pwc-archive/papers-with-abstracts huggingface dataset:
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Authors: Ayan Chakrabarti, Jingyu Shao, Gregory Shakhnarovich
Date: 2016-05-23 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Depth Estimation, Monocular Depth Estimation

A single color image can contain many cues informative towards different
aspects of local geometric structure. We approach the problem of monocular
depth estimation by using a neural network to produce a mid-level
representation that summarizes these cues. This network is trained to
characterize local scene geometry by predicting, at every image location, depth
derivatives of different orders, orientations and scales. However, instead of a
single estimate for each derivative, the network outputs probability
distributions that allow it to express confidence about some coefficients, and
ambiguity about others. Scene depth is then estimated by harmonizing this
overcomplete set of network predictions, using a globalization procedure that
finds a single consistent depth map that best matches all the local derivative
distributions. We demonstrate the efficacy of this approach through evaluation
on the NYU v2 depth data set.
A Repeated Signal Difference for Recognising Patterns
Authors: Kieran Greer
Date: 2016-04-18 00:00:00

This paper describes a new mechanism that might help with defining pattern
sequences, by the fact that it can produce an upper bound on the ensemble value
that can persistently oscillate with the actual values produced from each
pattern. With every firing event, a node also receives an on/off feedback
switch. If the node fires, then it sends a feedback result depending on the
input signal strength. If the input signal is positive or larger, it can store
an 'on' switch feedback for the next iteration. If the signal is negative or
smaller, it can store an 'off' switch feedback for the next iteration. If the
node does not fire, then it does not affect the current feedback situation and
receives the switch command produced by the last active pattern event for the
same neuron. The upper bound therefore also represents the largest or most
enclosing pattern set and the lower value is for the actual set of firing
patterns. If the pattern sequence repeats, it will oscillate between the two
values, allowing them to be recognised and measured more easily, over time.
Tests show that changing the sequence ordering produces different value sets,
which can also be measured.
Visual Saliency Detection Based on Multiscale Deep CNN Features
Authors: Guanbin Li, Yizhou Yu
Date: 2016-09-07 00:00:00
Tasks: Saliency Detection

Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this paper, we discover that a
high-quality visual saliency model can be learned from multiscale features
extracted using deep convolutional neural networks (CNNs), which have had many
successes in visual recognition tasks. For learning such saliency models, we
introduce a neural network architecture, which has fully connected layers on
top of CNNs responsible for feature extraction at three different scales. The
penultimate layer of our neural network has been confirmed to be a
discriminative high-level feature vector for saliency detection, which we call
deep contrast feature. To generate a more robust feature, we integrate
handcrafted low-level features with our deep contrast feature. To promote
further research and evaluation of visual saliency models, we also construct a
new large database of 4447 challenging images and their pixelwise saliency
annotations. Experimental results demonstrate that our proposed method is
capable of achieving state-of-the-art performance on all public benchmarks,
improving the F- measure by 6.12% and 10.0% respectively on the DUT-OMRON
dataset and our new dataset (HKU-IS), and lowering the mean absolute error by
9% and 35.3% respectively on these two datasets.
The Social Dynamics of Language Change in Online Networks
Authors: Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, Fernando Diaz, Jacob Eisenstein
Date: 2016-09-07 00:00:00

Language change is a complex social phenomenon, revealing pathways of
communication and sociocultural influence. But, while language change has long
been a topic of study in sociolinguistics, traditional linguistic research
methods rely on circumstantial evidence, estimating the direction of change
from differences between older and younger speakers. In this paper, we use a
data set of several million Twitter users to track language changes in
progress. First, we show that language change can be viewed as a form of social
influence: we observe complex contagion for phonetic spellings and "netspeak"
abbreviations (e.g., lol), but not for older dialect markers from spoken
language. Next, we test whether specific types of social network connections
are more influential than others, using a parametric Hawkes process model. We
find that tie strength plays an important role: densely embedded social ties
are significantly better conduits of linguistic influence. Geographic locality
appears to play a more limited role: we find relatively little evidence to
support the hypothesis that individuals are more influenced by geographically
local social ties, even in their usage of geographical dialect markers.
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Authors: Filip Radenović, Giorgos Tolias, Ondřej Chum
Date: 2016-04-08 00:00:00
Tasks: Image Retrieval, Retrieval

Convolutional Neural Networks (CNNs) achieve state-of-the-art performance in
many computer vision tasks. However, this achievement is preceded by extreme
manual annotation in order to perform either training from scratch or
fine-tuning for the target task. In this work, we propose to fine-tune CNN for
image retrieval from a large collection of unordered images in a fully
automated manner. We employ state-of-the-art retrieval and
Structure-from-Motion (SfM) methods to obtain 3D models, which are used to
guide the selection of the training data for CNN fine-tuning. We show that both
hard positive and hard negative examples enhance the final performance in
particular object retrieval with compact codes.
Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane
Authors: Luciano Zunino, Haroldo V. Ribeiro
Date: 2016-09-06 00:00:00

The aim of this paper is to further explore the usefulness of the
two-dimensional complexity-entropy causality plane as a texture image
descriptor. A multiscale generalization is introduced in order to distinguish
between different roughness features of images at small and large spatial
scales. Numerically generated two-dimensional structures are initially
considered for illustrating basic concepts in a controlled framework. Then,
more realistic situations are studied. Obtained results allow us to confirm
that intrinsic spatial correlations of images are successfully unveiled by
implementing this multiscale symbolic information-theory approach.
Consequently, we conclude that the proposed representation space is a versatile
and practical tool for identifying, characterizing and discriminating image
textures.
Unsupervised Sentence Simplification Using Deep Semantics
Authors: Shashi Narayan, Claire Gardent
Date: 2015-07-30 00:00:00
Proceeding: WS 2016 9
Tasks: Sentence, Text Simplification

We present a novel approach to sentence simplification which departs from
previous work in two main ways. First, it requires neither hand written rules
nor a training corpus of aligned standard and simplified sentences. Second,
sentence splitting operates on deep semantic structure. We show (i) that the
unsupervised framework we propose is competitive with four state-of-the-art
supervised systems and (ii) that our semantic based approach allows for a
principled and effective handling of sentence splitting.
Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks
Authors: Davide Zambrano, Sander M. Bohte
Date: 2016-09-07 00:00:00
Tasks: Open-Ended Question Answering

Biological neurons communicate with a sparing exchange of pulses - spikes. It
is an open question how real spiking neurons produce the kind of powerful
neural computation that is possible with deep artificial neural networks, using
only so very few spikes to communicate. Building on recent insights in
neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on
adaptive spiking neurons. These spiking neurons efficiently encode information
in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while
homeostatically optimizing their firing rate. In the proposed paradigm of
spiking neuron computation, neural adaptation is tightly coupled to synaptic
plasticity, to ensure that downstream neurons can correctly decode upstream
spiking neurons. We show that this type of network is inherently able to carry
out asynchronous and event-driven neural computation, while performing
identical to corresponding artificial neural networks (ANNs). In particular, we
show that these adaptive spiking neurons can be drop in replacements for ReLU
neurons in standard feedforward ANNs comprised of such units. We demonstrate
that this can also be successfully applied to a ReLU based deep convolutional
neural network for classifying the MNIST dataset. The ASNN thus outperforms
current Spiking Neural Networks (SNNs) implementations, while responding (up
to) an order of magnitude faster and using an order of magnitude fewer spikes.
Additionally, in a streaming setting where frames are continuously classified,
we show that the ASNN requires substantially fewer network updates as compared
to the corresponding ANN.
Feasibility of Post-Editing Speech Transcriptions with a Mismatched Crowd
Authors: Purushotam Radadia, Shirish Karande
Date: 2016-09-07 00:00:00

Manual correction of speech transcription can involve a selection from
plausible transcriptions. Recent work has shown the feasibility of employing a
mismatched crowd for speech transcription. However, it is yet to be established
whether a mismatched worker has sufficiently fine-granular speech perception to
choose among the phonetically proximate options that are likely to be generated
from the trellis of an ASRU. Hence, we consider five languages, Arabic, German,
Hindi, Russian and Spanish. For each we generate synthetic, phonetically
proximate, options which emulate post-editing scenarios of varying difficulty.
We consistently observe non-trivial crowd ability to choose among fine-granular
options.
Deep Markov Random Field for Image Modeling
Authors: Zhirong Wu, Dahua Lin, Xiaoou Tang
Date: 2016-09-07 00:00:00

Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.
Equilibrium Graphs
Authors: Pedro Cabalar, Carlos Pérez, Gilberto Pérez
Date: 2016-09-07 00:00:00

In this paper we present an extension of Peirce's existential graphs to
provide a diagrammatic representation of expressions in Quantified Equilibrium
Logic (QEL). Using this formalisation, logical connectives are replaced by
encircled regions (circles and squares) and quantified variables are
represented as "identity" lines. Although the expressive power is equivalent to
that of QEL, the new representation can be useful for illustrative or
educational purposes.
Non-Evolutionary Superintelligences Do Nothing, Eventually
Authors: Telmo Menezes
Date: 2016-09-07 00:00:00

There is overwhelming evidence that human intelligence is a product of
Darwinian evolution. Investigating the consequences of self-modification, and
more precisely, the consequences of utility function self-modification, leads
to the stronger claim that not only human, but any form of intelligence is
ultimately only possible within evolutionary processes. Human-designed
artificial intelligences can only remain stable until they discover how to
manipulate their own utility function. By definition, a human designer cannot
prevent a superhuman intelligence from modifying itself, even if protection
mechanisms against this action are put in place. Without evolutionary pressure,
sufficiently advanced artificial intelligences become inert by simplifying
their own utility function. Within evolutionary processes, the implicit utility
function is always reducible to persistence, and the control of superhuman
intelligences embedded in evolutionary processes is not possible. Mechanisms
against utility function self-modification are ultimately futile. Instead,
scientific effort toward the mitigation of existential risks from the
development of superintelligences should be in two directions: understanding
consciousness, and the complex dynamics of evolutionary systems.
Human Body Orientation Estimation using Convolutional Neural Network
Authors: Jinyoung Choi, Beom-Jin Lee, Byoung-Tak Zhang
Date: 2016-09-07 00:00:00
Tasks: Face Detection

Personal robots are expected to interact with the user by recognizing the
user's face. However, in most of the service robot applications, the user needs
to move himself/herself to allow the robot to see him/her face to face. To
overcome such limitations, a method for estimating human body orientation is
required. Previous studies used various components such as feature extractors
and classification models to classify the orientation which resulted in low
performance. For a more robust and accurate approach, we propose the light
weight convolutional neural networks, an end to end system, for estimating
human body orientation. Our body orientation estimation model achieved 81.58%
and 94% accuracy with the benchmark dataset and our own dataset respectively.
The proposed method can be used in a wide range of service robot applications
which depend on the ability to estimate human body orientation. To show its
usefulness in service robot applications, we designed a simple robot
application which allows the robot to move towards the user's frontal plane.
With this, we demonstrated an improved face detection rate.
Doubly Stochastic Neighbor Embedding on Spheres
Authors: Yao Lu, Jukka Corander, Zhirong Yang
Date: 2016-09-07 00:00:00
Tasks: Data Visualization

Stochastic Neighbor Embedding (SNE) methods minimize the divergence between
the similarity matrix of a high-dimensional data set and its counterpart from a
low-dimensional embedding, leading to widely applied tools for data
visualization. Despite their popularity, the current SNE methods experience a
crowding problem when the data include highly imbalanced similarities. This
implies that the data points with higher total similarity tend to get crowded
around the display center. To solve this problem, we introduce a fast
normalization method and normalize the similarity matrix to be doubly
stochastic such that all the data points have equal total similarities.
Furthermore, we show empirically and theoretically that the doubly
stochasticity constraint often leads to embeddings which are approximately
spherical. This suggests replacing a flat space with spheres as the embedding
space. The spherical embedding eliminates the discrepancy between the center
and the periphery in visualization, which efficiently resolves the crowding
problem. We compared the proposed method (DOSNES) with the state-of-the-art SNE
method on three real-world datasets and the results clearly indicate that our
method is more favorable in terms of visualization quality.
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem
Authors: Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard
Date: 2016-09-07 00:00:00

We consider a non-stationary formulation of the stochastic multi-armed bandit
where the rewards are no longer assumed to be identically distributed. For the
best-arm identification task, we introduce a version of Successive Elimination
based on random shuffling of the $K$ arms. We prove that under a novel and mild
assumption on the mean gap $\Delta$, this simple but powerful modification
achieves the same guarantees in term of sample complexity and cumulative regret
than its original version, but in a much wider class of problems, as it is not
anymore constrained to stationary distributions. We also show that the original
{\sc Successive Elimination} fails to have controlled regret in this more
general scenario, thus showing the benefit of shuffling. We then remove our
mild assumption and adapt the algorithm to the best-arm identification task
with switching arms. We adapt the definition of the sample complexity for that
case and prove that, against an optimal policy with $N-1$ switches of the
optimal arm, this new algorithm achieves an expected sample complexity of
$O(\Delta^{-2}\sqrt{NK\delta^{-1} \log(K \delta^{-1})})$, where $\delta$ is the
probability of failure of the algorithm, and an expected cumulative regret of
$O(\Delta^{-1}{\sqrt{NTK \log (TK)}})$ after $T$ time steps.
Using Gaussian Processes for Rumour Stance Classification in Social Media
Authors: Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Arkaitz Zubiaga, Maria Liakata, Rob Procter
Date: 2016-09-07 00:00:00
Tasks: Gaussian Processes, General Classification, Multi-Task Learning, Rumour Detection, Stance Classification

Social media tend to be rife with rumours while new reports are released
piecemeal during breaking news. Interestingly, one can mine multiple reactions
expressed by social media users in those situations, exploring their stance
towards rumours, ultimately enabling the flagging of highly disputed rumours as
being potentially false. In this work, we set out to develop an automated,
supervised classifier that uses multi-task learning to classify the stance
expressed in each individual tweet in a rumourous conversation as either
supporting, denying or questioning the rumour. Using a classifier based on
Gaussian Processes, and exploring its effectiveness on two datasets with very
different characteristics and varying distributions of stances, we show that
our approach consistently outperforms competitive baseline classifiers. Our
classifier is especially effective in estimating the distribution of different
types of stance associated with a given rumour, which we set forth as a desired
characteristic for a rumour-tracking system that will warn both ordinary users
of Twitter and professional news practitioners when a rumour is being rebutted.
Object Tracking via Dynamic Feature Selection Processes
Authors: Giorgio Roffo, Simone Melzi
Date: 2016-09-07 00:00:00
Tasks: feature selection, Object, Object Tracking, Position, Visual Tracking

DFST proposes an optimized visual tracking algorithm based on the real-time
selection of locally and temporally discriminative features. A feature
selection mechanism is embedded in the Adaptive colour Names (CN) tracking
system that adaptively selects the top-ranked discriminative features for
tracking. DFST provides a significant gain in accuracy and precision allowing
the use of a dynamic set of features that results in an increased system
flexibility. DFST is based on the unsupervised method "Infinite Feature
Selection" (Inf-FS), which ranks features according with their "redundancy"
without using class labels. By using a fast online algorithm for learning
dictionaries the size of the box is adapted during the processing. At each
update, we use multiple examples around the target (at different positions and
scales). DFST also improved the CN by adding micro-shift at the predicted
position and bounding box adaptation.
Sentiment Classification of Food Reviews
Authors: Hua Feng, Ruixi Lin
Date: 2016-09-07 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

Sentiment analysis of reviews is a popular task in natural language
processing. In this work, the goal is to predict the score of food reviews on a
scale of 1 to 5 with two recurrent neural networks that are carefully tuned. As
for baseline, we train a simple RNN for classification. Then we extend the
baseline to GRU. In addition, we present two different methods to deal with
highly skewed data, which is a common problem for reviews. Models are evaluated
using accuracies.
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms
Authors: Toni Heidenreich, Michael W. Spratling
Date: 2016-09-07 00:00:00
Tasks: speech-recognition, Speech Recognition, Visual Speech Recognition

Visual speech recognition aims to identify the sequence of phonemes from
continuous speech. Unlike the traditional approach of using 2D image feature
extraction methods to derive features of each video frame separately, this
paper proposes a new approach using a 3D (spatio-temporal) Discrete Cosine
Transform to extract features of each feasible sub-sequence of an input video
which are subsequently classified individually using Support Vector Machines
and combined to find the most likely phoneme sequence using a tailor-made
Hidden Markov Model. The algorithm is trained and tested on the VidTimit
database to recognise sequences of phonemes as well as visemes (visual speech
units). Furthermore, the system is extended with the training on phoneme or
viseme pairs (biphones) to counteract the human speech ambiguity of
co-articulation. The test set accuracy for the recognition of phoneme sequences
is 20%, and the accuracy of viseme sequences is 39%. Both results improve the
best values reported in other papers by approximately 2%. The contribution of
the result is three-fold: Firstly, this paper is the first to show that 3D
feature extraction methods can be applied to continuous sequence recognition
tasks despite the unknown start positions and durations of each phoneme.
Secondly, the result confirms that 3D feature extraction methods improve the
accuracy compared to 2D features extraction methods. Thirdly, the paper is the
first to specifically compare an otherwise identical method with and without
using biphones, verifying that the usage of biphones has a positive impact on
the result.
Progressive Neural Networks
Authors: Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, Raia Hadsell
Date: 2016-06-15 00:00:00
Tasks: Continual Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sensitivity

Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy.