pwc-archive/papers-with-abstracts huggingface dataset:
Cascade Learning by Optimally Partitioning
Authors: Yanwei Pang, Jiale Cao, Xuelong. Li
Date: 2015-08-18 00:00:00
Tasks: Face Detection, object-detection, Object Detection

Cascaded AdaBoost classifier is a well-known efficient object detection
algorithm. The cascade structure has many parameters to be determined. Most of
existing cascade learning algorithms are designed by assigning detection rate
and false positive rate to each stage either dynamically or statically. Their
objective functions are not directly related to minimum computation cost. These
algorithms are not guaranteed to have optimal solution in the sense of
minimizing computation cost. On the assumption that a strong classifier is
given, in this paper we propose an optimal cascade learning algorithm (we call
it iCascade) which iteratively partitions the strong classifiers into two parts
until predefined number of stages are generated. iCascade searches the optimal
number ri of weak classifiers of each stage i by directly minimizing the
computation cost of the cascade. Theorems are provided to guarantee the
existence of the unique optimal solution. Theorems are also given for the
proposed efficient algorithm of searching optimal parameters ri. Once a new
stage is added, the parameter ri for each stage decreases gradually as
iteration proceeds, which we call decreasing phenomenon. Moreover, with the
goal of minimizing computation cost, we develop an effective algorithm for
setting the optimal threshold of each stage classifier. In addition, we prove
in theory why more new weak classifiers are required compared to the last
stage. Experimental results on face detection demonstrate the effectiveness and
efficiency of the proposed algorithm.
What is Holding Back Convnets for Detection?
Authors: Bojan Pepik, Rodrigo Benenson, Tobias Ritschel, Bernt Schiele
Date: 2015-08-12 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

Convolutional neural networks have recently shown excellent results in
general object detection and many other tasks. Albeit very effective, they
involve many user-defined design choices. In this paper we want to better
understand these choices by inspecting two key aspects "what did the network
learn?", and "what can the network learn?". We exploit new annotations
(Pascal3D+), to enable a new empirical analysis of the R-CNN detector. Despite
common belief, our results indicate that existing state-of-the-art convnet
architectures are not invariant to various appearance factors. In fact, all
considered networks have similar weak points which cannot be mitigated by
simply increasing the training data (architectural changes are needed). We show
that overall performance can improve when using image renderings for data
augmentation. We report the best known results on the Pascal3D+ detection and
view-point estimation tasks.
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
Authors: Markus Heinonen, Henrik Mannerström, Juho Rousu, Samuel Kaski, Harri Lähdesmäki
Date: 2015-08-18 00:00:00
Tasks: GPR, regression

We present a novel approach for fully non-stationary Gaussian process
regression (GPR), where all three key parameters -- noise variance, signal
variance and lengthscale -- can be simultaneously input-dependent. We develop
gradient-based inference methods to learn the unknown function and the
non-stationary model parameters, without requiring any model approximations. We
propose to infer full parameter posterior with Hamiltonian Monte Carlo (HMC),
which conveniently extends the analytical gradient-based GPR learning by
guiding the sampling with model gradients. We also learn the MAP solution from
the posterior by gradient ascent. In experiments on several synthetic datasets
and in modelling of temporal gene expression, the nonstationary GPR is shown to
be necessary for modeling realistic input-dependent dynamics, while it performs
comparably to conventional stationary or previous non-stationary GPR models
otherwise.
Deep clustering: Discriminative embeddings for segmentation and separation
Authors: John R. Hershey, Zhuo Chen, Jonathan Le Roux, Shinji Watanabe
Date: 2015-08-18 00:00:00
Tasks: Clustering, Deep Clustering, Image Segmentation, Semantic Segmentation, Speech Separation

We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.
Multimodal Deep Learning for Robust RGB-D Object Recognition
Authors: Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, Martin Riedmiller, Wolfram Burgard
Date: 2015-07-24 00:00:00
Tasks: Data Augmentation, Deep Learning, Multimodal Deep Learning, Object, Object Recognition

Robust object recognition is a crucial ingredient of many, if not all,
real-world robotics applications. This paper leverages recent progress on
Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture
for object recognition. Our architecture is composed of two separate CNN
processing streams - one for each modality - which are consecutively combined
with a late fusion network. We focus on learning with imperfect sensor data, a
typical problem in real-world robotics tasks. For accurate learning, we
introduce a multi-stage training methodology and two crucial ingredients for
handling depth data with CNNs. The first, an effective encoding of depth
information for CNNs that enables learning without the need for large depth
datasets. The second, a data augmentation scheme for robust learning with depth
images by corrupting them with realistic noise patterns. We present
state-of-the-art results on the RGB-D object dataset and show recognition in
challenging RGB-D real-world noisy settings.
Reward Shaping with Recurrent Neural Networks for Speeding up On-Line Policy Learning in Spoken Dialogue Systems
Authors: Pei-Hao Su, David Vandyke, Milica Gasic, Nikola Mrksic, Tsung-Hsien Wen, Steve Young
Date: 2015-08-14 00:00:00
Proceeding: WS 2015 9
Tasks: Reinforcement Learning, Spoken Dialogue Systems

Statistical spoken dialogue systems have the attractive property of being
able to be optimised from data via interactions with real users. However in the
reinforcement learning paradigm the dialogue manager (agent) often requires
significant time to explore the state-action space to learn to behave in a
desirable manner. This is a critical issue when the system is trained on-line
with real users where learning costs are expensive. Reward shaping is one
promising technique for addressing these concerns. Here we examine three
recurrent neural network (RNN) approaches for providing reward shaping
information in addition to the primary (task-orientated) environmental
feedback. These RNNs are trained on returns from dialogues generated by a
simulated user and attempt to diffuse the overall evaluation of the dialogue
back down to the turn level to guide the agent towards good behaviour faster.
In both simulated and real user scenarios these RNNs are shown to increase
policy learning speed. Importantly, they do not require prior knowledge of the
user's goal.
Probabilistic Modelling of Morphologically Rich Languages
Authors: Jan A. Botha
Date: 2015-08-18 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Translation

This thesis investigates how the sub-structure of words can be accounted for
in probabilistic models of language. Such models play an important role in
natural language processing tasks such as translation or speech recognition,
but often rely on the simplistic assumption that words are opaque symbols. This
assumption does not fit morphologically complex language well, where words can
have rich internal structure and sub-word elements are shared across distinct
word forms.
  Our approach is to encode basic notions of morphology into the assumptions of
three different types of language models, with the intention that leveraging
shared sub-word structure can improve model performance and help overcome data
sparsity that arises from morphological processes.
  In the context of n-gram language modelling, we formulate a new Bayesian
model that relies on the decomposition of compound words to attain better
smoothing, and we develop a new distributed language model that learns vector
representations of morphemes and leverages them to link together
morphologically related words. In both cases, we show that accounting for word
sub-structure improves the models' intrinsic performance and provides benefits
when applied to other tasks, including machine translation.
  We then shift the focus beyond the modelling of word sequences and consider
models that automatically learn what the sub-word elements of a given language
are, given an unannotated list of words. We formulate a novel model that can
learn discontiguous morphemes in addition to the more conventional contiguous
morphemes that most previous models are limited to. This approach is
demonstrated on Semitic languages, and we find that modelling discontiguous
sub-word structures leads to improvements in the task of segmenting words into
their contiguous morphemes.
Preprint ARPPS Augmented Reality Pipeline Prospect System
Authors: Xiao-Lei Zhang, Yong Han, DongSheng Hao, Zhihan Lv
Date: 2015-08-18 00:00:00

This is the preprint version of our paper on ICONIP. Outdoor augmented
reality geographic information system (ARGIS) is the hot application of
augmented reality over recent years. This paper concludes the key solutions of
ARGIS, designs the mobile augmented reality pipeline prospect system (ARPPS),
and respectively realizes the machine vision based pipeline prospect system
(MVBPPS) and the sensor based pipeline prospect system (SBPPS). With the
MVBPPS's realization, this paper studies the neural network based 3D features
matching method.
Minimizing the Number of Matching Queries for Object Retrieval
Authors: Johannes Niedermayer, Peer Kröger
Date: 2014-12-18 00:00:00
Tasks: Computational Efficiency, Object, Retrieval

To increase the computational efficiency of interest-point based object
retrieval, researchers have put remarkable research efforts into improving the
efficiency of kNN-based feature matching, pursuing to match thousands of
features against a database within fractions of a second. However, due to the
high-dimensional nature of image features that reduces the effectivity of index
structures (curse of dimensionality), due to the vast amount of features stored
in image databases (images are often represented by up to several thousand
features), this ultimate goal demanded to trade query runtimes for query
precision. In this paper we address an approach complementary to indexing in
order to improve the runtimes of retrieval by querying only the most promising
keypoint descriptors, as this affects matching runtimes linearly and can
therefore lead to increased efficiency. As this reduction of kNN queries
reduces the number of tentative correspondences, a loss of query precision is
minimized by an additional image-level correspondence generation stage with a
computational performance independent of the underlying indexing structure. We
evaluate such an adaption of the standard recognition pipeline on a variety of
datasets using both SIFT and state-of-the-art binary descriptors. Our results
suggest that decreasing the number of queried descriptors does not necessarily
imply a reduction in the result quality as long as alternative ways of
increasing query recall (by thoroughly selecting k) and MAP (using image-level
correspondence generation) are considered.
Partial Optimality by Pruning for MAP-Inference with General Graphical Models
Authors: Paul Swoboda, Alexander Shekhovtsov, Jörg Hendrik Kappes, Christoph Schnörr, Bogdan Savchynskyy
Date: 2014-10-24 00:00:00
Proceeding: CVPR 2014 6

We consider the energy minimization problem for undirected graphical models,
also known as MAP-inference problem for Markov random fields which is NP-hard
in general. We propose a novel polynomial time algorithm to obtain a part of
its optimal non-relaxed integral solution. Our algorithm is initialized with
variables taking integral values in the solution of a convex relaxation of the
MAP-inference problem and iteratively prunes those, which do not satisfy our
criterion for partial optimality. We show that our pruning strategy is in a
certain sense theoretically optimal. Also empirically our method outperforms
previous approaches in terms of the number of persistently labelled variables.
The method is very general, as it is applicable to models with arbitrary
factors of an arbitrary order and can employ any solver for the considered
relaxed problem. Our method's runtime is determined by the runtime of the
convex relaxation solver for the MAP-inference problem.
Image tag completion by local learning
Authors: Jing-Yan Wang, Yihua Zhou, Haoxiang Wang, Xiaohong Yang, Feng Yang, Austin Peterson
Date: 2015-08-18 00:00:00
Tasks: TAG

The problem of tag completion is to learn the missing tags of an image. In
this paper, we propose to learn a tag scoring vector for each image by local
linear learning. A local linear function is used in the neighborhood of each
image to predict the tag scoring vectors of its neighboring images. We
construct a unified objective function for the learning of both tag scoring
vectors and local linear function parame- ters. In the objective, we impose the
learned tag scoring vectors to be consistent with the known associations to the
tags of each image, and also minimize the prediction error of each local linear
function, while reducing the complexity of each local function. The objective
function is optimized by an alternate optimization strategy and gradient
descent methods in an iterative algorithm. We compare the proposed algorithm
against different state-of-the-art tag completion methods, and the results show
its advantages.
Representing data by sparse combination of contextual data points for classification
Authors: Jing-Yan Wang, Yihua Zhou, Ming Yin, Shaochang Chen, Benjamin Edwards
Date: 2015-06-30 00:00:00
Tasks: General Classification

In this paper, we study the problem of using contextual da- ta points of a
data point for its classification problem. We propose to represent a data point
as the sparse linear reconstruction of its context, and learn the sparse
context to gather with a linear classifier in a su- pervised way to increase
its discriminative ability. We proposed a novel formulation for context
learning, by modeling the learning of context reconstruction coefficients and
classifier in a unified objective. In this objective, the reconstruction error
is minimized and the coefficient spar- sity is encouraged. Moreover, the hinge
loss of the classifier is minimized and the complexity of the classifier is
reduced. This objective is opti- mized by an alternative strategy in an
iterative algorithm. Experiments on three benchmark data set show its advantage
over state-of-the-art context-based data representation and classification
methods.
When Are Tree Structures Necessary for Deep Learning of Representations?
Authors: Jiwei Li, Minh-Thang Luong, Dan Jurafsky, Eudard Hovy
Date: 2015-02-28 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Deep Learning, Discourse Parsing, Relation, Relation Extraction, Sentence, Sentiment Analysis, Sentiment Classification

Recursive neural models, which use syntactic parse trees to recursively
generate representations bottom-up, are a popular architecture. But there have
not been rigorous evaluations showing for exactly which tasks this syntax-based
method is appropriate. In this paper we benchmark {\bf recursive} neural models
against sequential {\bf recurrent} neural models (simple recurrent and LSTM
models), enforcing apples-to-apples comparison as much as possible. We
investigate 4 tasks: (1) sentiment classification at the sentence level and
phrase level; (2) matching questions to answer-phrases; (3) discourse parsing;
(4) semantic relation extraction (e.g., {\em component-whole} between nouns).
  Our goal is to understand better when, and why, recursive models can
outperform simpler models. We find that recursive models help mainly on tasks
(like semantic relation extraction) that require associating headwords across a
long distance, particularly on very long sequences. We then introduce a method
for allowing recurrent models to achieve similar performance: breaking long
sentences into clause-like units at punctuation and processing them separately
before combining. Our results thus help understand the limitations of both
classes of models, and suggest directions for improving recurrent models.
Supervised learning of sparse context reconstruction coefficients for data representation and classification
Authors: Xuejie Liu, Jingbin Wang, Ming Yin, Benjamin Edwards, Peijuan Xu
Date: 2015-08-18 00:00:00
Tasks: Classification, General Classification

Context of data points, which is usually defined as the other data points in
a data set, has been found to play important roles in data representation and
classification. In this paper, we study the problem of using context of a data
point for its classification problem. Our work is inspired by the observation
that actually only very few data points are critical in the context of a data
point for its representation and classification. We propose to represent a data
point as the sparse linear combination of its context, and learn the sparse
context in a supervised way to increase its discriminative ability. To this
end, we proposed a novel formulation for context learning, by modeling the
learning of context parameter and classifier in a unified objective, and
optimizing it with an alternative strategy in an iterative algorithm.
Experiments on three benchmark data set show its advantage over
state-of-the-art context-based data representation and classification methods.
Supervised cross-modal factor analysis for multiple modal data classification
Authors: Jingbin Wang, Yihua Zhou, Kanghong Duan, Jim Jing-Yan Wang, Halima Bensmail
Date: 2015-02-18 00:00:00
Tasks: Document Classification, General Classification

In this paper we study the problem of learning from multiple modal data for
purpose of document classification. In this problem, each document is composed
two different modals of data, i.e., an image and a text. Cross-modal factor
analysis (CFA) has been proposed to project the two different modals of data to
a shared data space, so that the classification of a image or a text can be
performed directly in this space. A disadvantage of CFA is that it has ignored
the supervision information. In this paper, we improve CFA by incorporating the
supervision information to represent and classify both image and text modals of
documents. We project both image and text data to a shared data space by factor
analysis, and then train a class label predictor in the shared space to use the
class label information. The factor analysis parameter and the predictor
parameter are learned jointly by solving one single objective function. With
this objective function, we minimize the distance between the projections of
image and text of the same document, and the classification error of the
projection measured by hinge loss function. The objective function is optimized
by an alternate optimization strategy in an iterative algorithm. Experiments in
two different multiple modal document data sets show the advantage of the
proposed algorithm over other CFA methods.
Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data
Authors: Changwei Hu, Piyush Rai, Changyou Chen, Matthew Harding, Lawrence Carin
Date: 2015-08-18 00:00:00

We present a Bayesian non-negative tensor factorization model for
count-valued tensor data, and develop scalable inference algorithms (both batch
and online) for dealing with massive tensors. Our generative model can handle
overdispersed counts as well as infer the rank of the decomposition. Moreover,
leveraging a reparameterization of the Poisson distribution as a multinomial
facilitates conjugacy in the model and enables simple and efficient Gibbs
sampling and variational Bayes (VB) inference updates, with a computational
cost that only depends on the number of nonzeros in the tensor. The model also
provides a nice interpretability for the factors; in our model, each factor
corresponds to a "topic". We develop a set of online inference algorithms that
allow further scaling up the model to massive tensors, for which batch
inference methods may be infeasible. We apply our framework on diverse
real-world applications, such as \emph{multiway} topic modeling on a scientific
publications database, analyzing a political science data set, and analyzing a
massive household transactions data set.
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
Authors: Changwei Hu, Piyush Rai, Lawrence Carin
Date: 2015-08-18 00:00:00
Tasks: Bayesian Inference

We present a scalable Bayesian model for low-rank factorization of massive
tensors with binary observations. The proposed model has the following key
properties: (1) in contrast to the models based on the logistic or probit
likelihood, using a zero-truncated Poisson likelihood for binary data allows
our model to scale up in the number of \emph{ones} in the tensor, which is
especially appealing for massive but sparse binary tensors; (2)
side-information in form of binary pairwise relationships (e.g., an adjacency
network) between objects in any tensor mode can also be leveraged, which can be
especially useful in "cold-start" settings; and (3) the model admits simple
Bayesian inference via batch, as well as \emph{online} MCMC; the latter allows
scaling up even for \emph{dense} binary data (i.e., when the number of ones in
the tensor/network is also massive). In addition, non-negative factor matrices
in our model provide easy interpretability, and the tensor rank can be inferred
from the data. We evaluate our model on several large-scale real-world binary
tensors, achieving excellent computational scalability, and also demonstrate
its usefulness in leveraging side-information provided in form of
mode-network(s).