pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning
Authors: Jingkuan Song, Zhao Guo, Lianli Gao, Wu Liu, Dongxiang Zhang, Heng Tao Shen
Date: 2017-06-05 00:00:00
Tasks: Caption Generation, Decoder, Language Modeling, Language Modelling, Video Captioning

Recent progress has been made in using attention based encoder-decoder
framework for video captioning. However, most existing decoders apply the
attention mechanism to every generated word including both visual words (e.g.,
"gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these
non-visual words can be easily predicted using natural language model without
considering visual signals or attention. Imposing attention mechanism on
non-visual words could mislead and decrease the overall performance of video
captioning. To address this issue, we propose a hierarchical LSTM with adjusted
temporal attention (hLSTMat) approach for video captioning. Specifically, the
proposed framework utilizes the temporal attention for selecting specific
frames to predict the related words, while the adjusted temporal attention is
for deciding whether to depend on the visual information or the language
context information. Also, a hierarchical LSTMs is designed to simultaneously
consider both low-level visual information and high-level language context
information to support the video caption generation. To demonstrate the
effectiveness of our proposed framework, we test our method on two prevalent
datasets: MSVD and MSR-VTT, and experimental results show that our approach
outperforms the state-of-the-art methods on both two datasets.
Ten Steps of EM Suffice for Mixtures of Two Gaussians
Authors: Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis
Date: 2016-09-01 00:00:00
Tasks: Clustering, Vocal Bursts Valence Prediction

The Expectation-Maximization (EM) algorithm is a widely used method for
maximum likelihood estimation in models with latent variables. For estimating
mixtures of Gaussians, its iteration can be viewed as a soft version of the
k-means clustering algorithm. Despite its wide use and applications, there are
essentially no known convergence guarantees for this method. We provide global
convergence guarantees for mixtures of two Gaussians with known covariance
matrices. We show that the population version of EM, where the algorithm is
given access to infinitely many samples from the mixture, converges
geometrically to the correct mean vectors, and provide simple, closed-form
expressions for the convergence rate. As a simple illustration, we show that,
in one dimension, ten steps of the EM algorithm initialized at infinity result
in less than 1\% error estimation of the means. In the finite sample regime, we
show that, under a random initialization, $\tilde{O}(d/\epsilon^2)$ samples
suffice to compute the unknown vectors to within $\epsilon$ in Mahalanobis
distance, where $d$ is the dimension. In particular, the error rate of the EM
based estimator is $\tilde{O}\left(\sqrt{d \over n}\right)$ where $n$ is the
number of samples, which is optimal up to logarithmic factors.
Inconsistent Node Flattening for Improving Top-down Hierarchical Classification
Authors: Azad Naik, Huzefa Rangwala
Date: 2017-06-05 00:00:00
Tasks: Classification, General Classification

Large-scale classification of data where classes are structurally organized
in a hierarchy is an important area of research. Top-down approaches that
exploit the hierarchy during the learning and prediction phase are efficient
for large scale hierarchical classification. However, accuracy of top-down
approaches is poor due to error propagation i.e., prediction errors made at
higher levels in the hierarchy cannot be corrected at lower levels. One of the
main reason behind errors at the higher levels is the presence of inconsistent
nodes that are introduced due to the arbitrary process of creating these
hierarchies by domain experts. In this paper, we propose two different
data-driven approaches (local and global) for hierarchical structure
modification that identifies and flattens inconsistent nodes present within the
hierarchy. Our extensive empirical evaluation of the proposed approaches on
several image and text datasets with varying distribution of features, classes
and training instances per class shows improved classification performance over
competing hierarchical modification approaches. Specifically, we see an
improvement upto 7% in Macro-F1 score with our approach over best TD baseline.
SOURCE CODE: http://www.cs.gmu.edu/~mlbio/InconsistentNodeFlattening
Multi-task memory networks for category-specific aspect and opinion terms co-extraction
Authors: Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier
Date: 2017-02-06 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

In aspect-based sentiment analysis, most existing methods either focus on
aspect/opinion terms extraction or aspect terms categorization. However, each
task by itself only provides partial information to end users. To generate more
detailed and structured opinion analysis, we propose a finer-grained problem,
which we call category-specific aspect and opinion terms extraction. This
problem involves the identification of aspect and opinion terms within each
sentence, as well as the categorization of the identified terms. To this end,
we propose an end-to-end multi-task attention model, where each task
corresponds to aspect/opinion terms extraction for a specific category. Our
model benefits from exploring the commonalities and relationships among
different tasks to address the data sparsity issue. We demonstrate its
state-of-the-art performance on three benchmark datasets.
One-step and Two-step Classification for Abusive Language Detection on Twitter
Authors: Ji Ho Park, Pascale Fung
Date: 2017-06-05 00:00:00
Proceeding: WS 2017 8
Tasks: Abuse Detection, Abusive Language, Classification, General Classification, Multi-class Classification, regression

Automatic abusive language detection is a difficult but important task for
online social media. Our research explores a two-step approach of performing
classification on abusive language and then classifying into specific types and
compares it with one-step approach of doing one multi-class classification for
detecting sexist and racist languages. With a public English Twitter corpus of
20 thousand tweets in the type of sexism and racism, our approach shows a
promising performance of 0.827 F-measure by using HybridCNN in one-step and
0.824 F-measure by using logistic regression in two-steps.
The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square
Authors: Pragya Sur, Yuxin Chen, Emmanuel J. Candès
Date: 2017-06-05 00:00:00
Tasks: regression

Logistic regression is used thousands of times a day to fit data, predict
future outcomes, and assess the statistical significance of explanatory
variables. When used for the purpose of statistical inference, logistic models
produce p-values for the regression coefficients by using an approximation to
the distribution of the likelihood-ratio test. Indeed, Wilks' theorem asserts
that whenever we have a fixed number $p$ of variables, twice the log-likelihood
ratio (LLR) $2\Lambda$ is distributed as a $\chi^2_k$ variable in the limit of
large sample sizes $n$; here, $k$ is the number of variables being tested. In
this paper, we prove that when $p$ is not negligible compared to $n$, Wilks'
theorem does not hold and that the chi-square approximation is grossly
incorrect; in fact, this approximation produces p-values that are far too small
(under the null hypothesis). Assume that $n$ and $p$ grow large in such a way
that $p/n\rightarrow\kappa$ for some constant $\kappa < 1/2$. We prove that for
a class of logistic models, the LLR converges to a rescaled chi-square, namely,
$2\Lambda~\stackrel{\mathrm{d}}{\rightarrow}~\alpha(\kappa)\chi_k^2$, where the
scaling factor $\alpha(\kappa)$ is greater than one as soon as the
dimensionality ratio $\kappa$ is positive. Hence, the LLR is larger than
classically assumed. For instance, when $\kappa=0.3$,
$\alpha(\kappa)\approx1.5$. In general, we show how to compute the scaling
factor by solving a nonlinear system of two equations with two unknowns. Our
mathematical arguments are involved and use techniques from approximate message
passing theory, non-asymptotic random matrix theory and convex geometry. We
also complement our mathematical study by showing that the new limiting
distribution is accurate for finite sample sizes. Finally, all the results from
this paper extend to some other regression models such as the probit regression
model.
AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification
Authors: Depeng Liang, Yongdong Zhang
Date: 2016-11-07 00:00:00
Tasks: Classification, General Classification, Sentence Embeddings, Sentiment Analysis, text-classification, Text Classification

Recently deeplearning models have been shown to be capable of making
remarkable performance in sentences and documents classification tasks. In this
work, we propose a novel framework called AC-BLSTM for modeling sentences and
documents, which combines the asymmetric convolution neural network (ACNN) with
the Bidirectional Long Short-Term Memory network (BLSTM). Experiment results
demonstrate that our model achieves state-of-the-art results on five tasks,
including sentiment analysis, question type classification, and subjectivity
classification. In order to further improve the performance of AC-BLSTM, we
propose a semi-supervised learning framework called G-AC-BLSTM for text
classification by combining the generative model with AC-BLSTM.
Parallel Structure from Motion from Local Increment to Global Averaging
Authors: Siyu Zhu, Tianwei Shen, Lei Zhou, Runze Zhang, Jinglu Wang, Tian Fang, Long Quan
Date: 2017-02-28 00:00:00
Tasks: Clustering

In this paper, we tackle the accurate and consistent Structure from Motion
(SfM) problem, in particular camera registration, far exceeding the memory of a
single computer in parallel. Different from the previous methods which
drastically simplify the parameters of SfM and sacrifice the accuracy of the
final reconstruction, we try to preserve the connectivities among cameras by
proposing a camera clustering algorithm to divide a large SfM problem into
smaller sub-problems in terms of camera clusters with overlapping. We then
exploit a hybrid formulation that applies the relative poses from local
incremental SfM into a global motion averaging framework and produce accurate
and consistent global camera poses. Our scalable formulation in terms of camera
clusters is highly applicable to the whole SfM pipeline including track
generation, local SfM, 3D point triangulation and bundle adjustment. We are
even able to reconstruct the camera poses of a city-scale data-set containing
more than one million high-resolution images with superior accuracy and
robustness evaluated on benchmark, Internet, and sequential data-sets.
PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks
Authors: Yu Shi, Po-Wei Chan, Honglei Zhuang, Huan Gui, Jiawei Han
Date: 2017-06-05 00:00:00

As a powerful representation paradigm for networked and multi-typed data, the
heterogeneous information network (HIN) is ubiquitous. Meanwhile, defining
proper relevance measures has always been a fundamental problem and of great
pragmatic importance for network mining tasks. Inspired by our probabilistic
interpretation of existing path-based relevance measures, we propose to study
HIN relevance from a probabilistic perspective. We also identify, from
real-world data, and propose to model cross-meta-path synergy, which is a
characteristic important for defining path-based HIN relevance and has not been
modeled by existing methods. A generative model is established to derive a
novel path-based relevance measure, which is data-driven and tailored for each
HIN. We develop an inference algorithm to find the maximum a posteriori (MAP)
estimate of the model parameters, which entails non-trivial tricks. Experiments
on two real-world datasets demonstrate the effectiveness of the proposed model
and relevance measure.
Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification
Authors: Srikrishna Karanam, Eric Lam, Richard J. Radke
Date: 2017-06-02 00:00:00
Tasks: Person Re-Identification

Designing useful person re-identification systems for real-world applications
requires attention to operational aspects not typically considered in academic
research. Here, we focus on the temporal aspect of re-identification; that is,
instead of finding a match to a probe person of interest in a fixed candidate
gallery, we consider the more realistic scenario in which the gallery is
continuously populated by new candidates over a long time period. A key
question of interest for an operator of such a system is: how long is a correct
match to a probe likely to remain in a rank-k shortlist of possible candidates?
We propose to distill this information into a Rank Persistence Curve (RPC),
which allows different algorithms' temporal performance characteristics to be
directly compared. We present examples to illustrate the RPC using a new
long-term dataset with multiple candidate reappearances, and discuss
considerations for future re-identification research that explicitly involves
temporal aspects.
Sample complexity of population recovery
Authors: Yury Polyanskiy, Ananda Theertha Suresh, Yihong Wu
Date: 2017-02-18 00:00:00

The problem of population recovery refers to estimating a distribution based on incomplete or corrupted samples. Consider a random poll of sample size $n$ conducted on a population of individuals, where each pollee is asked to answer $d$ binary questions. We consider one of the two polling impediments: (a) in lossy population recovery, a pollee may skip each question with probability $\epsilon$, (b) in noisy population recovery, a pollee may lie on each question with probability $\epsilon$. Given $n$ lossy or noisy samples, the goal is to estimate the probabilities of all $2^d$ binary vectors simultaneously within accuracy $\delta$ with high probability. This paper settles the sample complexity of population recovery. For lossy model, the optimal sample complexity is $\tilde\Theta(\delta^{-2\max\{\frac{\epsilon}{1-\epsilon},1\}})$, improving the state of the art by Moitra and Saks in several ways: a lower bound is established, the upper bound is improved and the result depends at most on the logarithm of the dimension. Surprisingly, the sample complexity undergoes a phase transition from parametric to nonparametric rate when $\epsilon$ exceeds $1/2$. For noisy population recovery, the sharp sample complexity turns out to be more sensitive to dimension and scales as $\exp(\Theta(d^{1/3} \log^{2/3}(1/\delta)))$ except for the trivial cases of $\epsilon=0,1/2$ or $1$. For both models, our estimators simply compute the empirical mean of a certain function, which is found by pre-solving a linear program (LP). Curiously, the dual LP can be understood as Le Cam's method for lower-bounding the minimax risk, thus establishing the statistical optimality of the proposed estimators. The value of the LP is determined by complex-analytic methods.
A New Representation of Skeleton Sequences for 3D Action Recognition
Authors: Qiuhong Ke, Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid
Date: 2017-03-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Action Recognition, Action Recognition, Multi-Task Learning, Skeleton Based Action Recognition, Temporal Action Localization

This paper presents a new method for 3D action recognition with skeleton
sequences (i.e., 3D trajectories of human skeleton joints). The proposed method
first transforms each skeleton sequence into three clips each consisting of
several frames for spatial temporal feature learning using deep neural
networks. Each clip is generated from one channel of the cylindrical
coordinates of the skeleton sequence. Each frame of the generated clips
represents the temporal information of the entire skeleton sequence, and
incorporates one particular spatial relationship between the joints. The entire
clips include multiple frames with different spatial relationships, which
provide useful spatial structural information of the human skeleton. We propose
to use deep convolutional neural networks to learn long-term temporal
information of the skeleton sequence from the frames of the generated clips,
and then use a Multi-Task Learning Network (MTLN) to jointly process all frames
of the generated clips in parallel to incorporate spatial structural
information for action recognition. Experimental results clearly show the
effectiveness of the proposed new representation and feature learning method
for 3D action recognition.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Authors: Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, William J. Dally
Date: 2017-05-24 00:00:00

Sparsity helps reduce the computational complexity of deep neural networks by
skipping zeros. Taking advantage of sparsity is listed as a high priority in
next generation DNN accelerators such as TPU. The structure of sparsity, i.e.,
the granularity of pruning, affects the efficiency of hardware accelerator
design as well as the prediction accuracy. Coarse-grained pruning creates
regular sparsity patterns, making it more amenable for hardware acceleration
but more challenging to maintain the same accuracy. In this paper we
quantitatively measure the trade-off between sparsity regularity and prediction
accuracy, providing insights in how to maintain accuracy while having more a
more structured sparsity pattern. Our experimental results show that
coarse-grained pruning can achieve a sparsity ratio similar to unstructured
pruning without loss of accuracy. Moreover, due to the index saving effect,
coarse-grained pruning is able to obtain a better compression ratio than
fine-grained sparsity at the same accuracy threshold. Based on the recent
sparse convolutional neural network accelerator (SCNN), our experiments further
demonstrate that coarse-grained sparsity saves about 2x the memory references
compared to fine-grained sparsity. Since memory reference is more than two
orders of magnitude more expensive than arithmetic operations, the regularity
of sparse structure leads to more efficient hardware design.
Generative Compression
Authors: Shibani Santurkar, David Budden, Nir Shavit
Date: 2017-03-04 00:00:00
Tasks: Decoder, Video Compression

Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes.
Graphical Nonconvex Optimization for Optimal Estimation in Gaussian Graphical Models
Authors: Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang
Date: 2017-06-04 00:00:00

We consider the problem of learning high-dimensional Gaussian graphical
models. The graphical lasso is one of the most popular methods for estimating
Gaussian graphical models. However, it does not achieve the oracle rate of
convergence. In this paper, we propose the graphical nonconvex optimization for
optimal estimation in Gaussian graphical models, which is then approximated by
a sequence of convex programs. Our proposal is computationally tractable and
produces an estimator that achieves the oracle rate of convergence. The
statistical error introduced by the sequential approximation using the convex
programs are clearly demonstrated via a contraction property. The rate of
convergence can be further improved using the notion of sparsity pattern. The
proposed methodology is then extended to semiparametric graphical models. We
show through numerical studies that the proposed estimator outperforms other
popular methods for estimating Gaussian graphical models.
Practical Coreset Constructions for Machine Learning
Authors: Olivier Bachem, Mario Lucic, Andreas Krause
Date: 2017-03-19 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, regression

We investigate coresets - succinct, small summaries of large data sets - so
that solutions found on the summary are provably competitive with solution
found on the full data set. We provide an overview over the state-of-the-art in
coreset construction for machine learning. In Section 2, we present both the
intuition behind and a theoretically sound framework to construct coresets for
general problems and apply it to $k$-means clustering. In Section 3 we
summarize existing coreset construction algorithms for a variety of machine
learning problems such as maximum likelihood estimation of mixture models,
Bayesian non-parametric models, principal component analysis, regression and
general empirical risk minimization.
Deep MIMO Detection
Authors: Neev Samuel, Tzvi Diskin, Ami Wiesel
Date: 2017-06-04 00:00:00

In this paper, we consider the use of deep neural networks in the context of
Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction
to deep learning and propose a modern neural network architecture suitable for
this detection task. First, we consider the case in which the MIMO channel is
constant, and we learn a detector for a specific system. Next, we consider the
harder case in which the parameters are known yet changing and a single
detector must be learned for all multiple varying channels. We demonstrate the
performance of our deep MIMO detector using numerical simulations in comparison
to competing methods including approximate message passing and semidefinite
relaxation. The results show that deep networks can achieve state of the art
accuracy with significantly lower complexity while providing robustness against
ill conditioned channels and mis-specified noise variance.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Authors: Gerda Bortsova, Gijs van Tulder, Florian Dubost, Tingying Peng, Nassir Navab, Aad van der Lugt, Daniel Bos, Marleen de Bruijne
Date: 2017-06-04 00:00:00

Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segmentation is therefore highly desirable, but difficult due to the
proximity of the lesions to bony structures with a similar attenuation
coefficient. In this paper, we propose a method for automatic segmentation of
ICAC; the first to our knowledge. Our method is based on a 3D fully
convolutional neural network that we extend with two regularization techniques.
Firstly, we use deep supervision (hidden layers supervision) to encourage
discriminative features in the hidden layers. Secondly, we augment the network
with skip connections, as in the recently developed ResNet, and dropout layers,
inserted in a way that skip connections circumvent them. We investigate the
effect of skip connections and dropout. In addition, we propose a simple
problem-specific modification of the network objective function that restricts
the focus to the most important image regions and simplifies the optimization.
We train and validate our model using 882 CT scans and test on 1,000. Our
regularization techniques and objective improve the average Dice score by 7.1%,
yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
volumes and manual annotations.