pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Classification in High Energy Physics
Authors: Lucio Mwinmaarong Dery, Benjamin Nachman, Francesco Rubbo, Ariel Schwartzman
Date: 2017-02-01 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, General Classification, Vocal Bursts Intensity Prediction, Weakly Supervised Classification

As machine learning algorithms become increasingly sophisticated to exploit
subtle features of the data, they often become more dependent on simulations.
This paper presents a new approach called weakly supervised classification in
which class proportions are the only input into the machine learning algorithm.
Using one of the most challenging binary classification tasks in high energy
physics - quark versus gluon tagging - we show that weakly supervised
classification can match the performance of fully supervised algorithms.
Furthermore, by design, the new algorithm is insensitive to any mis-modeling of
discriminating features in the data by the simulation. Weakly supervised
classification is a general procedure that can be applied to a wide variety of
learning problems to boost performance and robustness when detailed simulations
are not reliable or not available.
Less than a Single Pass: Stochastically Controlled Stochastic Gradient Method
Authors: Lihua Lei, Michael. I. Jordan
Date: 2016-09-12 00:00:00

We develop and analyze a procedure for gradient-based optimization that we refer to as stochastically controlled stochastic gradient (SCSG). As a member of the SVRG family of algorithms, SCSG makes use of gradient estimates at two scales, with the number of updates at the faster scale being governed by a geometric random variable. Unlike most existing algorithms in this family, both the computation cost and the communication cost of SCSG do not necessarily scale linearly with the sample size $n$; indeed, these costs are independent of $n$ when the target accuracy is low. An experimental evaluation on real datasets confirms the effectiveness of SCSG.
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing
Authors: Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, Tarek Abdelzaher
Date: 2016-11-07 00:00:00
Tasks: Activity Recognition, General Classification, Human Activity Recognition, Question Answering, Time Series, Time Series Analysis, User Identification

Mobile sensing applications usually require time-series inputs from sensors.
Some applications, such as tracking, can use sensed acceleration and rate of
rotation to calculate displacement based on physical system models. Other
applications, such as activity recognition, extract manually designed features
from sensor inputs for classification. Such applications face two challenges.
On one hand, on-device sensor measurements are noisy. For many mobile
applications, it is hard to find a distribution that exactly describes the
noise in practice. Unfortunately, calculating target quantities based on
physical system and noise models is only as accurate as the noise assumptions.
Similarly, in classification applications, although manually designed features
have proven to be effective, it is not always straightforward to find the most
robust features to accommodate diverse sensor noise patterns and user
behaviors. To this end, we propose DeepSense, a deep learning framework that
directly addresses the aforementioned noise and feature customization
challenges in a unified manner. DeepSense integrates convolutional and
recurrent neural networks to exploit local interactions among similar mobile
sensors, merge local interactions of different sensory modalities into global
interactions, and extract temporal relationships to model signal dynamics.
DeepSense thus provides a general signal estimation and classification
framework that accommodates a wide range of applications. We demonstrate the
effectiveness of DeepSense using three representative and challenging tasks:
car tracking with motion sensors, heterogeneous human activity recognition, and
user identification with biometric motion analysis. DeepSense significantly
outperforms the state-of-the-art methods for all three tasks. In addition,
DeepSense is feasible to implement on smartphones due to its moderate energy
consumption and low latency
Evaluating Ising Processing Units with Integer Programming
Authors: Carleton Coffrin, Harsha Nagarajan, Russell Bent
Date: 2017-07-02 00:00:00
Tasks: Benchmarking

The recent emergence of novel computational devices, such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators, present new opportunities for hybrid-optimization algorithms that are hardware accelerated by these devices. In this work, we propose the idea of an Ising processing unit as a computational abstraction for reasoning about these emerging devices. The challenges involved in using and benchmarking these devices are presented and commercial mixed integer programming solvers are proposed as a valuable tool for the validation of these disparate hardware platforms. The proposed validation methodology is demonstrated on a D-Wave 2X adiabatic quantum computer, one example of an Ising processing unit. The computational results demonstrate that the D-Wave hardware consistently produces high-quality solutions and suggests that as IPU technology matures it could become a valuable co-processor in hybrid-optimization algorithms.
Efficient Learning of Mixed Membership Models
Authors: Zilong Tan, Sayan Mukherjee
Date: 2017-02-25 00:00:00

We present an efficient algorithm for learning mixed membership models when
the number of variables $p$ is much larger than the number of hidden components
$k$. This algorithm reduces the computational complexity of state-of-the-art
tensor methods, which require decomposing an $O\left(p^3\right)$ tensor, to
factorizing $O\left(p/k\right)$ sub-tensors each of size $O\left(k^3\right)$.
In addition, we address the issue of negative entries in the empirical method
of moments based estimators. We provide sufficient conditions under which our
approach has provable guarantees. Our approach obtains competitive empirical
results on both simulated and real data.
Dimensionality reduction with missing values imputation
Authors: Rania Mkhinini Gahar, Olfa Arfaoui, Minyar Sassi Hidri, Nejib Ben-Hadj Alouane
Date: 2017-07-02 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Imputation, Missing Values

In this study, we propose a new statical approach for high-dimensionality
reduction of heterogenous data that limits the curse of dimensionality and
deals with missing values. To handle these latter, we propose to use the Random
Forest imputation's method. The main purpose here is to extract useful
information and so reducing the search space to facilitate the data exploration
process. Several illustrative numeric examples, using data coming from publicly
available machine learning repositories are also included. The experimental
component of the study shows the efficiency of the proposed analytical
approach.
Generalization Error of Invariant Classifiers
Authors: Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues
Date: 2016-10-14 00:00:00

This paper studies the generalization error of invariant classifiers. In
particular, we consider the common scenario where the classification task is
invariant to certain transformations of the input, and that the classifier is
constructed (or learned) to be invariant to these transformations. Our approach
relies on factoring the input space into a product of a base space and a set of
transformations. We show that whereas the generalization error of a
non-invariant classifier is proportional to the complexity of the input space,
the generalization error of an invariant classifier is proportional to the
complexity of the base space. We also derive a set of sufficient conditions on
the geometry of the base space and the set of transformations that ensure that
the complexity of the base space is much smaller than the complexity of the
input space. Our analysis applies to general classifiers such as convolutional
neural networks. We demonstrate the implications of the developed theory for
such classifiers with experiments on the MNIST and CIFAR-10 datasets.
Spatially Adaptive Computation Time for Residual Networks
Authors: Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, Computational Efficiency, General Classification, image-classification, Image Classification, Image Segmentation, Object, object-detection, Object Detection, Semantic Segmentation

This paper proposes a deep learning architecture based on Residual Network
that dynamically adjusts the number of executed layers for the regions of the
image. This architecture is end-to-end trainable, deterministic and
problem-agnostic. It is therefore applicable without any modifications to a
wide range of computer vision problems such as image classification, object
detection and image segmentation. We present experimental results showing that
this model improves the computational efficiency of Residual Networks on the
challenging ImageNet classification and COCO object detection datasets.
Additionally, we evaluate the computation time maps on the visual saliency
dataset cat2000 and find that they correlate surprisingly well with human eye
fixation positions.
Topic supervised non-negative matrix factorization
Authors: Kelsey MacMillan, James D. Wilson
Date: 2017-06-12 00:00:00
Tasks: Topic Models

Topic models have been extensively used to organize and interpret the
contents of large, unstructured corpora of text documents. Although topic
models often perform well on traditional training vs. test set evaluations, it
is often the case that the results of a topic model do not align with human
interpretation. This interpretability fallacy is largely due to the
unsupervised nature of topic models, which prohibits any user guidance on the
results of a model. In this paper, we introduce a semi-supervised method called
topic supervised non-negative matrix factorization (TS-NMF) that enables the
user to provide labeled example documents to promote the discovery of more
meaningful semantic structure of a corpus. In this way, the results of TS-NMF
better match the intuition and desired labeling of the user. The core of TS-NMF
relies on solving a non-convex optimization problem for which we derive an
iterative algorithm that is shown to be monotonic and convergent to a local
optimum. We demonstrate the practical utility of TS-NMF on the Reuters and
PubMed corpora, and find that TS-NMF is especially useful for conceptual or
broad topics, where topic key terms are not well understood. Although
identifying an optimal latent structure for the data is not a primary objective
of the proposed approach, we find that TS-NMF achieves higher weighted Jaccard
similarity scores than the contemporary methods, (unsupervised) NMF and latent
Dirichlet allocation, at supervision rates as low as 10% to 20%.
Variance Regularizing Adversarial Learning
Authors: Karan Grewal, R. Devon Hjelm, Yoshua Bengio
Date: 2017-07-02 00:00:00
Proceeding: ICLR 2018 1

We introduce a novel approach for training adversarial models by replacing
the discriminator score with a bi-modal Gaussian distribution over the
real/fake indicator variables. In order to do this, we train the Gaussian
classifier to match the target bi-modal distribution implicitly through
meta-adversarial training. We hypothesize that this approach ensures a non-zero
gradient to the generator, even in the limit of a perfect classifier. We test
our method against standard benchmark image datasets as well as show the
classifier output distribution is smooth and has overlap between the real and
fake modes.
Grammatical Error Correction with Neural Reinforcement Learning
Authors: Keisuke Sakaguchi, Matt Post, Benjamin Van Durme
Date: 2017-07-02 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Grammatical Error Correction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence

We propose a neural encoder-decoder model with reinforcement learning (NRL)
for grammatical error correction (GEC). Unlike conventional maximum likelihood
estimation (MLE), the model directly optimizes towards an objective that
considers a sentence-level, task-specific evaluation metric, avoiding the
exposure bias issue in MLE. We demonstrate that NRL outperforms MLE both in
human and automated evaluation metrics, achieving the state-of-the-art on a
fluency-oriented GEC corpus.
Classification non supervisée des données hétérogènes à large échelle
Authors: Mohamed Ali Zoghlami, Olfa Arfaoui, Minyar Sassi Hidri, Rahma Ben Ayed
Date: 2017-07-02 00:00:00
Tasks: Classification, Clustering, Descriptive, General Classification

When it comes to cluster massive data, response time, disk access and quality
of formed classes becoming major issues for companies. It is in this context
that we have come to define a clustering framework for large scale
heterogeneous data that contributes to the resolution of these issues. The
proposed framework is based on, firstly, the descriptive analysis based on MCA,
and secondly, the MapReduce paradigm in a large scale environment. The results
are encouraging and prove the efficiency of the hybrid deployment on response
quality and time component as on qualitative and quantitative data.
A Batch-Incremental Video Background Estimation Model using Weighted Low-Rank Approximation of Matrices
Authors: Aritra Dutta, Xin Li, Peter Richtárik
Date: 2017-07-02 00:00:00

Principal component pursuit (PCP) is a state-of-the-art approach for
background estimation problems. Due to their higher computational cost, PCP
algorithms, such as robust principal component analysis (RPCA) and its
variants, are not feasible in processing high definition videos. To avoid the
curse of dimensionality in those algorithms, several methods have been proposed
to solve the background estimation problem in an incremental manner. We propose
a batch-incremental background estimation model using a special weighted
low-rank approximation of matrices. Through experiments with real and synthetic
video sequences, we demonstrate that our method is superior to the
state-of-the-art background estimation algorithms such as GRASTA, ReProCS,
incPCP, and GFL.
Location Dependent Dirichlet Processes
Authors: Shiliang Sun, John Paisley, Qiuyang Liu
Date: 2017-07-02 00:00:00
Tasks: Gaussian Processes, Image Segmentation, Semantic Segmentation, Variational Inference

Dirichlet processes (DP) are widely applied in Bayesian nonparametric
modeling. However, in their basic form they do not directly integrate
dependency information among data arising from space and time. In this paper,
we propose location dependent Dirichlet processes (LDDP) which incorporate
nonparametric Gaussian processes in the DP modeling framework to model such
dependencies. We develop the LDDP in the context of mixture modeling, and
develop a mean field variational inference algorithm for this mixture model.
The effectiveness of the proposed modeling framework is shown on an image
segmentation task.
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Authors: Xinchi Chen, Xipeng Qiu, Xuanjing Huang
Date: 2016-11-16 00:00:00
Tasks: Chinese Word Segmentation, Feature Engineering, Part-Of-Speech Tagging

Recently, neural network models for natural language processing tasks have
been increasingly focused on for their ability of alleviating the burden of
manual feature engineering. However, the previous neural models cannot extract
the complicated feature compositions as the traditional methods with discrete
features. In this work, we propose a feature-enriched neural model for joint
Chinese word segmentation and part-of-speech tagging task. Specifically, to
simulate the feature templates of traditional discrete feature based models, we
use different filters to model the complex compositional features with
convolutional and pooling layer, and then utilize long distance dependency
information with recurrent layer. Experimental results on five different
datasets show the effectiveness of our proposed model.
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Authors: Abhijit Guha Roy, Sailesh Conjeti, Debdoot Sheet, Amin Katouzian, Nassir Navab, Christian Wachinger
Date: 2017-05-02 00:00:00
Tasks: Brain Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on initially unlabeled data with existing tools and to use them for
pre-training. For the subsequent fine-tuning of the network with manually
labeled data, we introduce error corrective boosting (ECB), which emphasizes
parameter updates on classes with lower accuracy. Furthermore, we introduce
SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that
combines skip connections with the unpooling strategy for upsampling. The
SD-Net addresses challenges of severe class imbalance and errors along
boundaries. With application to whole-brain MRI T1 scan segmentation, we
generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on
two datasets with manual annotations. Our results show that the inclusion of
auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D
scan in 7 secs in comparison to 30 hours for the closest multi-atlas
segmentation method, while reaching similar performance. It also outperforms
the latest state-of-the-art F-CNN models.
Where to Play: Retrieval of Video Segments using Natural-Language Queries
Authors: Sangkuk Lee, Daesik Kim, Myunggi Lee, Jihye Hwang, Nojun Kwak
Date: 2017-07-02 00:00:00
Tasks: Image Captioning, Natural Language Queries, Retrieval, Text Generation

In this paper, we propose a new approach for retrieval of video segments
using natural language queries. Unlike most previous approaches such as
concept-based methods or rule-based structured models, the proposed method uses
image captioning model to construct sentential queries for visual information.
In detail, our approach exploits multiple captions generated by visual features
in each image with `Densecap'. Then, the similarities between captions of
adjacent images are calculated, which is used to track semantically similar
captions over multiple frames. Besides introducing this novel idea of 'tracking
by captioning', the proposed method is one of the first approaches that uses a
language generation model learned by neural networks to construct semantic
query describing the relations and properties of visual information. To
evaluate the effectiveness of our approach, we have created a new evaluation
dataset, which contains about 348 segments of scenes in 20 movie-trailers.
Through quantitative and qualitative evaluation, we show that our method is
effective for retrieval of video segments using natural language queries.
DAG-based Long Short-Term Memory for Neural Word Segmentation
Authors: Xinchi Chen, Zhan Shi, Xipeng Qiu, Xuanjing Huang
Date: 2017-07-02 00:00:00
Tasks: Chinese Word Segmentation, Feature Engineering, Segmentation, Word Embeddings

Neural word segmentation has attracted more and more research interests for
its ability to alleviate the effort of feature engineering and utilize the
external resource by the pre-trained character or word embeddings. In this
paper, we propose a new neural model to incorporate the word-level information
for Chinese word segmentation. Unlike the previous word-based models, our model
still adopts the framework of character-based sequence labeling, which has
advantages on both effectiveness and efficiency at the inference stage. To
utilize the word-level information, we also propose a new long short-term
memory (LSTM) architecture over directed acyclic graph (DAG). Experimental
results demonstrate that our model leads to better performances than the
baseline models.
Deep-learning-based data page classification for holographic memory
Authors: Tomoyoshi Shimobaba, Naoki Kuwata, Mizuha Homma, Takayuki Takahashi, Yuki Nagahama, Marie Sano, Satoki Hasegawa, Ryuji Hirayama, Takashi Kakue, Atsushi Shiraki, Naoki Takada, Tomoyoshi Ito
Date: 2017-07-02 00:00:00
Tasks: Classification, Deep Learning, General Classification

We propose a deep-learning-based classification of data pages used in
holographic memory. We numerically investigated the classification performance
of a conventional multi-layer perceptron (MLP) and a deep neural network, under
the condition that reconstructed page data are contaminated by some noise and
are randomly laterally shifted. The MLP was found to have a classification
accuracy of 91.58%, whereas the deep neural network was able to classify data
pages at an accuracy of 99.98%. The accuracy of the deep neural network is two
orders of magnitude better than the MLP.
A Spatio-Temporal Representation for the Orienteering Problem with Time-Varying Profits
Authors: Zhibei Ma, Kai Yin, Lantao Liu, Gaurav S. Sukhatme
Date: 2016-11-24 00:00:00

We consider an orienteering problem (OP) where an agent needs to visit a
series (possibly a subset) of depots, from which the maximal accumulated
profits are desired within given limited time budget. Different from most
existing works where the profits are assumed to be static, in this work we
investigate a variant that has arbitrary time-dependent profits. Specifically,
the profits to be collected change over time and they follow different (e.g.,
independent) time-varying functions. The problem is of inherent nonlinearity
and difficult to solve by existing methods. To tackle the challenge, we present
a simple and effective framework that incorporates time-variations into the
fundamental planning process. Specifically, we propose a deterministic
spatio-temporal representation where both spatial description and temporal
logic are unified into one routing topology. By employing existing basic
sorting and searching algorithms, the routing solutions can be computed in an
extremely efficient way. The proposed method is easy to implement and extensive
numerical results show that our approach is time efficient and generates
near-optimal solutions.
Modifying Optimal SAT-based Approach to Multi-agent Path-finding Problem to Suboptimal Variants
Authors: Pavel Surynek, Ariel Felner, Roni Stern, Eli Boyarski
Date: 2017-07-02 00:00:00
Tasks: Multi-Agent Path Finding

In multi-agent path finding (MAPF) the task is to find non-conflicting paths
for multiple agents. In this paper we focus on finding suboptimal solutions for
MAPF for the sum-of-costs variant. Recently, a SAT-based approached was
developed to solve this problem and proved beneficial in many cases when
compared to other search-based solvers. In this paper, we present SAT-based
unbounded- and bounded-suboptimal algorithms and compare them to relevant
algorithms. Experimental results show that in many case the SAT-based solver
significantly outperforms the search-based solvers.