pwc-archive/papers-with-abstracts huggingface dataset:
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
Authors: Ashkan Esmaeili, Arash Amini, Farokh Marvasti
Date: 2016-06-26 00:00:00
Tasks: compressed sensing, Matrix Completion

In this paper, we investigate the recovery of a sparse weight vector
(parameters vector) from a set of noisy linear combinations. However, only
partial information about the matrix representing the linear combinations is
available. Assuming a low-rank structure for the matrix, one natural solution
would be to first apply a matrix completion on the data, and then to solve the
resulting compressed sensing problem. In big data applications such as massive
MIMO and medical data, the matrix completion step imposes a huge computational
burden. Here, we propose to reduce the computational cost of the completion
task by ignoring the columns corresponding to zero elements in the sparse
vector. To this end, we employ a technique to initially approximate the support
of the sparse vector. We further propose to unify the partial matrix completion
and sparse vector recovery into an augmented four-step problem. Simulation
results reveal that the augmented approach achieves the best performance, while
both proposed methods outperform the natural two-step technique with
substantially less computational requirements.
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
Authors: Andrey Kuzmin, Dmitry Mikushin, Victor Lempitsky
Date: 2016-11-17 00:00:00
Tasks: Deep Learning, GPU, Stereo Matching, Stereo Matching Hand

We present a new deep learning-based approach for dense stereo matching.
Compared to previous works, our approach does not use deep learning of pixel
appearance descriptors, employing very fast classical matching scores instead.
At the same time, our approach uses a deep convolutional network to predict the
local parameters of cost volume aggregation process, which in this paper we
implement using differentiable domain transform. By treating such transform as
a recurrent neural network, we are able to train our whole system that includes
cost volume computation, cost-volume aggregation (smoothing), and
winner-takes-all disparity selection end-to-end. The resulting method is highly
efficient at test time, while achieving good matching accuracy. On the KITTI
2015 benchmark, it achieves a result of 6.34\% error rate while running at 29
frames per second rate on a modern GPU.
Finite LTL Synthesis is EXPTIME-complete
Authors: Jorge A. Baier, Alberto Camacho, Christian Muise, Sheila A. McIlraith
Date: 2016-09-14 00:00:00

LTL synthesis -- the construction of a function to satisfy a logical
specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete
problem with relevant applications in controller synthesis and a myriad of
artificial intelligence applications. In this research note we consider De
Giacomo and Vardi's variant of the synthesis problem for LTL formulas
interpreted over finite rather than infinite traces. Rather surprisingly, given
the existing claims on complexity, we establish that LTL synthesis is
EXPTIME-complete for the finite interpretation, and not 2EXPTIME-complete as
previously reported. Our result coincides nicely with the planning perspective
where non-deterministic planning with full observability is EXPTIME-complete
and partial observability increases the complexity to 2EXPTIME-complete; a
recent related result for LTL synthesis shows that in the finite case with
partial observability, the problem is 2EXPTIME-complete.
Beyond Sharing Weights for Deep Domain Adaptation
Authors: Artem Rozantsev, Mathieu Salzmann, Pascal Fua
Date: 2016-03-21 00:00:00
Tasks: Domain Adaptation, Object Recognition

The performance of a classifier trained on data coming from a specific domain
typically degrades when applied to a related but different one. While
annotating many samples from the new domain would address this issue, it is
often too expensive or impractical. Domain Adaptation has therefore emerged as
a solution to this problem; It leverages annotated data from a source domain,
in which it is abundant, to train a classifier to operate in a target domain,
in which it is either sparse or even lacking altogether. In this context, the
recent trend consists of learning deep architectures whose weights are shared
for both domains, which essentially amounts to learning domain invariant
features.
  Here, we show that it is more effective to explicitly model the shift from
one domain to the other. To this end, we introduce a two-stream architecture,
where one operates in the source domain and the other in the target domain. In
contrast to other approaches, the weights in corresponding layers are related
but not shared. We demonstrate that this both yields higher accuracy than
state-of-the-art methods on several object recognition and detection tasks and
consistently outperforms networks with shared weights in both supervised and
unsupervised settings.
Study on Feature Subspace of Archetypal Emotions for Speech Emotion Recognition
Authors: Xi Ma, Zhiyong Wu, Jia Jia, Mingxing Xu, Helen Meng, Lianhong Cai
Date: 2016-11-17 00:00:00
Tasks: Emotion Recognition, General Classification, Speech Emotion Recognition

Feature subspace selection is an important part in speech emotion
recognition. Most of the studies are devoted to finding a feature subspace for
representing all emotions. However, some studies have indicated that the
features associated with different emotions are not exactly the same. Hence,
traditional methods may fail to distinguish some of the emotions with just one
global feature subspace. In this work, we propose a new divide and conquer idea
to solve the problem. First, the feature subspaces are constructed for all the
combinations of every two different emotions (emotion-pair). Bi-classifiers are
then trained on these feature subspaces respectively. The final emotion
recognition result is derived by the voting and competition method.
Experimental results demonstrate that the proposed method can get better
results than the traditional multi-classification method.
Learning to detect and localize many objects from few examples
Authors: Bastien Moysset, Christoper Kermorvant, Christian Wolf
Date: 2016-11-17 00:00:00
Tasks: object-detection, Object Detection

The current trend in object detection and localization is to learn
predictions with high capacity deep neural networks trained on a very large
amount of annotated data and using a high amount of processing power. In this
work, we propose a new neural model which directly predicts bounding box
coordinates. The particularity of our contribution lies in the local
computations of predictions with a new form of local parameter sharing which
keeps the overall amount of trainable parameters low. Key components of the
model are spatial 2D-LSTM recurrent layers which convey contextual information
between the regions of the image. We show that this model is more powerful than
the state of the art in applications where training data is not as abundant as
in the classical configuration of natural images and Imagenet/Pascal VOC tasks.
We particularly target the detection of text in document images, but our method
is not limited to this setting. The proposed model also facilitates the
detection of many objects in a single image and can deal with inputs of
variable sizes without resizing.
WS4A: a Biomedical Question and Answering System based on public Web Services and Ontologies
Authors: Miguel J. Rodrigues, Miguel Falé, Andre Lamurias, Francisco M. Couto
Date: 2016-09-27 00:00:00
Tasks: Retrieval

This paper describes our system, dubbed WS4A (Web Services for All), that
participated in the fourth edition of the BioASQ challenge (2016). We used WS4A
to perform the Question and Answering (QA) task 4b, which consisted on the
retrieval of relevant concepts, documents, snippets, RDF triples, exact answers
and ideal answers for each given question. The novelty in our approach consists
on the maximum exploitation of existing web services in each step of WS4A, such
as the annotation of text, and the retrieval of metadata for each annotation.
The information retrieved included concept identifiers, ontologies, ancestors,
and most importantly, PubMed identifiers. The paper describes the WS4A pipeline
and also presents the precision, recall and f-measure values obtained in task
4b. Our system achieved two second places in two subtasks on one of the five
batches.
Inverting The Generator Of A Generative Adversarial Network
Authors: Antonia Creswell, Anil Anthony Bharath
Date: 2016-11-17 00:00:00
Tasks: Generative Adversarial Network, image-classification, Image Classification, Image Retrieval, One-Shot Learning, Retrieval

Generative adversarial networks (GANs) learn to synthesise new samples from a
high-dimensional distribution by passing samples drawn from a latent space
through a generative network. When the high-dimensional distribution describes
images of a particular data set, the network should learn to generate visually
similar image samples for latent variables that are close to each other in the
latent space. For tasks such as image retrieval and image classification, it
may be useful to exploit the arrangement of the latent space by projecting
images into it, and using this as a representation for discriminative tasks.
GANs often consist of multiple layers of non-linear computations, making them
very difficult to invert. This paper introduces techniques for projecting image
samples into the latent space using any pre-trained GAN, provided that the
computational graph is available. We evaluate these techniques on both MNIST
digits and Omniglot handwritten characters. In the case of MNIST digits, we
show that projections into the latent space maintain information about the
style and the identity of the digit. In the case of Omniglot characters, we
show that even characters from alphabets that have not been seen during
training may be projected well into the latent space; this suggests that this
approach may have applications in one-shot learning.
Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks
Authors: Cristóbal Esteban, Oliver Staeck, Yinchong Yang, Volker Tresp
Date: 2016-02-08 00:00:00

In clinical data sets we often find static information (e.g. patient gender,
blood type, etc.) combined with sequences of data that are recorded during
multiple hospital visits (e.g. medications prescribed, tests performed, etc.).
Recurrent Neural Networks (RNNs) have proven to be very successful for
modelling sequences of data in many areas of Machine Learning. In this work we
present an approach based on RNNs, specifically designed for the clinical
domain, that combines static and dynamic information in order to predict future
events. We work with a database collected in the Charit\'{e} Hospital in Berlin
that contains complete information concerning patients that underwent a kidney
transplantation. After the transplantation three main endpoints can occur:
rejection of the kidney, loss of the kidney and death of the patient. Our goal
is to predict, based on information recorded in the Electronic Health Record of
each patient, whether any of those endpoints will occur within the next six or
twelve months after each visit to the clinic. We compared different types of
RNNs that we developed for this work, with a model based on a Feedforward
Neural Network and a Logistic Regression model. We found that the RNN that we
developed based on Gated Recurrent Units provides the best performance for this
task. We also used the same models for a second task, i.e., next event
prediction, and found that here the model based on a Feedforward Neural Network
outperformed the other models. Our hypothesis is that long-term dependencies
are not as relevant in this task.
Stream Packing for Asynchronous Multi-Context Systems using ASP
Authors: Stefan Ellmauthaler, Jörg Pührer
Date: 2016-11-17 00:00:00

When a processing unit relies on data from external streams, we may face the
problem that the stream data needs to be rearranged in a way that allows the
unit to perform its task(s). On arrival of new data, we must decide whether
there is sufficient information available to start processing or whether to
wait for more data. Furthermore, we need to ensure that the data meets the
input specification of the processing step. In the case of multiple input
streams it is also necessary to coordinate which data from which incoming
stream should form the input of the next process instantiation. In this work,
we propose a declarative approach as an interface between multiple streams and
a processing unit. The idea is to specify via answer-set programming how to
arrange incoming data in packages that are suitable as input for subsequent
processing. Our approach is intended for use in asynchronous multi-context
systems (aMCSs), a recently proposed framework for loose coupling of knowledge
representation formalisms that allows for online reasoning in a dynamic
environment. Contexts in aMCSs process data streams from external sources and
other contexts.
Sparsity-driven weighted ensemble classifier
Authors: Atilla Ozgur, Hamit Erdem, Fatih Nar
Date: 2016-10-02 00:00:00
Tasks: General Classification

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affect classifier accuracy. In the proposed method, ensemble weights finding problem is modeled as a cost function with the following terms: (a) a data fidelity term aiming to decrease misclassification rate, (b) a sparsity term aiming to decrease the number of classifiers, and (c) a non-negativity constraint on the weights of the classifiers. As the proposed cost function is non-convex thus hard to solve, convex relaxation techniques and novel approximations are employed to obtain a numerically efficient solution. Sparsity term of cost function allows trade-off between accuracy and testing time when needed. The efficiency of SDWEC was tested on 11 datasets and compared with the state-of-the art classifier ensemble methods. The results show that SDWEC provides better or similar accuracy levels using fewer classifiers and reduces testing time for ensemble.
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization
Authors: Kai Yu, Biao Leng, Zhang Zhang, Dangwei Li, Kaiqi Huang
Date: 2016-11-17 00:00:00
Tasks: Attribute, Clustering, image-classification, Image Classification, Multi-Label Image Classification, object-detection, Object Detection, Pedestrian Attribute Recognition, Weakly-supervised Learning, Weakly Supervised Object Detection

State-of-the-art methods treat pedestrian attribute recognition as a
multi-label image classification problem. The location information of person
attributes is usually eliminated or simply encoded in the rigid splitting of
whole body in previous work. In this paper, we formulate the task in a
weakly-supervised attribute localization framework. Based on GoogLeNet,
firstly, a set of mid-level attribute features are discovered by novelly
designed detection layers, where a max-pooling based weakly-supervised object
detection technique is used to train these layers with only image-level labels
without the need of bounding box annotations of pedestrian attributes.
Secondly, attribute labels are predicted by regression of the detection
response magnitudes. Finally, the locations and rough shapes of pedestrian
attributes can be inferred by performing clustering on a fusion of activation
maps of the detection layers, where the fusion weights are estimated as the
correlation strengths between each attribute and its relevant mid-level
features. Extensive experiments are performed on the two currently largest
pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset.
Results show that the proposed method has achieved competitive performance on
attribute recognition, compared to other state-of-the-art methods. Moreover,
the results of attribute localization are visualized to understand the
characteristics of the proposed method.
Multimodal Memory Modelling for Video Captioning
Authors: Junbo Wang, Wei Wang, Yan Huang, Liang Wang, Tieniu Tan
Date: 2016-11-17 00:00:00
Tasks: Decoder, Sentence, Video Captioning

Video captioning which automatically translates video clips into natural
language sentences is a very important task in computer vision. By virtue of
recent deep learning technologies, e.g., convolutional neural networks (CNNs)
and recurrent neural networks (RNNs), video captioning has made great progress.
However, learning an effective mapping from visual sequence space to language
space is still a challenging problem. In this paper, we propose a Multimodal
Memory Model (M3) to describe videos, which builds a visual and textual shared
memory to model the long-term visual-textual dependency and further guide
global visual attention on described targets. Specifically, the proposed M3
attaches an external memory to store and retrieve both visual and textual
contents by interacting with video and sentence with multiple read and write
operations. First, text representation in the Long Short-Term Memory (LSTM)
based text decoder is written into the memory, and the memory contents will be
read out to guide an attention to select related visual targets. Then, the
selected visual information is written into the memory, which will be further
read out to the text decoder. To evaluate the proposed model, we perform
experiments on two publicly benchmark datasets: MSVD and MSR-VTT. The
experimental results demonstrate that our method outperforms the
state-of-theart methods in terms of BLEU and METEOR.
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM
Authors: Yan Huang, Wei Wang, Liang Wang
Date: 2016-11-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence

Effective image and sentence matching depends on how to well measure their
global visual-semantic similarity. Based on the observation that such a global
similarity arises from a complex aggregation of multiple local similarities
between pairwise instances of image (objects) and sentence (words), we propose
a selective multimodal Long Short-Term Memory network (sm-LSTM) for
instance-aware image and sentence matching. The sm-LSTM includes a multimodal
context-modulated attention scheme at each timestep that can selectively attend
to a pair of instances of image and sentence, by predicting pairwise
instance-aware saliency maps for image and sentence. For selected pairwise
instances, their representations are obtained based on the predicted saliency
maps, and then compared to measure their local similarity. By similarly
measuring multiple local similarities within a few timesteps, the sm-LSTM
sequentially aggregates them with hidden states to obtain a final matching
score as the desired global similarity. Extensive experiments show that our
model can well match image and sentence with complex content, and achieve the
state-of-the-art results on two public benchmark datasets.
Approximating Wisdom of Crowds using K-RBMs
Authors: Abhay Gupta
Date: 2016-11-16 00:00:00
Tasks: Clustering

An important way to make large training sets is to gather noisy labels from
crowds of non experts. We propose a method to aggregate noisy labels collected
from a crowd of workers or annotators. Eliciting labels is important in tasks
such as judging web search quality and rating products. Our method assumes that
labels are generated by a probability distribution over items and labels. We
formulate the method by drawing parallels between Gaussian Mixture Models
(GMMs) and Restricted Boltzmann Machines (RBMs) and show that the problem of
vote aggregation can be viewed as one of clustering. We use K-RBMs to perform
clustering. We finally show some empirical evaluations over real datasets.
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Authors: Hisham Cholakkal, Jubin Johnson, Deepu Rajan
Date: 2016-11-16 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.
Automatic Node Selection for Deep Neural Networks using Group Lasso Regularization
Authors: Tsubasa Ochiai, Shigeki Matsuda, Hideyuki Watanabe, Shigeru Katagiri
Date: 2016-11-17 00:00:00
Tasks: General Classification, Playing the Game of 2048

We examine the effect of the Group Lasso (gLasso) regularizer in selecting
the salient nodes of Deep Neural Network (DNN) hidden layers by applying a
DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of
gLasso regularization, one for outgoing weight vectors and another for incoming
weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096
nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment
results demonstrate that our DNN training, in which the gLasso regularizer was
embedded, successfully selected the hidden layer nodes that are necessary and
sufficient for achieving high classification power.