pwc-archive/papers-with-abstracts huggingface dataset:
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
Authors: Juergen Schmidhuber
Date: 2015-11-30 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL)

This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers.
Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
Authors: ZongYuan Ge, Alex Bewley, Christopher Mccool, Ben Upcroft, Peter Corke, Conrad Sanderson
Date: 2015-11-30 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification

We present a novel deep convolutional neural network (DCNN) system for
fine-grained image classification, called a mixture of DCNNs (MixDCNN). The
fine-grained image classification problem is characterised by large intra-class
variations and small inter-class variations. To overcome these problems our
proposed MixDCNN system partitions images into K subsets of similar images and
learns an expert DCNN for each subset. The output from each of the K DCNNs is
combined to form a single classification decision. In contrast to previous
techniques, we provide a formulation to perform joint end-to-end training of
the K DCNNs simultaneously. Extensive experiments, on three datasets using two
network structures (AlexNet and GoogLeNet), show that the proposed MixDCNN
system consistently outperforms other methods. It provides a relative
improvement of 12.7% and achieves state-of-the-art results on two datasets.
Category Enhanced Word Embedding
Authors: Chunting Zhou, Chonglin Sun, Zhiyuan Liu, Francis C. M. Lau
Date: 2015-11-27 00:00:00
Tasks: General Classification, Representation Learning, Sentiment Analysis, text-classification, Text Classification, Word Similarity

Distributed word representations have been demonstrated to be effective in
capturing semantic and syntactic regularities. Unsupervised representation
learning from large unlabeled corpora can learn similar representations for
those words that present similar co-occurrence statistics. Besides local
occurrence statistics, global topical information is also important knowledge
that may help discriminate a word from another. In this paper, we incorporate
category information of documents in the learning of word representations and
to learn the proposed models in a document-wise manner. Our models outperform
several state-of-the-art models in word analogy and word similarity tasks.
Moreover, we evaluate the learned word vectors on sentiment analysis and text
classification tasks, which shows the superiority of our learned word vectors.
We also learn high-quality category embeddings that reflect topical meanings.
A C-LSTM Neural Network for Text Classification
Authors: Chunting Zhou, Chonglin Sun, Zhiyuan Liu, Francis C. M. Lau
Date: 2015-11-27 00:00:00
Tasks: General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Text Classification

Neural network models have been demonstrated to be capable of achieving
remarkable performance in sentence and document modeling. Convolutional neural
network (CNN) and recurrent neural network (RNN) are two mainstream
architectures for such modeling tasks, which adopt totally different ways of
understanding natural languages. In this work, we combine the strengths of both
architectures and propose a novel and unified model called C-LSTM for sentence
representation and text classification. C-LSTM utilizes CNN to extract a
sequence of higher-level phrase representations, and are fed into a long
short-term memory recurrent neural network (LSTM) to obtain the sentence
representation. C-LSTM is able to capture both local features of phrases as
well as global and temporal sentence semantics. We evaluate the proposed
architecture on sentiment classification and question classification tasks. The
experimental results show that the C-LSTM outperforms both CNN and LSTM and can
achieve excellent performance on these tasks.
Recognizing Temporal Linguistic Expression Pattern of Individual with Suicide Risk on Social Media
Authors: Aiqi Zhang, Ang Li, Tingshao Zhu
Date: 2015-11-30 00:00:00
Tasks: Time Series, Time Series Analysis

Suicide is a global public health problem. Early detection of individual
suicide risk plays a key role in suicide prevention. In this paper, we propose
to look into individual suicide risk through time series analysis of personal
linguistic expression on social media (Weibo). We examined temporal patterns of
the linguistic expression of individuals on Chinese social media (Weibo). Then,
we used such temporal patterns as predictor variables to build classification
models for estimating levels of individual suicide risk. Characteristics of
time sequence curves to linguistic features including parentheses, auxiliary
verbs, personal pronouns and body words are reported to affect performance of
suicide most, and the predicting model has a accuracy higher than 0.60, shown
by the results. This paper confirms the efficiency of the social media data in
detecting individual suicide risk. Results of this study may be insightful for
improving the performance of suicide prevention programs.
ELM-Based Distributed Cooperative Learning Over Networks
Authors: Wu Ai, Weisheng Chen
Date: 2015-04-04 00:00:00
Tasks: Cloud Computing, Distributed Optimization

This paper investigates distributed cooperative learning algorithms for data
processing in a network setting. Specifically, the extreme learning machine
(ELM) is introduced to train a set of data distributed across several
components, and each component runs a program on a subset of the entire data.
In this scheme, there is no requirement for a fusion center in the network due
to e.g., practical limitations, security, or privacy reasons. We first
reformulate the centralized ELM training problem into a separable form among
nodes with consensus constraints. Then, we solve the equivalent problem using
distributed optimization tools. A new distributed cooperative learning
algorithm based on ELM, called DC-ELM, is proposed. The architecture of this
algorithm differs from that of some existing parallel/distributed ELMs based on
MapReduce or cloud computing. We also present an online version of the proposed
algorithm that can learn data sequentially in a one-by-one or chunk-by-chunk
mode. The novel algorithm is well suited for potential applications such as
artificial intelligence, computational biology, finance, wireless sensor
networks, and so on, involving datasets that are often extremely large,
high-dimensional and located on distributed data sources. We show simulation
results on both synthetic and real-world data sets.
Proximal gradient method for huberized support vector machine
Authors: Yangyang Xu, Ioannis Akrotirianakis, Amit Chakraborty
Date: 2015-11-30 00:00:00

The Support Vector Machine (SVM) has been used in a wide variety of
classification problems. The original SVM uses the hinge loss function, which
is non-differentiable and makes the problem difficult to solve in particular
for regularized SVMs, such as with $\ell_1$-regularization. This paper
considers the Huberized SVM (HSVM), which uses a differentiable approximation
of the hinge loss function. We first explore the use of the Proximal Gradient
(PG) method to solving binary-class HSVM (B-HSVM) and then generalize it to
multi-class HSVM (M-HSVM). Under strong convexity assumptions, we show that our
algorithm converges linearly. In addition, we give a finite convergence result
about the support of the solution, based on which we further accelerate the
algorithm by a two-stage method. We present extensive numerical experiments on
both synthetic and real datasets which demonstrate the superiority of our
methods over some state-of-the-art methods for both binary- and multi-class
SVMs.
Alternating direction method of multipliers for regularized multiclass support vector machines
Authors: Yangyang Xu, Ioannis Akrotirianakis, Amit Chakraborty
Date: 2015-11-30 00:00:00
Tasks: Second-order methods

The support vector machine (SVM) was originally designed for binary
classifications. A lot of effort has been put to generalize the binary SVM to
multiclass SVM (MSVM) which are more complex problems. Initially, MSVMs were
solved by considering their dual formulations which are quadratic programs and
can be solved by standard second-order methods. However, the duals of MSVMs
with regularizers are usually more difficult to formulate and computationally
very expensive to solve. This paper focuses on several regularized MSVMs and
extends the alternating direction method of multiplier (ADMM) to these MSVMs.
Using a splitting technique, all considered MSVMs are written as two-block
convex programs, for which the ADMM has global convergence guarantees.
Numerical experiments on synthetic and real data demonstrate the high
efficiency and accuracy of our algorithms.
Hierarchical Invariant Feature Learning with Marginalization for Person Re-Identification
Authors: Rahul Rama Varior, Gang Wang
Date: 2015-11-30 00:00:00
Tasks: Metric Learning, Person Re-Identification

This paper addresses the problem of matching pedestrians across multiple
camera views, known as person re-identification. Variations in lighting
conditions, environment and pose changes across camera views make
re-identification a challenging problem. Previous methods address these
challenges by designing specific features or by learning a distance function.
We propose a hierarchical feature learning framework that learns invariant
representations from labeled image pairs. A mapping is learned such that the
extracted features are invariant for images belonging to same individual across
views. To learn robust representations and to achieve better generalization to
unseen data, the system has to be trained with a large amount of data.
Critically, most of the person re-identification datasets are small. Manually
augmenting the dataset by partial corruption of input data introduces
additional computational burden as it requires several training epochs to
converge. We propose a hierarchical network which incorporates a
marginalization technique that can reap the benefits of training on large
datasets without explicit augmentation. We compare our approach with several
baseline algorithms as well as popular linear and non-linear metric learning
algorithms and demonstrate improved performance on challenging publicly
available datasets, VIPeR, CUHK01, CAVIAR4REID and iLIDS. Our approach also
achieves the stateof-the-art results on these datasets.
Clustering is Efficient for Approximate Maximum Inner Product Search
Authors: Alex Auvolat, Sarath Chandar, Pascal Vincent, Hugo Larochelle, Yoshua Bengio
Date: 2015-07-21 00:00:00
Tasks: Clustering, Recommendation Systems, Retrieval, Word Embeddings

Efficient Maximum Inner Product Search (MIPS) is an important task that has a
wide applicability in recommendation systems and classification with a large
number of classes. Solutions based on locality-sensitive hashing (LSH) as well
as tree-based solutions have been investigated in the recent literature, to
perform approximate MIPS in sublinear time. In this paper, we compare these to
another extremely simple approach for solving approximate MIPS, based on
variants of the k-means clustering algorithm. Specifically, we propose to train
a spherical k-means, after having reduced the MIPS problem to a Maximum Cosine
Similarity Search (MCSS). Experiments on two standard recommendation system
benchmarks as well as on large vocabulary word embeddings, show that this
simple approach yields much higher speedups, for the same retrieval precision,
than current state-of-the-art hashing-based and tree-based methods. This simple
method also yields more robust retrievals when the query is corrupted by noise.
Aspect-based Opinion Summarization with Convolutional Neural Networks
Authors: Haibing Wu, Yiwei Gu, Shangdi Sun, Xiaodong Gu
Date: 2015-11-30 00:00:00
Tasks: Aspect Extraction, Classification, General Classification, Opinion Summarization, Sentence, Sentiment Analysis, Sentiment Classification, Word Embeddings

This paper considers Aspect-based Opinion Summarization (AOS) of reviews on
particular products. To enable real applications, an AOS system needs to
address two core subtasks, aspect extraction and sentiment classification. Most
existing approaches to aspect extraction, which use linguistic analysis or
topic modeling, are general across different products but not precise enough or
suitable for particular products. Instead we take a less general but more
precise scheme, directly mapping each review sentence into pre-defined aspects.
To tackle aspect mapping and sentiment classification, we propose two
Convolutional Neural Network (CNN) based methods, cascaded CNN and multitask
CNN. Cascaded CNN contains two levels of convolutional networks. Multiple CNNs
at level 1 deal with aspect mapping task, and a single CNN at level 2 deals
with sentiment classification. Multitask CNN also contains multiple aspect CNNs
and a sentiment CNN, but different networks share the same word embeddings.
Experimental results indicate that both cascaded and multitask CNNs outperform
SVM-based methods by large margins. Multitask CNN generally performs better
than cascaded CNN.
Optimal prediction for sparse linear models? Lower bounds for coordinate-separable M-estimators
Authors: Yuchen Zhang, Martin J. Wainwright, Michael. I. Jordan
Date: 2015-03-11 00:00:00

For the problem of high-dimensional sparse linear regression, it is known
that an $\ell_0$-based estimator can achieve a $1/n$ "fast" rate on the
prediction error without any conditions on the design matrix, whereas in
absence of restrictive conditions on the design matrix, popular polynomial-time
methods only guarantee the $1/\sqrt{n}$ "slow" rate. In this paper, we show
that the slow rate is intrinsic to a broad class of M-estimators. In
particular, for estimators based on minimizing a least-squares cost function
together with a (possibly non-convex) coordinate-wise separable regularizer,
there is always a "bad" local optimum such that the associated prediction error
is lower bounded by a constant multiple of $1/\sqrt{n}$. For convex
regularizers, this lower bound applies to all global optima. The theory is
applicable to many popular estimators, including convex $\ell_1$-based methods
as well as M-estimators based on nonconvex regularizers, including the SCAD
penalty or the MCP regularizer. In addition, for a broad class of nonconvex
regularizers, we show that the bad local optima are very common, in that a
broad class of local minimization algorithms with random initialization will
typically converge to a bad solution.
Tracking Randomly Moving Objects on Edge Box Proposals
Authors: Gao Zhu, Fatih Porikli, Hongdong Li
Date: 2015-07-29 00:00:00
Tasks: Object, valid

Most tracking-by-detection methods employ a local search window around the
predicted object location in the current frame assuming the previous location
is accurate, the trajectory is smooth, and the computational capacity permits a
search radius that can accommodate the maximum speed yet small enough to reduce
mismatches. These, however, may not be valid always, in particular for fast and
irregularly moving objects. Here, we present an object tracker that is not
limited to a local search window and has ability to probe efficiently the
entire frame. Our method generates a small number of "high-quality" proposals
by a novel instance-specific objectness measure and evaluates them against the
object model that can be adopted from an existing tracking-by-detection
approach as a core tracker. During the tracking process, we update the object
model concentrating on hard false-positives supplied by the proposals, which
help suppressing distractors caused by difficult background clutters, and learn
how to re-rank proposals according to the object model. Since we reduce
significantly the number of hypotheses the core tracker evaluates, we can use
richer object descriptors and stronger detector. Our method outperforms most
recent state-of-the-art trackers on popular tracking benchmarks, and provides
improved robustness for fast moving objects as well as for ultra low-frame-rate
videos.
Exploring Models and Data for Image Question Answering
Authors: Mengye Ren, Ryan Kiros, Richard Zemel
Date: 2015-05-08 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Image Segmentation, object-detection, Object Detection, Question Answering, Question Generation, Question-Generation, Semantic Segmentation, Video Question Answering, Visual Question Answering (VQA)

This work aims to address the problem of image-based question-answering (QA)
with new models and datasets. In our work, we propose to use neural networks
and visual semantic embeddings, without intermediate stages such as object
detection and image segmentation, to predict answers to simple questions about
images. Our model performs 1.8 times better than the only published results on
an existing image QA dataset. We also present a question generation algorithm
that converts image descriptions, which are widely available, into QA form. We
used this algorithm to produce an order-of-magnitude larger dataset, with more
evenly distributed answers. A suite of baseline results on this new dataset are
also presented.
Machine Learning Sentiment Prediction based on Hybrid Document Representation
Authors: Panagiotis Stalidis, Maria Giatsoglou, Konstantinos Diamantaras, George Sarigiannidis, Konstantinos Ch. Chatzisavvas
Date: 2015-11-29 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Opinion Mining, Prediction, Sentiment Analysis

Automated sentiment analysis and opinion mining is a complex process
concerning the extraction of useful subjective information from text. The
explosion of user generated content on the Web, especially the fact that
millions of users, on a daily basis, express their opinions on products and
services to blogs, wikis, social networks, message boards, etc., render the
reliable, automated export of sentiments and opinions from unstructured text
crucial for several commercial applications. In this paper, we present a novel
hybrid vectorization approach for textual resources that combines a weighted
variant of the popular Word2Vec representation (based on Term Frequency-Inverse
Document Frequency) representation and with a Bag- of-Words representation and
a vector of lexicon-based sentiment values. The proposed text representation
approach is assessed through the application of several machine learning
classification algorithms on a dataset that is used extensively in literature
for sentiment detection. The classification accuracy derived through the
proposed hybrid vectorization approach is higher than when its individual
components are used for text represenation, and comparable with
state-of-the-art sentiment detection methodologies.
Position paper: a general framework for applying machine learning techniques in operating room
Authors: Filippo Maria Bianchi, Enrico De Santis, Hedieh Montazeri, Parisa Naraei, Alireza Sadeghian
Date: 2015-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Position

In this position paper we describe a general framework for applying machine
learning and pattern recognition techniques in healthcare. In particular, we
are interested in providing an automated tool for monitoring and incrementing
the level of awareness in the operating room and for identifying human errors
which occur during the laparoscopy surgical operation. The framework that we
present is divided in three different layers: each layer implements algorithms
which have an increasing level of complexity and which perform functionality
with an higher degree of abstraction. In the first layer, raw data collected
from sensors in the operating room during surgical operation, they are
pre-processed and aggregated. The results of this initial phase are transferred
to a second layer, which implements pattern recognition techniques and extract
relevant features from the data. Finally, in the last layer, expert systems are
employed to take high level decisions, which represent the final output of the
system.
Neural Network with Unbounded Activation Functions is Universal Approximator
Authors: Sho Sonoda, Noboru Murata
Date: 2015-05-14 00:00:00

This paper presents an investigation of the approximation property of neural
networks with unbounded activation functions, such as the rectified linear unit
(ReLU), which is the new de-facto standard of deep learning. The ReLU network
can be analyzed by the ridgelet transform with respect to Lizorkin
distributions. By showing three reconstruction formulas by using the Fourier
slice theorem, the Radon transform, and Parseval's relation, it is shown that a
neural network with unbounded activation functions still satisfies the
universal approximation property. As an additional consequence, the ridgelet
transform, or the backprojection filter in the Radon domain, is what the
network learns after backpropagation. Subject to a constructive admissibility
condition, the trained network can be obtained by simply discretizing the
ridgelet transform, without backpropagation. Numerical examples not only
support the consistency of the admissibility condition but also imply that some
non-admissible cases result in low-pass filtering.