pwc-archive/papers-with-abstracts huggingface dataset:
Wind ramp event prediction with parallelized Gradient Boosted Regression Trees
Authors: Saurav Gupta, Nitin Anand Shrivastava, Abbas Khosravi, Bijaya Ketan Panigrahi
Date: 2016-10-17 00:00:00
Tasks: Classification, General Classification, regression

Accurate prediction of wind ramp events is critical for ensuring the
reliability and stability of the power systems with high penetration of wind
energy. This paper proposes a classification based approach for estimating the
future class of wind ramp event based on certain thresholds. A parallelized
gradient boosted regression tree based technique has been proposed to
accurately classify the normal as well as rare extreme wind power ramp events.
The model has been validated using wind power data obtained from the National
Renewable Energy Laboratory database. Performance comparison with several
benchmark techniques indicates the superiority of the proposed technique in
terms of superior classification accuracy.
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Authors: Jiacheng Xu, Danlu Chen, Xipeng Qiu, Xuangjing Huang
Date: 2016-10-17 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Classification, Feature Engineering, General Classification, Sentiment Analysis, Sentiment Classification

Recently, neural networks have achieved great success on sentiment
classification due to their ability to alleviate feature engineering. However,
one of the remaining challenges is to model long texts in document-level
sentiment classification under a recurrent architecture because of the
deficiency of the memory unit. To address this problem, we present a Cached
Long Short-Term Memory neural networks (CLSTM) to capture the overall semantic
information in long texts. CLSTM introduces a cache mechanism, which divides
memory into several groups with different forgetting rates and thus enables the
network to keep sentiment information better within a recurrent unit. The
proposed CLSTM outperforms the state-of-the-art models on three publicly
available document-level sentiment analysis datasets.
Saliency Detection for Improving Object Proposals
Authors: Shuhan Chen, Jindong Li, Xuelong Hu, Ping Zhou
Date: 2016-03-14 00:00:00
Tasks: Object, object-detection, Object Detection, Saliency Detection

Object proposals greatly benefit object detection task in recent
state-of-the-art works. However, the existing object proposals usually have low
localization accuracy at high intersection over union threshold. To address it,
we apply saliency detection to each bounding box to improve their quality in
this paper. We first present a geodesic saliency detection method in contour,
which is designed to find closed contours. Then, we apply it to each candidate
box with multi-sizes, and refined boxes can be easily produced in the obtained
saliency maps which are further used to calculate saliency scores for proposal
ranking. Experiments on PASCAL VOC 2007 test dataset demonstrate the proposed
refinement approach can greatly improve existing models.
Multiple Instance Fuzzy Inference Neural Networks
Authors: Amine Ben Khalifa, Hichem Frigui
Date: 2016-10-17 00:00:00
Tasks: Landmine, Multiple Instance Learning

Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements
imprecision, and vagueness. However, there is another type of vagueness that
arises when data have multiple forms of expression that fuzzy logic does not
address quite well. This is the case for multiple instance learning problems
(MIL). In MIL, an object is represented by a collection of instances, called a
bag. A bag is labeled negative if all of its instances are negative, and
positive if at least one of its instances is positive. Positive bags encode
ambiguity since the instances themselves are not labeled. In this paper, we
introduce fuzzy inference systems and neural networks designed to handle bags
of instances as input and capable of learning from ambiguously labeled data.
First, we introduce the Multiple Instance Sugeno style fuzzy inference
(MI-Sugeno) that extends the standard Sugeno style inference to handle
reasoning with multiple instances. Second, we use MI-Sugeno to define and
develop Multiple Instance Adaptive Neuro Fuzzy Inference System (MI-ANFIS). We
expand the architecture of the standard ANFIS to allow reasoning with bags and
derive a learning algorithm using backpropagation to identify the premise and
consequent parameters of the network. The proposed inference system is tested
and validated using synthetic and benchmark datasets suitable for MIL problems.
We also apply the proposed MI-ANFIS to fuse the output of multiple
discrimination algorithms for the purpose of landmine detection using Ground
Penetrating Radar.
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization
Authors: Ohad Shamir
Date: 2016-03-02 00:00:00
Proceeding: NeurIPS 2016
Tasks: Distributed Optimization, Learning Theory, Stochastic Optimization, Transductive Learning

Stochastic gradient methods for machine learning and optimization problems
are usually analyzed assuming data points are sampled \emph{with} replacement.
In practice, however, sampling \emph{without} replacement is very common,
easier to implement in many cases, and often performs better. In this paper, we
provide competitive convergence guarantees for without-replacement sampling,
under various scenarios, for three types of algorithms: Any algorithm with
online regret guarantees, stochastic gradient descent, and SVRG. A useful
application of our SVRG analysis is a nearly-optimal algorithm for regularized
least squares in a distributed setting, in terms of both communication
complexity and runtime complexity, when the data is randomly partitioned and
the condition number can be as large as the data size per machine (up to
logarithmic factors). Our proof techniques combine ideas from stochastic
optimization, adversarial online learning, and transductive learning theory,
and can potentially be applied to other stochastic optimization and learning
problems.
Improvements in Sub-optimal Solving of the $(N^2-1)$-Puzzle via Joint Relocation of Pebbles and its Applications to Rule-based Cooperative Path-Finding
Authors: Pavel Surynek, Petr Michalík
Date: 2016-10-17 00:00:00

The problem of solving $(n^2-1)$-puzzle and cooperative path-finding (CPF)
sub-optimally by rule based algorithms is addressed in this manuscript. The
task in the puzzle is to rearrange $n^2-1$ pebbles on the square grid of the
size of n x n using one vacant position to a desired goal configuration. An
improvement to the existent polynomial-time algorithm is proposed and
experimentally analyzed. The improved algorithm is trying to move pebbles in a
more efficient way than the original algorithm by grouping them into so-called
snakes and moving them jointly within the snake. An experimental evaluation
showed that the algorithm using snakes produces solutions that are 8% to 9%
shorter than solutions generated by the original algorithm.
  The snake-based relocation has been also integrated into rule-based
algorithms for solving the CPF problem sub-optimally, which is a closely
related task. The task in CPF is to relocate a group of abstract robots that
move over an undirected graph to given goal vertices. Robots can move to
unoccupied neighboring vertices and at most one robot can be placed in each
vertex. The $(n^2-1)$-puzzle is a special case of CPF where the underlying
graph is represented by a 4-connected grid and there is only one vacant vertex.
Two major rule-based algorithms for CPF were included in our study - BIBOX and
PUSH-and-SWAP (PUSH-and-ROTATE). Improvements gained by using snakes in the
BIBOX algorithm were stable around 30% in $(n^2-1)$-puzzle solving and up to
50% in CPFs over bi-connected graphs with various ear decompositions and
multiple vacant vertices. In the case of the PUSH-and-SWAP algorithm the
improvement achieved by snakes was around 5% to 8%. However, the improvement
was unstable and hardly predictable in the case of PUSH-and-SWAP.
Automatic Subspace Learning via Principal Coefficients Embedding
Authors: Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan
Date: 2014-11-17 00:00:00

In this paper, we address two challenging problems in unsupervised subspace
learning: 1) how to automatically identify the feature dimension of the learned
subspace (i.e., automatic subspace learning), and 2) how to learn the
underlying subspace in the presence of Gaussian noise (i.e., robust subspace
learning). We show that these two problems can be simultaneously solved by
proposing a new method (called principal coefficients embedding, PCE). For a
given data set $\mathbf{D}\in \mathds{R}^{m\times n}$, PCE recovers a clean
data set $\mathbf{D}_{0}\in \mathds{R}^{m\times n}$ from $\mathbf{D}$ and
simultaneously learns a global reconstruction relation $\mathbf{C}\in
\mathbf{R}^{n\times n}$ of $\mathbf{D}_{0}$. By preserving $\mathbf{C}$ into an
$m^{\prime}$-dimensional space, the proposed method obtains a projection matrix
that can capture the latent manifold structure of $\mathbf{D}_{0}$, where
$m^{\prime}\ll m$ is automatically determined by the rank of $\mathbf{C}$ with
theoretical guarantees. PCE has three advantages: 1) it can automatically
determine the feature dimension even though data are sampled from a union of
multiple linear subspaces in presence of the Gaussian noise, 2) Although the
objective function of PCE only considers the Gaussian noise, experimental
results show that it is robust to the non-Gaussian noise (\textit{e.g.}, random
pixel corruption) and real disguises, 3) Our method has a closed-form solution
and can be calculated very fast. Extensive experimental results show the
superiority of PCE on a range of databases with respect to the classification
accuracy, robustness and efficiency.
Connections Between Nuclear Norm and Frobenius Norm Based Representations
Authors: Xi Peng, Can-Yi Lu, Zhang Yi, Huajin Tang
Date: 2015-02-26 00:00:00
Tasks: Clustering

A lot of works have shown that frobenius-norm based representation (FNR) is
competitive to sparse representation and nuclear-norm based representation
(NNR) in numerous tasks such as subspace clustering. Despite the success of FNR
in experimental studies, less theoretical analysis is provided to understand
its working mechanism. In this paper, we fill this gap by building the
theoretical connections between FNR and NNR. More specially, we prove that: 1)
when the dictionary can provide enough representative capacity, FNR is exactly
NNR even though the data set contains the Gaussian noise, Laplacian noise, or
sample-specified corruption, 2) otherwise, FNR and NNR are two solutions on the
column space of the dictionary.
What is the Best Way for Extracting Meaningful Attributes from Pictures?
Authors: Liangchen Liu, Arnold Wiliem, Shaokang Chen, Brian C. Lovell
Date: 2016-10-17 00:00:00
Tasks: Attribute

Automatic attribute discovery methods have gained in popularity to extract
sets of visual attributes from images or videos for various tasks. Despite
their good performance in some classification tasks, it is difficult to
evaluate whether the attributes discovered by these methods are meaningful and
which methods are the most appropriate to discover attributes for visual
descriptions. In its simplest form, such an evaluation can be performed by
manually verifying whether there is any consistent identifiable visual concept
distinguishing between positive and negative exemplars labelled by an
attribute. This manual checking is tedious, expensive and labour intensive. In
addition, comparisons between different methods could also be problematic as it
is not clear how one could quantitatively decide which attribute is more
meaningful than the others. In this paper, we propose a novel attribute
meaningfulness metric to address this challenging problem. With this metric,
automatic quantitative evaluation can be performed on the attribute sets; thus,
reducing the enormous effort to perform manual evaluation. The proposed metric
is applied to some recent automatic attribute discovery and hashing methods on
four attribute-labelled datasets. To further validate the efficacy of the
proposed method, we conducted a user study. In addition, we also compared our
metric with a semi-supervised attribute discover method using the mixture of
probabilistic PCA. In our evaluation, we gleaned several insights that could be
beneficial in developing new automatic attribute discovery methods.
Partial Procedural Geometric Model Fitting for Point Clouds
Authors: Zongliang Zhang, Jonathan Li, Yulan Guo, Yangbin Lin, Ming Cheng, Cheng Wang
Date: 2016-10-17 00:00:00
Tasks: model

Geometric model fitting is a fundamental task in computer graphics and
computer vision. However, most geometric model fitting methods are unable to
fit an arbitrary geometric model (e.g. a surface with holes) to incomplete
data, due to that the similarity metrics used in these methods are unable to
measure the rigid partial similarity between arbitrary models. This paper hence
proposes a novel rigid geometric similarity metric, which is able to measure
both the full similarity and the partial similarity between arbitrary geometric
models. The proposed metric enables us to perform partial procedural geometric
model fitting (PPGMF). The task of PPGMF is to search a procedural geometric
model space for the model rigidly similar to a query of non-complete point set.
Models in the procedural model space are generated according to a set of
parametric modeling rules. A typical query is a point cloud. PPGMF is very
useful as it can be used to fit arbitrary geometric models to non-complete
(incomplete, over-complete or hybrid-complete) point cloud data. For example,
most laser scanning data is non-complete due to occlusion. Our PPGMF method
uses Markov chain Monte Carlo technique to optimize the proposed similarity
metric over the model space. To accelerate the optimization process, the method
also employs a novel coarse-to-fine model dividing strategy to reject
dissimilar models in advance. Our method has been demonstrated on a variety of
geometric models and non-complete data. Experimental results show that the
PPGMF method based on the proposed metric is able to fit non-complete data,
while the method based on other metrics is unable. It is also shown that our
method can be accelerated by several times via early rejection.
Probabilistic Dimensionality Reduction via Structure Learning
Authors: Li Wang
Date: 2016-10-16 00:00:00
Tasks: Clustering, Data Visualization, Dimensionality Reduction, scientific discovery

We propose a novel probabilistic dimensionality reduction framework that can
naturally integrate the generative model and the locality information of data.
Based on this framework, we present a new model, which is able to learn a
smooth skeleton of embedding points in a low-dimensional space from
high-dimensional noisy data. The formulation of the new model can be
equivalently interpreted as two coupled learning problem, i.e., structure
learning and the learning of projection matrix. This interpretation motivates
the learning of the embedding points that can directly form an explicit graph
structure. We develop a new method to learn the embedding points that form a
spanning tree, which is further extended to obtain a discriminative and compact
feature representation for clustering problems. Unlike traditional clustering
methods, we assume that centers of clusters should be close to each other if
they are connected in a learned graph, and other cluster centers should be
distant. This can greatly facilitate data visualization and scientific
discovery in downstream analysis. Extensive experiments are performed that
demonstrate that the proposed framework is able to obtain discriminative
feature representations, and correctly recover the intrinsic structures of
various real-world datasets.
Stochastic Variance-Reduced ADMM
Authors: Shuai Zheng, James T. Kwok
Date: 2016-04-24 00:00:00

The alternating direction method of multipliers (ADMM) is a powerful
optimization solver in machine learning. Recently, stochastic ADMM has been
integrated with variance reduction methods for stochastic gradient, leading to
SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration
complexities. However, their space requirements can still be high. In this
paper, we propose an integration of ADMM with the method of stochastic variance
reduced gradient (SVRG). Unlike another recent integration attempt called
SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of
SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage
requirement is very low, even independent of the sample size $n$. We also
extend the proposed method for nonconvex problems, and obtain a convergence
rate of $O(1/T)$. Experimental results demonstrate that it is as fast as
SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much
bigger data sets.
Introduzione all'Intelligenza Artificiale
Authors: Fabrizio Riguzzi
Date: 2015-11-13 00:00:00

The paper presents an introduction to Artificial Intelligence (AI) in an accessible and informal but precise form. The paper focuses on the algorithmic aspects of the discipline, presenting the main techniques used in AI systems groped in symbolic and subsymbolic. The last part of the paper is devoted to the discussion ongoing among experts in the field and the public at large about on the advantages and disadvantages of AI and in particular on the possible dangers. The personal opinion of the author on this subject concludes the paper. -- -- L'articolo presenta un'introduzione all'Intelligenza Artificiale (IA) in forma divulgativa e informale ma precisa. L'articolo affronta prevalentemente gli aspetti informatici della disciplina, presentando le principali tecniche usate nei sistemi di IA divise in simboliche e subsimboliche. L'ultima parte dell'articolo presenta il dibattito in corso tra gli esperi e il pubblico su vantaggi e svantaggi dell'IA e in particolare sui possibili pericoli. L'articolo termina con l'opinione dell'autore al riguardo.
Real-time Joint Tracking of a Hand Manipulating an Object from RGB-D Input
Authors: Srinath Sridhar, Franziska Mueller, Michael Zollhöfer, Dan Casas, Antti Oulasvirta, Christian Theobalt
Date: 2016-10-16 00:00:00
Tasks: Object, Object Tracking

Real-time simultaneous tracking of hands manipulating and interacting with
external objects has many potential applications in augmented reality, tangible
computing, and wearable computing. However, due to difficult occlusions, fast
motions, and uniform hand appearance, jointly tracking hand and object pose is
more challenging than tracking either of the two separately. Many previous
approaches resort to complex multi-camera setups to remedy the occlusion
problem and often employ expensive segmentation and optimization steps which
makes real-time tracking impossible. In this paper, we propose a real-time
solution that uses a single commodity RGB-D camera. The core of our approach is
a 3D articulated Gaussian mixture alignment strategy tailored to hand-object
tracking that allows fast pose optimization. The alignment energy uses novel
regularizers to address occlusions and hand-object contacts. For added
robustness, we guide the optimization with discriminative part classification
of the hand and segmentation of the object. We conducted extensive experiments
on several existing datasets and introduce a new annotated hand-object dataset.
Quantitative and qualitative results show the key advantages of our method:
speed, accuracy, and robustness.
Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM
Authors: Bodhisattwa Prasad Majumder, Ayan Sengupta, Sajal jain, Parikshit Bhaduri
Date: 2016-10-16 00:00:00
Tasks: Community Detection, Fault Detection

With the advancement of huge data generation and data handling capability,
Machine Learning and Probabilistic modelling enables an immense opportunity to
employ predictive analytics platform in high security critical industries
namely data centers, electricity grids, utilities, airport etc. where downtime
minimization is one of the primary objectives. This paper proposes a novel,
complete architecture of an intelligent predictive analytics platform, Fault
Engine, for huge device network connected with electrical/information flow.
Three unique modules, here proposed, seamlessly integrate with available
technology stack of data handling and connect with middleware to produce online
intelligent prediction in critical failure scenarios. The Markov Failure module
predicts the severity of a failure along with survival probability of a device
at any given instances. The Root Cause Analysis model indicates probable
devices as potential root cause employing Bayesian probability assignment and
topological sort. Finally, a community detection algorithm produces correlated
clusters of device in terms of failure probability which will further narrow
down the search space of finding route cause. The whole Engine has been tested
with different size of network with simulated failure environments and shows
its potential to be scalable in real-time implementation.
Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach
Authors: Lijun Zhang, Tianbao Yang, Rong Jin, Zhi-Hua Zhou
Date: 2015-11-12 00:00:00
Tasks: Sparse Learning

In this paper, we develop a randomized algorithm and theory for learning a
sparse model from large-scale and high-dimensional data, which is usually
formulated as an empirical risk minimization problem with a sparsity-inducing
regularizer. Under the assumption that there exists a (approximately) sparse
solution with high classification accuracy, we argue that the dual solution is
also sparse or approximately sparse. The fact that both primal and dual
solutions are sparse motivates us to develop a randomized approach for a
general convex-concave optimization problem. Specifically, the proposed
approach combines the strength of random projection with that of sparse
learning: it utilizes random projection to reduce the dimensionality, and
introduces $\ell_1$-norm regularization to alleviate the approximation error
caused by random projection. Theoretical analysis shows that under favored
conditions, the randomized algorithm can accurately recover the optimal
solutions to the convex-concave optimization problem (i.e., recover both the
primal and dual solutions).
A Deep Bag-of-Features Model for Music Auto-Tagging
Authors: Juhan Nam, Jorge Herrera, Kyogu Lee
Date: 2015-08-20 00:00:00
Tasks: Audio Classification, Information Retrieval, Music Auto-Tagging, Music Information Retrieval, Retrieval, TAG

Feature learning and deep learning have drawn great attention in recent years
as a way of transforming input data into more effective representations using
learning algorithms. Such interest has grown in the area of music information
retrieval (MIR) as well, particularly in music audio classification tasks such
as auto-tagging. In this paper, we present a two-stage learning model to
effectively predict multiple labels from music audio. The first stage learns to
project local spectral patterns of an audio track onto a high-dimensional
sparse space in an unsupervised manner and summarizes the audio track as a
bag-of-features. The second stage successively performs the unsupervised
learning on the bag-of-features in a layer-by-layer manner to initialize a deep
neural network and finally fine-tunes it with the tag labels. Through the
experiment, we rigorously examine training choices and tuning parameters, and
show that the model achieves high performance on Magnatagatune, a popularly
used dataset in music auto-tagging.