pwc-archive/papers-with-abstracts huggingface dataset:
Alternative Semantic Representations for Zero-Shot Human Action Recognition
Authors: Qian Wang, Ke Chen
Date: 2017-06-28 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Zero-Shot Action Recognition, Zero-Shot Learning

A proper semantic representation for encoding side information is key to the
success of zero-shot learning. In this paper, we explore two alternative
semantic representations especially for zero-shot human action recognition:
textual descriptions of human actions and deep features extracted from still
images relevant to human actions. Such side information are accessible on Web
with little cost, which paves a new way in gaining side information for
large-scale zero-shot human action recognition. We investigate different
encoding methods to generate semantic representations for human actions from
such side information. Based on our zero-shot visual recognition method, we
conducted experiments on UCF101 and HMDB51 to evaluate two proposed semantic
representations . The results suggest that our proposed text- and image-based
semantic representations outperform traditional attributes and word vectors
considerably for zero-shot human action recognition. In particular, the
image-based semantic representations yield the favourable performance even
though the representation is extracted from a small number of images per class.
AP17-OLR Challenge: Data, Plan, and Baseline
Authors: Zhiyuan Tang, Dong Wang, Yixiang Chen, Qing Chen
Date: 2017-06-28 00:00:00

We present the data profile and the evaluation plan of the second oriental
language recognition (OLR) challenge AP17-OLR. Compared to the event last year
(AP16-OLR), the new challenge involves more languages and focuses more on short
utterances. The data is offered by SpeechOcean and the NSFC M2ASR project. Two
types of baselines are constructed to assist the participants, one is based on
the i-vector model and the other is based on various neural networks. We report
the baseline results evaluated with various metrics defined by the AP17-OLR
evaluation plan and demonstrate that the combined database is a reasonable data
resource for multilingual research. All the data is free for participants, and
the Kaldi recipes for the baselines have been published online.
Coupling Adaptive Batch Sizes with Learning Rates
Authors: Lukas Balles, Javier Romero, Philipp Hennig
Date: 2016-12-15 00:00:00
Tasks: image-classification, Image Classification, Stochastic Optimization

Mini-batch stochastic gradient descent and variants thereof have become
standard for large-scale empirical risk minimization like the training of
neural networks. These methods are usually used with a constant batch size
chosen by simple empirical inspection. The batch size significantly influences
the behavior of the stochastic optimization algorithm, though, since it
determines the variance of the gradient estimates. This variance also changes
over the optimization process; when using a constant batch size, stability and
convergence is thus often enforced by means of a (manually tuned) decreasing
learning rate schedule.
  We propose a practical method for dynamic batch size adaptation. It estimates
the variance of the stochastic gradients and adapts the batch size to decrease
the variance proportionally to the value of the objective function, removing
the need for the aforementioned learning rate decrease. In contrast to recent
related work, our algorithm couples the batch size to the learning rate,
directly reflecting the known relationship between the two. On popular image
classification benchmarks, our batch size adaptation yields faster optimization
convergence, while simultaneously simplifying learning rate tuning. A
TensorFlow implementation is available.
Asymptotic Confidence Regions for High-dimensional Structured Sparsity
Authors: Benjamin Stucky, Sara van de Geer
Date: 2017-06-28 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

In the setting of high-dimensional linear regression models, we propose two
frameworks for constructing pointwise and group confidence sets for penalized
estimators which incorporate prior knowledge about the organization of the
non-zero coefficients. This is done by desparsifying the estimator as in van de
Geer et al. [18] and van de Geer and Stucky [17], then using an appropriate
estimator for the precision matrix $\Theta$. In order to estimate the precision
matrix a corresponding structured matrix norm penalty has to be introduced.
  After normalization the result is an asymptotic pivot.
  The asymptotic behavior is studied and simulations are added to study the
differences between the two schemes.
Approximation of probability density functions on the Euclidean group parametrized by dual quaternions
Authors: Muriel Lang
Date: 2017-06-28 00:00:00
Tasks: Translation

Perception is fundamental to many robot application areas especially in
service robotics. Our aim is to perceive and model an unprepared kitchen
scenario with many objects. We start with the perception of a single target
object. The modeling relies especially on fusing and merging of weak
information from the sensors of the robot in order to localize objects. This
requires the representation of various probability distributions of pose in
$S_3 \times \mathbb{R}^3$ as orientation and position have to be localized. In
this thesis I present a framework for probabilistic modeling of poses in $S_3
\times \mathbb{R}^3$ that represents a large class of probability distributions
and provides among others the operations of the fusion and the merge of
estimates. Further it offers the propagation of uncertain information data. I
work out why we choose to represent the orientation part of a pose by a unit
quaternion. The translation part is described either by a 3-dimensional vector
or by a purely imaginary quaternion. This depends on whether we define the
probability density function or whether we want to represent a transformation
which consists of a rotation and a translation by a dual quaternion. A basic
probability den- sity function over the poses is defined by a tangent point on
the hypersphere and a 6-dimensional Gaussian distribution. The hypersphere is
embedded to the R4 which is representing a unit quaternions whereas the
Gaussian is defined over the product of the tangent space of the sphere and of
the space of translations. The projection of this Gaussian to the hypersphere
induces a distribution over poses in $S_3 \times \mathbb{R}^3$. The set of
mixtures of projected Gaussians can approximate the probability density
functions that arise in our application. Moreover it is closed under the
operations introduced in this framework and allows for an efficient
implementation.
A Roadmap for the Development of the "SP Machine" for Artificial Intelligence
Authors: J. Gerard Wolff
Date: 2017-06-28 00:00:00

This paper describes a roadmap for the development of the "SP Machine", based
on the "SP Theory of Intelligence" and its realisation in the "SP Computer
Model". The SP Machine will be developed initially as a software virtual
machine with high levels of parallel processing, hosted on a high-performance
computer. The system should help users visualise knowledge structures and
processing. Research is needed into how the system may discover low-level
features in speech and in images. Strengths of the SP System in the processing
of natural language may be augmented, in conjunction with the further
development of the SP System's strengths in unsupervised learning. Strengths of
the SP System in pattern recognition may be developed for computer vision. Work
is needed on the representation of numbers and the performance of arithmetic
processes. A computer model is needed of "SP-Neural", the version of the SP
Theory expressed in terms of neurons and their inter-connections. The SP
Machine has potential in many areas of application, several of which may be
realised on short-to-medium timescales.
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
Authors: Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, Sungroh Yoon
Date: 2017-06-28 00:00:00
Tasks: Imitation Learning, Recommendation Systems, Time Series, Time Series Analysis

Recommender systems aim to find an accurate and efficient mapping from
historic data of user-preferred items to a new item that is to be liked by a
user. Towards this goal, energy-based sequence generative adversarial nets
(EB-SeqGANs) are adopted for recommendation by learning a generative model for
the time series of user-preferred items. By recasting the energy function as
the feature function, the proposed EB-SeqGANs is interpreted as an instance of
maximum-entropy imitation learning.
A Parameterized Approach to Personalized Variable Length Summarization of Soccer Matches
Authors: Mohak Sukhwani, Ravi Kothari
Date: 2017-06-28 00:00:00

We present a parameterized approach to produce personalized variable length
summaries of soccer matches. Our approach is based on temporally segmenting the
soccer video into 'plays', associating a user-specifiable 'utility' for each
type of play and using 'bin-packing' to select a subset of the plays that add
up to the desired length while maximizing the overall utility (volume in
bin-packing terms). Our approach systematically allows a user to override the
default weights assigned to each type of play with individual preferences and
thus see a highly personalized variable length summarization of soccer matches.
We demonstrate our approach based on the output of an end-to-end pipeline that
we are building to produce such summaries. Though aspects of the overall
end-to-end pipeline are human assisted at present, the results clearly show
that the proposed approach is capable of producing semantically meaningful and
compelling summaries. Besides the obvious use of producing summaries of
superior league matches for news broadcasts, we anticipate our work to promote
greater awareness of the local matches and junior leagues by producing
consumable summaries of them.
Population-Contrastive-Divergence: Does Consistency help with RBM training?
Authors: Oswin Krause, Asja Fischer, Christian Igel
Date: 2015-10-06 00:00:00

Estimating the log-likelihood gradient with respect to the parameters of a
Restricted Boltzmann Machine (RBM) typically requires sampling using Markov
Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov
chains are only run for a small number of steps, which leads to a biased
estimate. This bias can cause RBM training algorithms such as Contrastive
Divergence (CD) learning to deteriorate. We adopt the idea behind Population
Monte Carlo (PMC) methods to devise a new RBM training algorithm termed
Population-Contrastive-Divergence (pop-CD). Compared to CD, it leads to a
consistent estimate and may have a significantly lower bias. Its computational
overhead is negligible compared to CD. However, the variance of the gradient
estimate increases. We experimentally show that pop-CD can significantly
outperform CD. In many cases, we observed a smaller bias and achieved higher
log-likelihood values. However, when the RBM distribution has many hidden
neurons, the consistent estimate of pop-CD may still have a considerable bias
and the variance of the gradient estimate requires a smaller learning rate.
Thus, despite its superior theoretical properties, it is not advisable to use
pop-CD in its current form on large problems.
Perceptual Adversarial Networks for Image-to-Image Transformation
Authors: Chaoyue Wang, Chang Xu, Chaohui Wang, DaCheng Tao
Date: 2017-06-28 00:00:00
Tasks: Image Inpainting

In this paper, we propose a principled Perceptual Adversarial Networks (PAN)
for image-to-image transformation tasks. Unlike existing application-specific
algorithms, PAN provides a generic framework of learning mapping relationship
between paired images (Fig. 1), such as mapping a rainy image to its de-rained
counterpart, object edges to its photo, semantic labels to a scenes image, etc.
The proposed PAN consists of two feed-forward convolutional neural networks
(CNNs), the image transformation network T and the discriminative network D.
Through combining the generative adversarial loss and the proposed perceptual
adversarial loss, these two networks can be trained alternately to solve
image-to-image transformation tasks. Among them, the hidden layers and output
of the discriminative network D are upgraded to continually and automatically
discover the discrepancy between the transformed image and the corresponding
ground-truth. Simultaneously, the image transformation network T is trained to
minimize the discrepancy explored by the discriminative network D. Through the
adversarial training process, the image transformation network T will
continually narrow the gap between transformed images and ground-truth images.
Experiments evaluated on several image-to-image transformation tasks (e.g.,
image de-raining, image inpainting, etc.) show that the proposed PAN
outperforms many related state-of-the-art methods.
Towards Metamerism via Foveated Style Transfer
Authors: Arturo Deza, Aditya Jonnalagadda, Miguel Eckstein
Date: 2017-05-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Metamerism, Style Transfer, Texture Synthesis

The problem of $\textit{visual metamerism}$ is defined as finding a family of
perceptually indistinguishable, yet physically different images. In this paper,
we propose our NeuroFovea metamer model, a foveated generative model that is
based on a mixture of peripheral representations and style transfer
forward-pass algorithms. Our gradient-descent free model is parametrized by a
foveated VGG19 encoder-decoder which allows us to encode images in high
dimensional space and interpolate between the content and texture information
with adaptive instance normalization anywhere in the visual field. Our
contributions include: 1) A framework for computing metamers that resembles a
noisy communication system via a foveated feed-forward encoder-decoder network
-- We observe that metamerism arises as a byproduct of noisy perturbations that
partially lie in the perceptual null space; 2) A perceptual optimization scheme
as a solution to the hyperparametric nature of our metamer model that requires
tuning of the image-texture tradeoff coefficients everywhere in the visual
field which are a consequence of internal noise; 3) An ABX psychophysical
evaluation of our metamers where we also find that the rate of growth of the
receptive fields in our model match V1 for reference metamers and V2 between
synthesized samples. Our model also renders metamers at roughly a second,
presenting a $\times1000$ speed-up compared to the previous work, which allows
for tractable data-driven metamer experiments.
Robust Lane Tracking with Multi-mode Observation Model and Particle Filtering
Authors: Jiawei Huang, Zhaowen Wang
Date: 2017-06-28 00:00:00

Automatic lane tracking involves estimating the underlying signal from a
sequence of noisy signal observations. Many models and methods have been
proposed for lane tracking, and dynamic targets tracking in general. The Kalman
Filter is a widely used method that works well on linear Gaussian models. But
this paper shows that Kalman Filter is not suitable for lane tracking, because
its Gaussian observation model cannot faithfully represent the procured
observations. We propose using a Particle Filter on top of a novel multiple
mode observation model. Experiments show that our method produces superior
performance to a conventional Kalman Filter.
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Authors: Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, Hanning Zhou
Date: 2016-11-16 00:00:00
Tasks: Clustering

Clustering is among the most fundamental tasks in computer vision and machine
learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel
unsupervised generative clustering approach within the framework of Variational
Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure
with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM
picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN
decodes the latent embedding into observables. Inference in VaDE is done in a
variational way: a different DNN is used to encode observables to latent
embeddings, so that the evidence lower bound (ELBO) can be optimized using
Stochastic Gradient Variational Bayes (SGVB) estimator and the
reparameterization trick. Quantitative comparisons with strong baselines are
included in this paper, and experimental results show that VaDE significantly
outperforms the state-of-the-art clustering methods on 4 benchmarks from
various modalities. Moreover, by VaDE's generative nature, we show its
capability of generating highly realistic samples for any specified cluster,
without using supervised information during training. Lastly, VaDE is a
flexible and extensible framework for unsupervised generative clustering, more
general mixture models than GMM can be easily plugged in.
Submodular Function Maximization for Group Elevator Scheduling
Authors: Srikumar Ramalingam, Arvind U. Raghunathan, Daniel Nikovski
Date: 2017-06-28 00:00:00
Tasks: Scheduling

We propose a novel approach for group elevator scheduling by formulating it
as the maximization of submodular function under a matroid constraint. In
particular, we propose to model the total waiting time of passengers using a
quadratic Boolean function. The unary and pairwise terms in the function denote
the waiting time for single and pairwise allocation of passengers to elevators,
respectively. We show that this objective function is submodular. The matroid
constraints ensure that every passenger is allocated to exactly one elevator.
We use a greedy algorithm to maximize the submodular objective function, and
derive provable guarantees on the optimality of the solution. We tested our
algorithm using Elevate 8, a commercial-grade elevator simulator that allows
simulation with a wide range of elevator settings. We achieve significant
improvement over the existing algorithms.
HTM-MAT: An online prediction software toolbox based on cortical machine learning algorithm
Authors: V. I. Anireh, EN Osegi
Date: 2017-06-28 00:00:00
Tasks: BIG-bench Machine Learning

HTM-MAT is a MATLAB based toolbox for implementing cortical learning
algorithms (CLA) including related cortical-like algorithms that possesses
spatiotemporal properties. CLA is a suite of predictive machine learning
algorithms developed by Numenta Inc. and is based on the hierarchical temporal
memory (HTM). This paper presents an implementation of HTM-MAT with several
illustrative examples including several toy datasets and compared with two
sequence learning applications employing state-of-the-art algorithms - the
recurrentjs based on the Long Short-Term Memory (LSTM) algorithm and OS-ELM
which is based on an online sequential version of the Extreme Learning Machine.
The performance of HTM-MAT using two historical benchmark datasets and one real
world dataset is also compared with one of the existing sequence learning
applications, the OS-ELM. The results indicate that HTM-MAT predictions are
indeed competitive and can outperform OS-ELM in sequential prediction tasks.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning
Authors: Jianpeng Zhang, Yong Xia, Qi Wu, Yutong Xie
Date: 2017-06-28 00:00:00
Tasks: General Classification, image-classification, Image Classification, Medical Image Classification, Retrieval

The Classification of medical images and illustrations in the literature aims
to label a medical image according to the modality it was produced or label an
illustration according to its production attributes. It is an essential and
challenging research hotspot in the area of automated literature review,
retrieval and mining. The significant intra-class variation and inter-class
similarity caused by the diverse imaging modalities and various illustration
types brings a great deal of difficulties to the problem. In this paper, we
propose a synergic deep learning (SDL) model to address this issue.
Specifically, a dual deep convolutional neural network with a synergic signal
system is designed to mutually learn image representation. The synergic signal
is used to verify whether the input image pair belongs to the same category and
to give the corrective feedback if a synergic error exists. Our SDL model can
be trained 'end to end'. In the test phase, the class label of an input can be
predicted by averaging the likelihood probabilities obtained by two
convolutional neural network components. Experimental results on the
ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL
model achieves the state-of-the art performance in this medical image
classification problem and its accuracy is higher than that of the first place
solution on the Challenge leader board so far.
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions
Authors: Huitian Lei, Yangyi Lu, Ambuj Tewari, Susan A. Murphy
Date: 2017-06-28 00:00:00

Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses real-time data collection and communication capabilities of modern mobile devices to deliver interventions in real-time that are adapted to the in-the-moment needs of the user. The lack of methodological guidance in constructing data-based JITAIs remains a hurdle in advancing JITAI research despite the increasing popularity of JITAIs among clinical scientists. In this article, we make a first attempt to bridge this methodological gap by formulating the task of tailoring interventions in real-time as a contextual bandit problem. Interpretability requirements in the domain of mobile health lead us to formulate the problem differently from existing formulations intended for web applications such as ad or news article placement. Under the assumption of linear reward function, we choose the reward function (the "critic") parameterization separately from a lower dimensional parameterization of stochastic policies (the "actor"). We provide an online actor-critic algorithm that guides the construction and refinement of a JITAI. Asymptotic properties of the actor-critic algorithm are developed and backed up by numerical experiments. Additional numerical experiments are conducted to test the robustness of the algorithm when idealized assumptions used in the analysis of contextual bandit algorithm are breached.
Super-Resolution via Deep Learning
Authors: Khizar Hayat
Date: 2017-06-28 00:00:00
Tasks: Benchmarking, Deep Learning, Super-Resolution

The recent phenomenal interest in convolutional neural networks (CNNs) must
have made it inevitable for the super-resolution (SR) community to explore its
potential. The response has been immense and in the last three years, since the
advent of the pioneering work, there appeared too many works not to warrant a
comprehensive survey. This paper surveys the SR literature in the context of
deep learning. We focus on the three important aspects of multimedia - namely
image, video and multi-dimensions, especially depth maps. In each case, first
relevant benchmarks are introduced in the form of datasets and state of the art
SR methods, excluding deep learning. Next is a detailed analysis of the
individual works, each including a short description of the method and a
critique of the results with special reference to the benchmarking done. This
is followed by minimum overall benchmarking in the form of comparison on some
common dataset, while relying on the results reported in various works.