
Moreover, we find several characteristic features have evolved alongside the
task training, such as selective activity, excitatory-inhibitory balance, and
weak pair-wise correlation. The coincidence between the self-evolved and
experimentally observed features indicates their importance on the brain
functionality. Our results suggest a unified setting in which diverse cognitive
computations and mechanisms can be studied.
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection
Authors: Marek Rei, Luana Bulat, Douwe Kiela, Ekaterina Shutova
Date: 2017-09-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Natural Language Understanding

The ubiquity of metaphor in our everyday communication makes it an important
problem for natural language understanding. Yet, the majority of metaphor
processing systems to date rely on hand-engineered features and there is still
no consensus in the field as to which features are optimal for this task. In
this paper, we present the first deep learning architecture designed to capture
metaphorical composition. Our results demonstrate that it outperforms the
existing approaches in the metaphor identification task.
XFlow: Cross-modal Deep Neural Networks for Audiovisual Classification
Authors: Cătălina Cangea, Petar Veličković, Pietro Liò
Date: 2017-09-02 00:00:00
Tasks: Classification, General Classification, Lip Reading, Multimodal Deep Learning

In recent years, there have been numerous developments towards solving
multimodal tasks, aiming to learn a stronger representation than through a
single modality. Certain aspects of the data can be particularly useful in this
case - for example, correlations in the space or time domain across modalities
- but should be wisely exploited in order to benefit from their full predictive
potential. We propose two deep learning architectures with multimodal
cross-connections that allow for dataflow between several feature extractors
(XFlow). Our models derive more interpretable features and achieve better
performances than models which do not exchange representations, usefully
exploiting correlations between audio and visual data, which have a different
dimensionality and are nontrivially exchangeable. Our work improves on existing
multimodal deep learning algorithms in two essential ways: (1) it presents a
novel method for performing cross-modality (before features are learned from
individual modalities) and (2) extends the previously proposed
cross-connections which only transfer information between streams that process
compatible data. Illustrating some of the representations learned by the
connections, we analyse their contribution to the increase in discrimination
ability and reveal their compatibility with a lip-reading network intermediate
representation. We provide the research community with Digits, a new dataset
consisting of three data types extracted from videos of people saying the
digits 0-9. Results show that both cross-modal architectures outperform their
baselines (by up to 11.5%) when evaluated on the AVletters, CUAVE and Digits
datasets, achieving state-of-the-art results.
Adaptive Scaling
Authors: Ting Li, Bing-Yi Jing, Ningchen Ying, Xianshi Yu
Date: 2017-09-02 00:00:00
Tasks: regression

Preprocessing data is an important step before any data analysis. In this
paper, we focus on one particular aspect, namely scaling or normalization. We
analyze various scaling methods in common use and study their effects on
different statistical learning models. We will propose a new two-stage scaling
method. First, we use some training data to fit linear regression model and
then scale the whole data based on the coefficients of regression. Simulations
are conducted to illustrate the advantages of our new scaling method. Some real
data analysis will also be given.
Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks
Authors: Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha, Aboul Ella Hassanien, I. M. Selim
Date: 2017-09-02 00:00:00
Tasks: Classification, General Classification

In this paper, a deep convolutional neural network architecture for galaxies
classification is presented. The galaxy can be classified based on its features
into main three categories Elliptical, Spiral, and Irregular. The proposed deep
galaxies architecture consists of 8 layers, one main convolutional layer for
features extraction with 96 filters, followed by two principles fully connected
layers for classification. It is trained over 1356 images and achieved 97.272%
in testing accuracy. A comparative result is made and the testing accuracy was
compared with other related works. The proposed architecture outperformed other
related works in terms of testing accuracy.
A Generalised Quantifier Theory of Natural Language in Categorical Compositional Distributional Semantics with Bialgebras
Authors: Jules Hedges, Mehrnoosh Sadrzadeh
Date: 2016-02-04 00:00:00

Categorical compositional distributional semantics is a model of natural
language; it combines the statistical vector space models of words with the
compositional models of grammar. We formalise in this model the generalised
quantifier theory of natural language, due to Barwise and Cooper. The
underlying setting is a compact closed category with bialgebras. We start from
a generative grammar formalisation and develop an abstract categorical
compositional semantics for it, then instantiate the abstract setting to sets
and relations and to finite dimensional vector spaces and linear maps. We prove
the equivalence of the relational instantiation to the truth theoretic
semantics of generalised quantifiers. The vector space instantiation formalises
the statistical usages of words and enables us to, for the first time, reason
about quantified phrases and sentences compositionally in distributional
semantics.
Patterns versus Characters in Subword-aware Neural Language Modeling
Authors: Rustem Takhanov, Zhenisbek Assylbekov
Date: 2017-09-02 00:00:00
Tasks: Language Modeling, Language Modelling

Words in some natural languages can have a composite structure. Elements of
this structure include the root (that could also be composite), prefixes and
suffixes with which various nuances and relations to other words can be
expressed. Thus, in order to build a proper word representation one must take
into account its internal structure. From a corpus of texts we extract a set of
frequent subwords and from the latter set we select patterns, i.e. subwords
which encapsulate information on character $n$-gram regularities. The selection
is made using the pattern-based Conditional Random Field model with $l_1$
regularization. Further, for every word we construct a new sequence over an
alphabet of patterns. The new alphabet's symbols confine a local statistical
context stronger than the characters, therefore they allow better
representations in ${\mathbb{R}}^n$ and are better building blocks for word
representation. In the task of subword-aware language modeling, pattern-based
models outperform character-based analogues by 2-20 perplexity points. Also, a
recurrent neural network in which a word is represented as a sum of embeddings
of its patterns is on par with a competitive and significantly more
sophisticated character-based convolutional architecture.
An Automated Compatibility Prediction Engine using DISC Theory Based Classification and Neural Networks
Authors: Chandrasekaran Anirudh Bhardwaj, Megha Mishra, Sweetlin Hemalatha
Date: 2017-09-02 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, General Classification

Traditionally psychometric tests were used for profiling incoming workers.
These methods use DISC profiling method to classify people into distinct
personality types, which are further used to predict if a person may be a
possible fit to the organizational culture. This concept is taken further by
introducing a novel technique to predict if a particular pair of an incoming
worker and the manager being assigned are compatible at a psychological scale.
This is done using multilayer perceptron neural network which can be adaptively
trained to showcase the true nature of the compatibility index. The proposed
prototype model is used to quantify the relevant attributes, use them to train
the prediction engine, and to define the data pipeline required for it.
Learning Dense Facial Correspondences in Unconstrained Images
Authors: Ronald Yu, Shunsuke Saito, Haoxiang Li, Duygu Ceylan, Hao Li
Date: 2017-09-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Face Alignment, Face Model

We present a minimalistic but effective neural network that computes dense
facial correspondences in highly unconstrained RGB images. Our network learns a
per-pixel flow and a matchability mask between 2D input photographs of a person
and the projection of a textured 3D face model. To train such a network, we
generate a massive dataset of synthetic faces with dense labels using
renderings of a morphable face model with variations in pose, expressions,
lighting, and occlusions. We found that a training refinement using real
photographs is required to drastically improve the ability to handle real
images. When combined with a facial detection and 3D face fitting step, we show
that our approach outperforms the state-of-the-art face alignment methods in
terms of accuracy and speed. By directly estimating dense correspondences, we
do not rely on the full visibility of sparse facial landmarks and are not
limited to the model space of regression-based approaches. We also assess our
method on video frames and demonstrate successful per-frame processing under
extreme pose variations, occlusions, and lighting conditions. Compared to
existing 3D facial tracking techniques, our fitting does not rely on previous
frames or frontal facial initialization and is robust to imperfect face
detections.
Facial 3D Model Registration Under Occlusions With SensiblePoints-based Reinforced Hypothesis Refinement
Authors: Yuhang Wu, Ioannis A. Kakadiaris
Date: 2017-09-02 00:00:00

Registering a 3D facial model to a 2D image under occlusion is difficult.
First, not all of the detected facial landmarks are accurate under occlusions.
Second, the number of reliable landmarks may not be enough to constrain the
problem. We propose a method to synthesize additional points (SensiblePoints)
to create pose hypotheses. The visual clues extracted from the fiducial points,
non-fiducial points, and facial contour are jointly employed to verify the
hypotheses. We define a reward function to measure whether the projected dense
3D model is well-aligned with the confidence maps generated by two fully
convolutional networks, and use the function to train recurrent policy networks
to move the SensiblePoints. The same reward function is employed in testing to
select the best hypothesis from a candidate pool of hypotheses. Experimentation
demonstrates that the proposed approach is very promising in solving the facial
model registration problem under occlusion.
Gaussian Filter in CRF Based Semantic Segmentation
Authors: Yichi Gu, Qisheng Wu, Jing Li, Kai Cheng
Date: 2017-09-02 00:00:00
Tasks: object-detection, Object Detection, Segmentation, Semantic Segmentation

Artificial intelligence is making great changes in academy and industry with
the fast development of deep learning, which is a branch of machine learning
and statistical learning. Fully convolutional network [1] is the standard model
for semantic segmentation. Conditional random fields coded as CNN [2] or RNN
[3] and connected with FCN has been successfully applied in object detection
[4]. In this paper, we introduce a multi-resolution neural network for FCN and
apply Gaussian filter to the extended CRF kernel neighborhood and the label
image to reduce the oscillating effect of CRF neural network segmentation, thus
achieve higher precision and faster training speed.
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision
Authors: Hsiao-Yu Fish Tung, Adam W. Harley, William Seto, Katerina Fragkiadaki
Date: 2017-05-31 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Human Pose Estimation, Image-to-Image Translation, Pose Estimation, Super-Resolution, Translation

Researchers have developed excellent feed-forward models that learn to map
images to desired outputs, such as to the images' latent factors, or to other
images, using supervised learning. Learning such mappings from unlabelled data,
or improving upon supervised models by exploiting unlabelled data, remains
elusive. We argue that there are two important parts to learning without
annotations: (i) matching the predictions to the input observations, and (ii)
matching the predictions to known priors. We propose Adversarial Inverse
Graphics networks (AIGNs): weakly supervised neural network models that combine
feedback from rendering their predictions, with distribution matching between
their predictions and a collection of ground-truth factors. We apply AIGNs to
3D human pose estimation and 3D structure and egomotion estimation, and
outperform models supervised by only paired annotations. We further apply AIGNs
to facial image transformation using super-resolution and inpainting renderers,
while deliberately adding biases in the ground-truth datasets. Our model
seamlessly incorporates such biases, rendering input faces towards young, old,
feminine, masculine or Tom Cruise-like equivalents (depending on the chosen
bias), or adding lip and nose augmentations while inpainting concealed lips and
noses.
A hierarchical loss and its problems when classifying non-hierarchically
Authors: Cinna Wu, Mark Tygert, Yann Lecun
Date: 2017-09-01 00:00:00
Tasks: General Classification

Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called "loss" or "win") used in textual or visual classification/recognition via neural networks seldom leverage a-priori information, such as a sheepdog being more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier's classes. An ultrametric tree is a tree with a so-called ultrametric distance metric such that all leaves are at the same distance from the root. Unfortunately, extensive numerical experiments indicate that the standard practice of training neural networks via stochastic gradient descent with random starting points often drives down the hierarchical loss nearly as much when minimizing the standard cross-entropy loss as when trying to minimize the hierarchical loss directly. Thus, this hierarchical loss is unreliable as an objective for plain, randomly started stochastic gradient descent to minimize; the main value of the hierarchical loss may be merely as a meaningful metric of success of a classifier.
Arc-Standard Spinal Parsing with Stack-LSTMs
Authors: Miguel Ballesteros, Xavier Carreras
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: ARC

We present a neural transition-based parser for spinal trees, a dependency
representation of constituent trees. The parser uses Stack-LSTMs that compose
constituent nodes with dependency-based derivations. In experiments, we show
that this model adapts to different styles of dependency relations, but this
choice has little effect for predicting constituent structure, suggesting that
LSTMs induce useful states by themselves.
Two-Level Structural Sparsity Regularization for Identifying Lattices and Defects in Noisy Images
Authors: Xin Li, Alex Belianinov, Ondrej Dyck, Stephen Jesse, Chiwoo Park
Date: 2016-11-24 00:00:00
Tasks: regression

This paper presents a regularized regression model with a two-level
structural sparsity penalty applied to locate individual atoms in a noisy
scanning transmission electron microscopy image (STEM). In crystals, the
locations of atoms is symmetric, condensed into a few lattice groups.
Therefore, by identifying the underlying lattice in a given image, individual
atoms can be accurately located. We propose to formulate the identification of
the lattice groups as a sparse group selection problem. Furthermore, real
atomic scale images contain defects and vacancies, so atomic identification
based solely on a lattice group may result in false positives and false
negatives. To minimize error, model includes an individual sparsity
regularization in addition to the group sparsity for a within-group selection,
which results in a regression model with a two-level sparsity regularization.
We propose a modification of the group orthogonal matching pursuit (gOMP)
algorithm with a thresholding step to solve the atom finding problem. The
convergence and statistical analyses of the proposed algorithm are presented.
The proposed algorithm is also evaluated through numerical experiments with
simulated images. The applicability of the algorithm on determination of atom
structures and identification of imaging distortions and atomic defects was
demonstrated using three real STEM images. We believe this is an important step
toward automatic phase identification and assignment with the advent of genomic
databases for materials.