pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding
Authors: Mohammad Javad Shafiee, Alexander Wong
Date: 2016-09-06 00:00:00
Tasks: Clustering, GPU, image-classification, Image Classification

There has been significant recent interest towards achieving highly efficient
deep neural network architectures. A promising paradigm for achieving this is
the concept of evolutionary deep intelligence, which attempts to mimic
biological evolution processes to synthesize highly-efficient deep neural
networks over successive generations. An important aspect of evolutionary deep
intelligence is the genetic encoding scheme used to mimic heredity, which can
have a significant impact on the quality of offspring deep neural networks.
Motivated by the neurobiological phenomenon of synaptic clustering, we
introduce a new genetic encoding scheme where synaptic probability is driven
towards the formation of a highly sparse set of synaptic clusters. Experimental
results for the task of image classification demonstrated that the synthesized
offspring networks using this synaptic cluster-driven genetic encoding scheme
can achieve state-of-the-art performance while having network architectures
that are not only significantly more efficient (with a ~125-fold decrease in
synapses for MNIST) compared to the original ancestor network, but also
tailored for GPU-accelerated machine learning applications.
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge
Authors: Michel Valstar, Jonathan Gratch, Bjorn Schuller, Fabien Ringeval, Denis Lalanne, Mercedes Torres Torres, Stefan Scherer, Guiota Stratou, Roddy Cowie, Maja Pantic
Date: 2016-05-05 00:00:00
Tasks: Emotion Recognition

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood
and Emotion" will be the sixth competition event aimed at comparison of
multimedia processing and machine learning methods for automatic audio, visual
and physiological depression and emotion analysis, with all participants
competing under strictly the same conditions. The goal of the Challenge is to
provide a common benchmark test set for multi-modal information processing and
to bring together the depression and emotion recognition communities, as well
as the audio, video and physiological processing communities, to compare the
relative merits of the various approaches to depression and emotion recognition
under well-defined and strictly comparable conditions and establish to what
extent fusion of the approaches is possible and beneficial. This paper presents
the challenge guidelines, the common data used, and the performance of the
baseline system on the two tasks.
Statistical comparison of classifiers through Bayesian hierarchical modelling
Authors: Giorgio Corani, Alessio Benavoli, Janez Demšar, Francesca Mangili, Marco Zaffalon
Date: 2016-09-28 00:00:00
Tasks: Two-sample testing

Usually one compares the accuracy of two competing classifiers via null
hypothesis significance tests (nhst). Yet the nhst tests suffer from important
shortcomings, which can be overcome by switching to Bayesian hypothesis
testing. We propose a Bayesian hierarchical model which jointly analyzes the
cross-validation results obtained by two classifiers on multiple data sets. It
returns the posterior probability of the accuracies of the two classifiers
being practically equivalent or significantly different. A further strength of
the hierarchical model is that, by jointly analyzing the results obtained on
all data sets, it reduces the estimation error compared to the usual approach
of averaging the cross-validation results obtained on a given data set.
Limbo: A Fast and Flexible Library for Bayesian Optimization
Authors: Antoine Cully, Konstantinos Chatzilygeroudis, Federico Allocati, Jean-Baptiste Mouret
Date: 2016-11-22 00:00:00
Tasks: Bayesian Optimization

Limbo is an open-source C++11 library for Bayesian optimization which is
designed to be both highly flexible and very fast. It can be used to optimize
functions for which the gradient is unknown, evaluations are expensive, and
runtime cost matters (e.g., on embedded systems or robots). Benchmarks on
standard functions show that Limbo is about 2 times faster than BayesOpt
(another C++ library) for a similar accuracy.
Investigating the influence of noise and distractors on the interpretation of neural networks
Authors: Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, Sven Dähne
Date: 2016-11-22 00:00:00

Understanding neural networks is becoming increasingly important. Over the
last few years different types of visualisation and explanation methods have
been proposed. However, none of them explicitly considered the behaviour in the
presence of noise and distracting elements. In this work, we will show how
noise and distracting dimensions can influence the result of an explanation
model. This gives a new theoretical insights to aid selection of the most
appropriate explanation model within the deep-Taylor decomposition framework.
Deep Transfer Learning for Person Re-identification
Authors: Mengyue Geng, Yao-Wei Wang, Tao Xiang, Yonghong Tian
Date: 2016-11-16 00:00:00
Tasks: General Classification, image-classification, Image Classification, Person Re-Identification, Transfer Learning

Person re-identification (Re-ID) poses a unique challenge to deep learning:
how to learn a deep model with millions of parameters on a small training set
of few or no labels. In this paper, a number of deep transfer learning models
are proposed to address the data sparsity problem. First, a deep network
architecture is designed which differs from existing deep Re-ID models in that
(a) it is more suitable for transferring representations learned from large
image classification datasets, and (b) classification loss and verification
loss are combined, each of which adopts a different dropout strategy. Second, a
two-stepped fine-tuning strategy is developed to transfer knowledge from
auxiliary datasets. Third, given an unlabelled Re-ID dataset, a novel
unsupervised deep transfer learning model is developed based on co-training.
The proposed models outperform the state-of-the-art deep Re-ID models by large
margins: we achieve Rank-1 accuracy of 85.4\%, 83.7\% and 56.3\% on CUHK03,
Market1501, and VIPeR respectively, whilst on VIPeR, our unsupervised model
(45.1\%) beats most supervised models.
Multi-Path Feedback Recurrent Neural Network for Scene Parsing
Authors: Xiaojie Jin, Yunpeng Chen, Jiashi Feng, Zequn Jie, Shuicheng Yan
Date: 2016-08-27 00:00:00
Tasks: Scene Parsing

In this paper, we consider the scene parsing problem and propose a novel
Multi-Path Feedback recurrent neural network (MPF-RNN) for parsing scene
images. MPF-RNN can enhance the capability of RNNs in modeling long-range
context information at multiple levels and better distinguish pixels that are
easy to confuse. Different from feedforward CNNs and RNNs with only single
feedback, MPF-RNN propagates the contextual features learned at top layer
through \textit{multiple} weighted recurrent connections to learn bottom
features. For better training MPF-RNN, we propose a new strategy that considers
accumulative loss at multiple recurrent steps to improve performance of the
MPF-RNN on parsing small objects. With these two novel components, MPF-RNN has
achieved significant improvement over strong baselines (VGG16 and Res101) on
five challenging scene parsing benchmarks, including traditional SiftFlow,
Barcelona, CamVid, Stanford Background as well as the recently released
large-scale ADE20K.
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Authors: Scott Wisdom, Thomas Powers, James Pitton, Les Atlas
Date: 2016-11-22 00:00:00
Tasks: Compressive Sensing

Recurrent neural networks (RNNs) are powerful and effective for processing
sequential data. However, RNNs are usually considered "black box" models whose
internal structure and learned parameters are not interpretable. In this paper,
we propose an interpretable RNN based on the sequential iterative
soft-thresholding algorithm (SISTA) for solving the sequential sparse recovery
problem, which models a sequence of correlated observations with a sequence of
sparse latent vectors. The architecture of the resulting SISTA-RNN is
implicitly defined by the computational structure of SISTA, which results in a
novel stacked RNN architecture. Furthermore, the weights of the SISTA-RNN are
perfectly interpretable as the parameters of a principled statistical model,
which in this case include a sparsifying dictionary, iterative step size, and
regularization parameters. In addition, on a particular sequential compressive
sensing task, the SISTA-RNN trains faster and achieves better performance than
conventional state-of-the-art black box RNNs, including long-short term memory
(LSTM) RNNs.
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Authors: Aditya Joshi, Prayas Jain, Pushpak Bhattacharyya, Mark Carman
Date: 2016-11-14 00:00:00
Proceeding: WS 2016 12
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sarcasm Detection, Sentiment Analysis, Topic Models

Topic Models have been reported to be beneficial for aspect-based sentiment
analysis. This paper reports a simple topic model for sarcasm detection, a
first, to the best of our knowledge. Designed on the basis of the intuition
that sarcastic tweets are likely to have a mixture of words of both sentiments
as against tweets with literal sentiment (either positive or negative), our
hierarchical topic model discovers sarcasm-prevalent topics and topic-level
sentiment. Using a dataset of tweets labeled using hashtags, the model
estimates topic-level, and sentiment-level distributions. Our evaluation shows
that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics.
Our model is also able to discover the mixture of sentiment-bearing words that
exist in a text of a given sentiment-related label. Finally, we apply our model
to predict sarcasm in tweets. We outperform two prior work based on statistical
classifiers with specific features, by around 25\%.
Unimodal Thompson Sampling for Graph-Structured Arms
Authors: Stefano Paladino, Francesco Trovò, Marcello Restelli, Nicola Gatti
Date: 2016-11-17 00:00:00
Tasks: Thompson Sampling

We study, to the best of our knowledge, the first Bayesian algorithm for
unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this
setting, each arm corresponds to a node of a graph and each edge provides a
relationship, unknown to the learner, between two nodes in terms of expected
reward. Furthermore, for any node of the graph there is a path leading to the
unique node providing the maximum expected reward, along which the expected
reward is monotonically increasing. Previous results on this setting describe
the behavior of frequentist MAB algorithms. In our paper, we design a Thompson
Sampling-based algorithm whose asymptotic pseudo-regret matches the lower bound
for the considered setting. We show that -as it happens in a wide number of
scenarios- Bayesian MAB algorithms dramatically outperform frequentist ones. In
particular, we provide a thorough experimental evaluation of the performance of
our and state-of-the-art algorithms as the properties of the graph vary.
Personalizing Universal Recurrent Neural Network Language Model with User Characteristic Features by Social Network Crowdsouring
Authors: Bo-Hsiang Tseng, Hung-Yi Lee, Lin-shan Lee
Date: 2015-06-03 00:00:00
Tasks: Language Modeling, Language Modelling

With the popularity of mobile devices, personalized speech recognizer becomes
more realizable today and highly attractive. Each mobile device is primarily
used by a single user, so it's possible to have a personalized recognizer well
matching to the characteristics of individual user. Although acoustic model
personalization has been investigated for decades, much less work have been
reported on personalizing language model, probably because of the difficulties
in collecting enough personalized corpora. Previous work used the corpora
collected from social networks to solve the problem, but constructing a
personalized model for each user is troublesome. In this paper, we propose a
universal recurrent neural network language model with user characteristic
features, so all users share the same model, except each with different user
characteristic features. These user characteristic features can be obtained by
crowdsouring over social networks, which include huge quantity of texts posted
by users with known friend relationships, who may share some subject topics and
wording patterns. The preliminary experiments on Facebook corpus showed that
this proposed approach not only drastically reduced the model perplexity, but
offered very good improvement in recognition accuracy in n-best rescoring
tests. This approach also mitigated the data sparseness problem for
personalized language models.
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
Authors: Lukas Cavigelli, Pascal Hager, Luca Benini
Date: 2016-11-22 00:00:00
Tasks: Image Compression

Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
address high-level computer vision tasks very successfully. Recently, they have
found their way into the areas of low-level computer vision and image
processing to solve regression problems mostly with relatively shallow
networks.
  We present a novel 12-layer deep convolutional network for image compression
artifact suppression with hierarchical skip connections and a multi-scale loss
function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an
improvement of up to 0.36 dB over the best previous ConvNet result. We show
that a network trained for a specific quality factor (QF) is resilient to the
QF used to compress the input image - a single network trained for QF 60
provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.
A Spatial and Temporal Non-Local Filter Based Data Fusion
Authors: Qing Cheng, Huiqing Liu, Huanfeng Shen, Penghai Wu, Liangpei Zhang
Date: 2016-11-22 00:00:00
Tasks: Prediction

The trade-off in remote sensing instruments that balances the spatial
resolution and temporal frequency limits our capacity to monitor spatial and
temporal dynamics effectively. The spatiotemporal data fusion technique is
considered as a cost-effective way to obtain remote sensing data with both high
spatial resolution and high temporal frequency, by blending observations from
multiple sensors with different advantages or characteristics. In this paper,
we develop the spatial and temporal non-local filter based fusion model
(STNLFFM) to enhance the prediction capacity and accuracy, especially for
complex changed landscapes. The STNLFFM method provides a new transformation
relationship between the fine-resolution reflectance images acquired from the
same sensor at different dates with the help of coarse-resolution reflectance
data, and makes full use of the high degree of spatiotemporal redundancy in the
remote sensing image sequence to produce the final prediction. The proposed
method was tested over both the Coleambally Irrigation Area study site and the
Lower Gwydir Catchment study site. The results show that the proposed method
can provide a more accurate and robust prediction, especially for heterogeneous
landscapes and temporally dynamic areas.
Recurrent Attention Models for Depth-Based Person Identification
Authors: Albert Haque, Alexandre Alahi, Li Fei-Fei
Date: 2016-11-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Person Identification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an attention-based model that reasons on human body shape and
motion dynamics to identify individuals in the absence of RGB information,
hence in the dark. Our approach leverages unique 4D spatio-temporal signatures
to address the identification problem across days. Formulated as a
reinforcement learning task, our model is based on a combination of
convolutional and recurrent neural networks with the goal of identifying small,
discriminative regions indicative of human identity. We demonstrate that our
model produces state-of-the-art results on several published datasets given
only depth images. We further study the robustness of our model towards
viewpoint, appearance, and volumetric changes. Finally, we share insights
gleaned from interpretable 2D, 3D, and 4D visualizations of our model's
spatio-temporal attention.
Learning to Distill: The Essence Vector Modeling Framework
Authors: Kuan-Yu Chen, Shih-Hung Liu, Berlin Chen, Hsin-Min Wang
Date: 2016-11-22 00:00:00
Proceeding: COLING 2016 12
Tasks: Denoising, Document Embedding, Document Summarization, Representation Learning, Sentence, Sentiment Analysis, Sentiment Classification, speech-recognition, Speech Recognition

In the context of natural language processing, representation learning has
emerged as a newly active research subject because of its excellent performance
in many applications. Learning representations of words is a pioneering study
in this school of research. However, paragraph (or sentence and document)
embedding learning is more suitable/reasonable for some tasks, such as
sentiment classification and document summarization. Nevertheless, as far as we
are aware, there is relatively less work focusing on the development of
unsupervised paragraph embedding methods. Classic paragraph embedding methods
infer the representation of a given paragraph by considering all of the words
occurring in the paragraph. Consequently, those stop or function words that
occur frequently may mislead the embedding learning process to produce a misty
paragraph representation. Motivated by these observations, our major
contributions in this paper are twofold. First, we propose a novel unsupervised
paragraph embedding method, named the essence vector (EV) model, which aims at
not only distilling the most representative information from a paragraph but
also excluding the general background information to produce a more informative
low-dimensional vector representation for the paragraph. Second, in view of the
increasing importance of spoken content processing, an extension of the EV
model, named the denoising essence vector (D-EV) model, is proposed. The D-EV
model not only inherits the advantages of the EV model but also can infer a
more robust representation for a given spoken paragraph against imperfect
speech recognition.
Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models
Authors: Xinyu He, Warren B. Powell
Date: 2016-11-22 00:00:00
Tasks: Stochastic Optimization

We consider the problem of estimating the expected value of information (the
knowledge gradient) for Bayesian learning problems where the belief model is
nonlinear in the parameters. Our goal is to maximize some metric, while
simultaneously learning the unknown parameters of the nonlinear belief model,
by guiding a sequential experimentation process which is expensive. We overcome
the problem of computing the expected value of an experiment, which is
computationally intractable, by using a sampled approximation, which helps to
guide experiments but does not provide an accurate estimate of the unknown
parameters. We then introduce a resampling process which allows the sampled
model to adapt to new information, exploiting past experiments. We show
theoretically that the method converges asymptotically to the true parameters,
while simultaneously maximizing our metric. We show empirically that the
process exhibits rapid convergence, yielding good results with a very small
number of experiments.
Fast and Energy-Efficient CNN Inference on IoT Devices
Authors: Mohammad Motamedi, Daniel Fong, Soheil Ghiasi
Date: 2016-11-22 00:00:00

Convolutional Neural Networks (CNNs) exhibit remarkable performance in
various machine learning tasks. As sensor-equipped internet of things (IoT)
devices permeate into every aspect of modern life, it is increasingly important
to run CNN inference, a computationally intensive application, on resource
constrained devices. We present a technique for fast and energy-efficient CNN
inference on mobile SoC platforms, which are projected to be a major player in
the IoT space. We propose techniques for efficient parallelization of CNN
inference targeting mobile GPUs, and explore the underlying tradeoffs.
Experiments with running Squeezenet on three different mobile devices confirm
the effectiveness of our approach. For further study, please refer to the
project repository available on our GitHub page:
https://github.com/mtmd/Mobile_ConvNet
Learning Multi-level Deep Representations for Image Emotion Classification
Authors: Tianrong Rao, Min Xu, Dong Xu
Date: 2016-11-22 00:00:00
Tasks: Classification, Emotion Classification, General Classification

In this paper, we propose a new deep network that learns multi-level deep
representations for image emotion classification (MldrNet). Image emotion can
be recognized through image semantics, image aesthetics and low-level visual
features from both global and local views. Existing image emotion
classification works using hand-crafted features or deep features mainly focus
on either low-level visual features or semantic-level image representations
without taking all factors into consideration. The proposed MldrNet combines
deep representations of different levels, i.e. image semantics, image
aesthetics, and low-level visual features to effectively classify the emotion
types of different kinds of images, such as abstract paintings and web images.
Extensive experiments on both Internet images and abstract paintings
demonstrate the proposed method outperforms the state-of-the-art methods using
deep features or hand-crafted features. The proposed approach also outperforms
the state-of-the-art methods with at least 6% performance improvement in terms
of overall classification accuracy.
Efficient Global Point Cloud Alignment using Bayesian Nonparametric Mixtures
Authors: Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III
Date: 2016-03-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Recognition, Object Recognition

Point cloud alignment is a common problem in computer vision and robotics,
with applications ranging from 3D object recognition to reconstruction. We
propose a novel approach to the alignment problem that utilizes Bayesian
nonparametrics to describe the point cloud and surface normal densities, and
branch and bound (BB) optimization to recover the relative transformation. BB
uses a novel, refinable, near-uniform tessellation of rotation space using 4D
tetrahedra, leading to more efficient optimization compared to the common
axis-angle tessellation. We provide objective function bounds for pruning given
the proposed tessellation, and prove that BB converges to the optimum of the
cost function along with providing its computational complexity. Finally, we
empirically demonstrate the efficiency of the proposed approach as well as its
robustness to real-world conditions such as missing data and partial overlap.