pwc-archive/papers-with-abstracts huggingface dataset:
Online Adaptation of Deep Architectures with Reinforcement Learning
Authors: Thushan Ganegedara, Lionel Ott, Fabio Ramos
Date: 2016-08-08 00:00:00
Tasks: Denoising, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Online learning has become crucial to many problems in machine learning. As
more data is collected sequentially, quickly adapting to changes in the data
distribution can offer several competitive advantages such as avoiding loss of
prior knowledge and more efficient learning. However, adaptation to changes in
the data distribution (also known as covariate shift) needs to be performed
without compromising past knowledge already built in into the model to cope
with voluminous and dynamic data. In this paper, we propose an online stacked
Denoising Autoencoder whose structure is adapted through reinforcement
learning. Our algorithm forces the network to exploit and explore favourable
architectures employing an estimated utility function that maximises the
accuracy of an unseen validation sequence. Different actions, such as Pool,
Increment and Merge are available to modify the structure of the network. As we
observe through a series of experiments, our approach is more responsive,
robust, and principled than its counterparts for non-stationary as well as
stationary data distributions. Experimental results indicate that our algorithm
performs better at preserving gained prior knowledge and responding to changes
in the data distribution.
Detecting Sarcasm in Multimodal Social Platforms
Authors: Rossano Schifanella, Paloma de Juan, Joel Tetreault, Liangliang Cao
Date: 2016-08-08 00:00:00

Sarcasm is a peculiar form of sentiment expression, where the surface
sentiment differs from the implied sentiment. The detection of sarcasm in
social media platforms has been applied in the past mainly to textual
utterances where lexical indicators (such as interjections and intensifiers),
linguistic markers, and contextual information (such as user profiles, or past
conversations) were used to detect the sarcastic tone. However, modern social
media platforms allow to create multimodal messages where audiovisual content
is integrated with the text, making the analysis of a mode in isolation
partial. In our work, we first study the relationship between the textual and
visual aspects in multimodal posts from three major social media platforms,
i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to
quantify the extent to which images are perceived as necessary by human
annotators. Moreover, we propose two different computational frameworks to
detect sarcasm that integrate the textual and visual modalities. The first
approach exploits visual semantics trained on an external dataset, and
concatenates the semantics features with state-of-the-art textual features. The
second method adapts a visual neural network initialized with parameters
trained on ImageNet to multimodal sarcastic posts. Results show the positive
effect of combining modalities for the detection of sarcasm across platforms
and methods.
Delta Epsilon Alpha Star: A PAC-Admissible Search Algorithm
Authors: David Cox
Date: 2016-08-08 00:00:00

Delta Epsilon Alpha Star is a minimal coverage, real-time robotic search
algorithm that yields a moderately aggressive search path with minimal
backtracking. Search performance is bounded by a placing a combinatorial bound,
epsilon and delta, on the maximum deviation from the theoretical shortest path
and the probability at which further deviations can occur. Additionally, we
formally define the notion of PAC-admissibility -- a relaxed admissibility
criteria for algorithms, and show that PAC-admissible algorithms are better
suited to robotic search situations than epsilon-admissible or strict
algorithms.
Statistical Guarantees for Estimating the Centers of a Two-component Gaussian Mixture by EM
Authors: Jason M. Klusowski, W. D. Brinda
Date: 2016-08-07 00:00:00
Tasks: LEMMA, valid

Recently, a general method for analyzing the statistical accuracy of the EM
algorithm has been developed and applied to some simple latent variable models
[Balakrishnan et al. 2016]. In that method, the basin of attraction for valid
initialization is required to be a ball around the truth. Using Stein's Lemma,
we extend these results in the case of estimating the centers of a
two-component Gaussian mixture in $d$ dimensions. In particular, we
significantly expand the basin of attraction to be the intersection of a half
space and a ball around the origin. If the signal-to-noise ratio is at least a
constant multiple of $ \sqrt{d\log d} $, we show that a random initialization
strategy is feasible.
Incorporation of Speech Duration Information in Score Fusion of Speaker Recognition Systems
Authors: Ali Khodabakhsh, Seyyed Saeed Sarfjoo, Umut Uludag, Osman Soyyigit, Cenk Demiroglu
Date: 2016-08-07 00:00:00
Tasks: Speaker Recognition, Speaker Verification

In recent years identity-vector (i-vector) based speaker verification (SV)
systems have become very successful. Nevertheless, environmental noise and
speech duration variability still have a significant effect on degrading the
performance of these systems. In many real-life applications, duration of
recordings are very short; as a result, extracted i-vectors cannot reliably
represent the attributes of the speaker. Here, we investigate the effect of
speech duration on the performance of three state-of-the-art speaker
recognition systems. In addition, using a variety of available score fusion
methods, we investigate the effect of score fusion for those speaker
verification techniques to benefit from the performance difference of different
methods under different enrollment and test speech duration conditions. This
technique performed significantly better than the baseline score fusion
methods.
Spontaneous Facial Micro-Expression Recognition using Discriminative Spatiotemporal Local Binary Pattern with an Improved Integral Projection
Authors: Xiaohua Huang, Su-Jing Wang, Xin Liu, Guoying Zhao, Xiaoyi Feng, Matti Pietikainen
Date: 2016-08-07 00:00:00
Tasks: Attribute, feature selection, Micro Expression Recognition, Micro-Expression Recognition

Recently, there are increasing interests in inferring mirco-expression from
facial image sequences. Due to subtle facial movement of micro-expressions,
feature extraction has become an important and critical issue for spontaneous
facial micro-expression recognition. Recent works usually used spatiotemporal
local binary pattern for micro-expression analysis. However, the commonly used
spatiotemporal local binary pattern considers dynamic texture information to
represent face images while misses the shape attribute of face images. On the
other hand, their works extracted the spatiotemporal features from the global
face regions, which ignore the discriminative information between two
micro-expression classes. The above-mentioned problems seriously limit the
application of spatiotemporal local binary pattern on micro-expression
recognition. In this paper, we propose a discriminative spatiotemporal local
binary pattern based on an improved integral projection to resolve the problems
of spatiotemporal local binary pattern for micro-expression recognition.
Firstly, we develop an improved integral projection for preserving the shape
attribute of micro-expressions. Furthermore, an improved integral projection is
incorporated with local binary pattern operators across spatial and temporal
domains. Specifically, we extract the novel spatiotemporal features
incorporating shape attributes into spatiotemporal texture features. For
increasing the discrimination of micro-expressions, we propose a new feature
selection based on Laplacian method to extract the discriminative information
for facial micro-expression recognition. Intensive experiments are conducted on
three availably published micro-expression databases. We compare our method
with the state-of-the-art algorithms. Experimental results demonstrate that our
proposed method achieves promising performance for micro-expression
recognition.
Reconciling Lambek's restriction, cut-elimination, and substitution in the presence of exponential modalities
Authors: Max Kanovich, Stepan Kuznetsov, Andre Scedrov
Date: 2016-08-07 00:00:00

The Lambek calculus can be considered as a version of non-commutative intuitionistic linear logic. One of the interesting features of the Lambek calculus is the so-called "Lambek's restriction," that is, the antecedent of any provable sequent should be non-empty. In this paper we discuss ways of extending the Lambek calculus with the linear logic exponential modality while keeping Lambek's restriction. Interestingly enough, we show that for any system equipped with a reasonable exponential modality the following holds: if the system enjoys cut elimination and substitution to the full extent, then the system necessarily violates Lambek's restriction. Nevertheless, we show that two of the three conditions can be implemented. Namely, we design a system with Lambek's restriction and cut elimination and another system with Lambek's restriction and substitution. For both calculi we prove that they are undecidable, even if we take only one of the two divisions provided by the Lambek calculus. The system with cut elimination and substitution and without Lambek's restriction is folklore and known to be undecidable.
Edge Based Grid Super-Imposition for Crowd Emotion Recognition
Authors: Amol Patwardhan
Date: 2016-08-07 00:00:00
Tasks: Edge Detection, Emotion Recognition

Numerous automatic continuous emotion detection system studies have examined
mostly use of videos and images containing individual person expressing
emotions. This study examines the detection of spontaneous emotions in a group
and crowd settings. Edge detection was used with a grid of lines
superimposition to extract the features. The feature movement in terms of
movement from the reference point was used to track across sequences of images
from the color channel. Additionally the video data capturing was done on
spontaneous emotions invoked by watching sports events from group of
participants. The method was view and occlusion independent and the results
were not affected by presence of multiple people chaotically expressing various
emotions. The edge thresholds of 0.2 and grid thresholds of 20 showed the best
accuracy results. The overall accuracy of the group emotion classifier was
70.9%.
Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty
Authors: Edward Johns, Stefan Leutenegger, Andrew J. Davison
Date: 2016-08-07 00:00:00

This paper presents a new method for parallel-jaw grasping of isolated
objects from depth images, under large gripper pose uncertainty. Whilst most
approaches aim to predict the single best grasp pose from an image, our method
first predicts a score for every possible grasp pose, which we denote the grasp
function. With this, it is possible to achieve grasping robust to the gripper's
pose uncertainty, by smoothing the grasp function with the pose uncertainty
function. Therefore, if the single best pose is adjacent to a region of poor
grasp quality, that pose will no longer be chosen, and instead a pose will be
chosen which is surrounded by a region of high grasp quality. To learn this
function, we train a Convolutional Neural Network which takes as input a single
depth image of an object, and outputs a score for each grasp pose across the
image. Training data for this is generated by use of physics simulation and
depth image simulation with 3D object meshes, to enable acquisition of
sufficient data without requiring exhaustive real-world experiments. We
evaluate with both synthetic and real experiments, and show that the learned
grasp score is more robust to gripper pose uncertainty than when this
uncertainty is not accounted for.
Bootstrapping Face Detection with Hard Negative Examples
Authors: Shaohua Wan, Zhijun Chen, Tao Zhang, Bo Zhang, Kong-kat Wong
Date: 2016-08-07 00:00:00
Tasks: Face Detection

Recently significant performance improvement in face detection was made
possible by deeply trained convolutional networks. In this report, a novel
approach for training state-of-the-art face detector is described. The key is
to exploit the idea of hard negative mining and iteratively update the Faster
R-CNN based face detector with the hard negatives harvested from a large set of
background examples. We demonstrate that our face detector outperforms
state-of-the-art detectors on the FDDB dataset, which is the de facto standard
for evaluating face detection algorithms.
Towards the Self-constructive Brain: emergence of adaptive behavior
Authors: Fernando Corbacho
Date: 2016-08-07 00:00:00

Adaptive behavior is mainly the result of adaptive brains. We go a step
beyond and claim that the brain does not only adapt to its surrounding reality
but rather, it builds itself up to constructs its own reality. That is, rather
than just trying to passively understand its environment, the brain is the
architect of its own reality in an active process where its internal models of
the external world frame how its new interactions with the environment are
assimilated. These internal models represent relevant predictive patterns of
interaction all over the different brain structures: perceptual, sensorimotor,
motor, etc. The emergence of adaptive behavior arises from this
self-constructive nature of the brain, based on the following principles of
organization: self-experimental, self- growing, and self-repairing.
Self-experimental, since to ensure survival, the self-constructive brain (SCB)
is an active machine capable of performing experiments of its own interactions
with the environment by mental simulation. Self-growing, since it dynamically
and incrementally constructs internal structures in order to build a model of
the world as it gathers statistics from its interactions with the environment.
Self-repairing, since to survive the SCB must also be robust and capable of
finding ways to repair parts of previously working structures and hence
re-construct a previous relevant pattern of activity.
Undecidability of the Lambek calculus with a relevant modality
Authors: Max Kanovich, Stepan Kuznetsov, Andre Scedrov
Date: 2016-01-23 00:00:00
Tasks: valid

Morrill and Valentin in the paper "Computational coverage of TLG:
Nonlinearity" considered an extension of the Lambek calculus enriched by a
so-called "exponential" modality. This modality behaves in the "relevant"
style, that is, it allows contraction and permutation, but not weakening.
Morrill and Valentin stated an open problem whether this system is decidable.
Here we show its undecidability. Our result remains valid if we consider the
fragment where all division operations have one direction. We also show that
the derivability problem in a restricted case, where the modality can be
applied only to variables (primitive types), is decidable and belongs to the NP
class.
Residual CNDS
Authors: Hussein A. Al-Barazanchi, Hussam Qassim, Abhishek Verma
Date: 2016-08-07 00:00:00
Tasks: General Classification, image-classification, Image Classification

Convolutional Neural networks nowadays are of tremendous importance for any
image classification system. One of the most investigated methods to increase
the accuracy of CNN is by increasing the depth of CNN. Increasing the depth by
stacking more layers also increases the difficulty of training besides making
it computationally expensive. Some research found that adding auxiliary forks
after intermediate layers increases the accuracy. Specifying which intermediate
layer shoud have the fork just addressed recently. Where a simple rule were
used to detect the position of intermediate layers that needs the auxiliary
supervision fork. This technique known as convolutional neural networks with
deep supervision (CNDS). This technique enhanced the accuracy of classification
over the straight forward CNN used on the MIT places dataset and ImageNet. In
the other side, Residual Learning is another technique emerged recently to ease
the training of very deep CNN. Residual Learning framwork changed the learning
of layers from unreferenced functions to learning residual function with regard
to the layer's input. Residual Learning achieved state of arts results on
ImageNet 2015 and COCO competitions. In this paper, we study the effect of
adding residual connections to CNDS network. Our experiments results show
increasing of accuracy over using CNDS only.
Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
Authors: Yiren Zhou, Hossein Nejati, Thanh-Toan Do, Ngai-Man Cheung, Lynette Cheah
Date: 2016-01-06 00:00:00
Tasks: Classification, General Classification, vehicle detection

We address the vehicle detection and classification problems using Deep
Neural Networks (DNNs) approaches. Here we answer to questions that are
specific to our application including how to utilize DNN for vehicle detection,
what features are useful for vehicle classification, and how to extend a model
trained on a limited size dataset, to the cases of extreme lighting condition.
Answering these questions we propose our approach that outperforms
state-of-the-art methods, and achieves promising results on image with extreme
lighting conditions.
Automating Political Bias Prediction
Authors: Felix Biessmann
Date: 2016-08-07 00:00:00
Tasks: Articles, Prediction

Every day media generate large amounts of text. An unbiased view on media
reports requires an understanding of the political bias of media content.
Assistive technology for estimating the political bias of texts can be helpful
in this context. This study proposes a simple statistical learning approach to
predict political bias from text. Standard text features extracted from
speeches and manifestos of political parties are used to predict political bias
in terms of political party affiliation and in terms of political views.
Results indicate that political bias can be predicted with above chance
accuracy. Mistakes of the model can be interpreted with respect to changes of
policies of political actors. Two approaches are presented to make the results
more interpretable: a) discriminative text features are related to the
political orientation of a party and b) sentiment features of texts are
correlated with a measure of political power. Political power appears to be
strongly correlated with positive sentiment of a text. To highlight some
potential use cases a web application shows how the model can be used for texts
for which the political bias is not clear such as news articles.
Playing for Data: Ground Truth from Computer Games
Authors: Stephan R. Richter, Vibhav Vineet, Stefan Roth, Vladlen Koltun
Date: 2016-08-07 00:00:00
Tasks: Semantic Segmentation

Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
Authors: Thomas Goldstein, Paul Hand, Choongbum Lee, Vladislav Voroninski, Stefano Soatto
Date: 2016-08-07 00:00:00

We introduce a new method for location recovery from pair-wise directions
that leverages an efficient convex program that comes with exact recovery
guarantees, even in the presence of adversarial outliers. When pairwise
directions represent scaled relative positions between pairs of views
(estimated for instance with epipolar geometry) our method can be used for
location recovery, that is the determination of relative pose up to a single
unknown scale. For this task, our method yields performance comparable to the
state-of-the-art with an order of magnitude speed-up. Our proposed numerical
framework is flexible in that it accommodates other approaches to location
recovery and can be used to speed up other methods. These properties are
demonstrated by extensively testing against state-of-the-art methods for
location recovery on 13 large, irregular collections of images of real scenes
in addition to simulated data with ground truth.
Adapting Deep Network Features to Capture Psychological Representations
Authors: Joshua C. Peterson, Joshua T. Abbott, Thomas L. Griffiths
Date: 2016-08-06 00:00:00
Tasks: Object Recognition, Scene Understanding, Semantic Segmentation

Deep neural networks have become increasingly successful at solving classic
perception problems such as object recognition, semantic segmentation, and
scene understanding, often reaching or surpassing human-level accuracy. This
success is due in part to the ability of DNNs to learn useful representations
of high-dimensional inputs, a problem that humans must also solve. We examine
the relationship between the representations learned by these networks and
human psychological representations recovered from similarity judgments. We
find that deep features learned in service of object classification account for
a significant amount of the variance in human similarity judgments for a set of
animal images. However, these features do not capture some qualitative
distinctions that are a key part of human representations. To remedy this, we
develop a method for adapting deep features to align with human similarity
judgments, resulting in image representations that can potentially be used to
extend the scope of psychological experiments.
Neural networks with differentiable structure
Authors: Thomas Miconi
Date: 2016-06-20 00:00:00

While gradient descent has proven highly successful in learning connection
weights for neural networks, the actual structure of these networks is usually
determined by hand, or by other optimization algorithms. Here we describe a
simple method to make network structure differentiable, and therefore
accessible to gradient descent. We test this method on recurrent neural
networks applied to simple sequence prediction problems. Starting with initial
networks containing only one node, the method automatically builds networks
that successfully solve the tasks. The number of nodes in the final network
correlates with task difficulty. The method can dynamically increase network
size in response to an abrupt complexification in the task; however, reduction
in network size in response to task simplification is not evident for
reasonable meta-parameters. The method does not penalize network performance
for these test tasks: variable-size networks actually reach better performance
than fixed-size networks of higher, lower or identical size. We conclude by
discussing how this method could be applied to more complex networks, such as
feedforward layered networks, or multiple-area networks of arbitrary shape.