pwc-archive/papers-with-abstracts huggingface dataset:
Learning Compact Appearance Representation for Video-based Person Re-Identification
Authors: Wei Zhang, Shengnan Hu, Kan Liu, Zheng-Jun Zha
Date: 2017-02-21 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

This paper presents a novel approach for video-based person re-identification using multiple Convolutional Neural Networks (CNNs). Unlike previous work, we intend to extract a compact yet discriminative appearance representation from several frames rather than the whole sequence. Specifically, given a video, the representative frames are selected based on the walking profile of consecutive frames. A multiple CNN architecture incorporated with feature pooling is proposed to learn and compile the features of the selected representative frames into a compact description about the pedestrian for identification. Experiments are conducted on benchmark datasets to demonstrate the superiority of the proposed method over existing person re-identification approaches.
Real-time visual tracking by deep reinforced decision making
Authors: Janghoon Choi, Junseok Kwon, Kyoung Mu Lee
Date: 2017-02-21 00:00:00
Tasks: Deep Reinforcement Learning, Real-Time Visual Tracking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Tracking

One of the major challenges of model-free visual tracking problem has been
the difficulty originating from the unpredictable and drastic changes in the
appearance of objects we target to track. Existing methods tackle this problem
by updating the appearance model on-line in order to adapt to the changes in
the appearance. Despite the success of these methods however, inaccurate and
erroneous updates of the appearance model result in a tracker drift. In this
paper, we introduce a novel real-time visual tracking algorithm based on a
template selection strategy constructed by deep reinforcement learning methods.
The tracking algorithm utilizes this strategy to choose the appropriate
template for tracking a given frame. The template selection strategy is
self-learned by utilizing a simple policy gradient method on numerous training
episodes randomly generated from a tracking benchmark dataset. Our proposed
reinforcement learning framework is generally applicable to other confidence
map based tracking algorithms. The experiment shows that our tracking algorithm
runs in real-time speed of 43 fps and the proposed policy network effectively
decides the appropriate template for successful visual tracking.
A Sparse Linear Model and Significance Test for Individual Consumption Prediction
Authors: Pan Li, Baosen Zhang, Yang Weng, Ram Rajagopal
Date: 2015-11-05 00:00:00
Tasks: Prediction, Two-sample testing

Accurate prediction of user consumption is a key part not only in
understanding consumer flexibility and behavior patterns, but in the design of
robust and efficient energy saving programs as well. Existing prediction
methods usually have high relative errors that can be larger than 30% and have
difficulties accounting for heterogeneity between individual users. In this
paper, we propose a method to improve prediction accuracy of individual users
by adaptively exploring sparsity in historical data and leveraging predictive
relationship between different users. Sparsity is captured by popular least
absolute shrinkage and selection estimator, while user selection is formulated
as an optimal hypothesis testing problem and solved via a covariance test.
Using real world data from PG&E, we provide extensive simulation validation of
the proposed method against well-known techniques such as support vector
machine, principle component analysis combined with linear regression, and
random forest. The results demonstrate that our proposed methods are
operationally efficient because of linear nature, and achieve optimal
prediction performance.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Authors: Emre Çakır, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen, Tuomas Virtanen
Date: 2017-02-21 00:00:00
Tasks: Event Detection, Sound Event Detection

Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.
Scene Recognition by Combining Local and Global Image Descriptors
Authors: Jobin Wilson, Muhammad Arif
Date: 2017-02-21 00:00:00
Tasks: Object Recognition, Scene Recognition

Object recognition is an important problem in computer vision, having diverse
applications. In this work, we construct an end-to-end scene recognition
pipeline consisting of feature extraction, encoding, pooling and
classification. Our approach simultaneously utilize global feature descriptors
as well as local feature descriptors from images, to form a hybrid feature
descriptor corresponding to each image. We utilize DAISY features associated
with key points within images as our local feature descriptor and histogram of
oriented gradients (HOG) corresponding to an entire image as a global
descriptor. We make use of a bag-of-visual-words encoding and apply Mini- Batch
K-Means algorithm to reduce the complexity of our feature encoding scheme. A
2-level pooling procedure is used to combine DAISY and HOG features
corresponding to each image. Finally, we experiment with a multi-class SVM
classifier with several kernels, in a cross-validation setting, and tabulate
our results on the fifteen scene categories dataset. The average accuracy of
our model was 76.4% in the case of a 40%-60% random split of images into
training and testing datasets respectively. The primary objective of this work
is to clearly outline the practical implementation of a basic
screne-recognition pipeline having a reasonable accuracy, in python, using
open-source libraries. A full implementation of the proposed model is available
in our github repository.
On Detecting Adversarial Perturbations
Authors: Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff
Date: 2017-02-14 00:00:00
Tasks: Adversarial Attack, Binary Classification, General Classification

Machine learning and deep learning in particular has advanced tremendously on
perceptual tasks in recent years. However, it remains vulnerable against
adversarial perturbations of the input that have been crafted specifically to
fool the system while being quasi-imperceptible to a human. In this work, we
propose to augment deep neural networks with a small "detector" subnetwork
which is trained on the binary classification task of distinguishing genuine
data from data containing adversarial perturbations. Our method is orthogonal
to prior work on addressing adversarial perturbations, which has mostly focused
on making the classification network itself more robust. We show empirically
that adversarial perturbations can be detected surprisingly well even though
they are quasi-imperceptible to humans. Moreover, while the detectors have been
trained to detect only a specific adversary, they generalize to similar and
weaker adversaries. In addition, we propose an adversarial attack that fools
both the classifier and the detector and a novel training procedure for the
detector that counteracts this attack.
Neural Architectures for Fine-grained Entity Type Classification
Authors: Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel
Date: 2016-06-04 00:00:00
Proceeding: EACL 2017 4
Tasks: Classification, General Classification, Vocal Bursts Type Prediction

In this work, we investigate several neural network architectures for
fine-grained entity type classification. Particularly, we consider extensions
to a recently proposed attentive neural architecture and make three key
contributions. Previous work on attentive neural architectures do not consider
hand-crafted features, we combine learnt and hand-crafted features and observe
that they complement each other. Additionally, through quantitative analysis we
establish that the attention mechanism is capable of learning to attend over
syntactic heads and the phrase containing the mention, where both are known
strong hand-crafted features for our task. We enable parameter sharing through
a hierarchical label encoding method, that in low-dimensional projections show
clear clusters for each type hierarchy. Lastly, despite using the same
evaluation dataset, the literature frequently compare models trained using
different data. We establish that the choice of training data has a drastic
impact on performance, with decreases by as much as 9.85% loose micro F1 score
for a previously proposed method. Despite this, our best model achieves
state-of-the-art results with 75.36% loose micro F1 score on the well-
established FIGER (GOLD) dataset.
Column normalization of a random measurement matrix
Authors: Shahar Mendelson
Date: 2017-02-21 00:00:00

In this note we answer a question of G. Lecu\'{e}, by showing that column
normalization of a random matrix with iid entries need not lead to good sparse
recovery properties, even if the generating random variable has a reasonable
moment growth. Specifically, for every $2 \leq p \leq c_1\log d$ we construct a
random vector $X \in R^d$ with iid, mean-zero, variance $1$ coordinates, that
satisfies $\sup_{t \in S^{d-1}} \|<X,t>\|_{L_q} \leq c_2\sqrt{q}$ for every
$2\leq q \leq p$.
  We show that if $m \leq c_3\sqrt{p}d^{1/p}$ and $\tilde{\Gamma}:R^d \to R^m$
is the column-normalized matrix generated by $m$ independent copies of $X$,
then with probability at least $1-2\exp(-c_4m)$, $\tilde{\Gamma}$ does not
satisfy the exact reconstruction property of order $2$.
Projection based advanced motion model for cubic mapping for 360-degree video
Authors: Li Li, Zhu Li, Madhukar Budagavi, Houqiang Li
Date: 2017-02-21 00:00:00

This paper proposes a novel advanced motion model to handle the irregular
motion for the cubic map projection of 360-degree video. Since the irregular
motion is mainly caused by the projection from the sphere to the cube map, we
first try to project the pixels in both the current picture and reference
picture from unfolding cube back to the sphere. Then through utilizing the
characteristic that most of the motions in the sphere are uniform, we can
derive the relationship between the motion vectors of various pixels in the
unfold cube. The proposed advanced motion model is implemented in the High
Efficiency Video Coding reference software. Experimental results demonstrate
that quite obvious performance improvement can be achieved for the sequences
with obvious motions.
Stochastic Canonical Correlation Analysis
Authors: Chao Gao, Dan Garber, Nathan Srebro, Jialei Wang, Weiran Wang
Date: 2017-02-21 00:00:00
Tasks: Stochastic Optimization

We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbitrarily small error. With mild assumptions on the data distribution, we show that in order to achieve $\epsilon$-suboptimality in a properly defined measure of alignment between the estimated canonical directions and the population solution, we can solve the empirical objective exactly with $N(\epsilon, \Delta, \gamma)$ samples, where $\Delta$ is the singular value gap of the whitened cross-covariance matrix and $1/\gamma$ is an upper bound of the condition number of auto-covariance matrices. Moreover, we can achieve the same learning accuracy by drawing the same level of samples and solving the empirical objective approximately with a stochastic optimization algorithm; this algorithm is based on the shift-and-invert power iterations and only needs to process the dataset for $\mathcal{O}\left(\log \frac{1}{\epsilon} \right)$ passes. Finally, we show that, given an estimate of the canonical correlation, the streaming version of the shift-and-invert power iterations achieves the same learning accuracy with the same level of sample complexity, by processing the data only once.
Neural Machine Translation with Pivot Languages
Authors: Yong Cheng, Yang Liu, Qian Yang, Maosong Sun, Wei Xu
Date: 2016-11-15 00:00:00
Tasks: Machine Translation, Translation

While recent neural machine translation approaches have delivered
state-of-the-art performance for resource-rich language pairs, they suffer from
the data scarcity problem for resource-scarce language pairs. Although this
problem can be alleviated by exploiting a pivot language to bridge the source
and target languages, the source-to-pivot and pivot-to-target translation
models are usually independently trained. In this work, we introduce a joint
training algorithm for pivot-based neural machine translation. We propose three
methods to connect the two models and enable them to interact with each other
during training. Experiments on Europarl and WMT corpora show that joint
training of source-to-pivot and pivot-to-target models leads to significant
improvements over independent training across various languages.
The Power of Sparsity in Convolutional Neural Networks
Authors: Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov
Date: 2017-02-21 00:00:00

Deep convolutional networks are well-known for their high computational and
memory demands. Given limited resources, how does one design a network that
balances its size, training time, and prediction accuracy? A surprisingly
effective approach to trade accuracy for size and speed is to simply reduce the
number of channels in each convolutional layer by a fixed fraction and retrain
the network. In many cases this leads to significantly smaller networks with
only minimal changes to accuracy. In this paper, we take a step further by
empirically examining a strategy for deactivating connections between filters
in convolutional layers in a way that allows us to harvest savings both in
run-time and memory for many network architectures. More specifically, we
generalize 2D convolution to use a channel-wise sparse connection structure and
show that this leads to significantly better results than the baseline approach
for large networks including VGG and Inception V3.
Universal halting times in optimization and machine learning
Authors: Levent Sagun, Thomas Trogdon, Yann Lecun
Date: 2015-11-19 00:00:00
Tasks: BIG-bench Machine Learning

The authors present empirical distributions for the halting time (measured by
the number of iterations to reach a given accuracy) of optimization algorithms
applied to two random systems: spin glasses and deep learning. Given an
algorithm, which we take to be both the optimization routine and the form of
the random landscape, the fluctuations of the halting time follow a
distribution that, after centering and scaling, remains unchanged even when the
distribution on the landscape is changed. We observe two qualitative classes: A
Gumbel-like distribution that appears in Google searches, human decision times,
the QR eigenvalue algorithm and spin glasses, and a Gaussian-like distribution
that appears in conjugate gradient method, deep network with MNIST input data
and deep network with random input data. This empirical evidence suggests
presence of a class of distributions for which the halting time is independent
of the underlying distribution under some conditions.
Zero-Shot Learning posed as a Missing Data Problem
Authors: Bo Zhao, Botong Wu, Tianfu Wu, Yizhou Wang
Date: 2016-12-02 00:00:00
Tasks: Zero-Shot Learning

This paper presents a method of zero-shot learning (ZSL) which poses ZSL as
the missing data problem, rather than the missing label problem. Specifically,
most existing ZSL methods focus on learning mapping functions from the image
feature space to the label embedding space. Whereas, the proposed method
explores a simple yet effective transductive framework in the reverse way \---
our method estimates data distribution of unseen classes in the image feature
space by transferring knowledge from the label embedding space. In experiments,
our method outperforms the state-of-the-art on two popular datasets.
Superpixel Segmentation Using Gaussian Mixture Model
Authors: Zhihua Ban, Jian-Guo Liu, Li Cao
Date: 2016-12-28 00:00:00
Tasks: Clustering, Computational Efficiency, model, Segmentation

Superpixel segmentation algorithms are to partition an image into
perceptually coherence atomic regions by assigning every pixel a superpixel
label. Those algorithms have been wildly used as a preprocessing step in
computer vision works, as they can enormously reduce the number of entries of
subsequent algorithms. In this work, we propose an alternative superpixel
segmentation method based on Gaussian mixture model (GMM) by assuming that each
superpixel corresponds to a Gaussian distribution, and assuming that each pixel
is generated by first randomly choosing one distribution from several Gaussian
distributions which are defined to be related to that pixel, and then the pixel
is drawn from the selected distribution. Based on this assumption, each pixel
is supposed to be drawn from a mixture of Gaussian distributions with unknown
parameters (GMM). An algorithm based on expectation-maximization method is
applied to estimate the unknown parameters. Once the unknown parameters are
obtained, the superpixel label of a pixel is determined by a posterior
probability. The success of applying GMM to superpixel segmentation depends on
the two major differences between the traditional GMM-based clustering and the
proposed one: data points in our model may be non-identically distributed, and
we present an approach to control the shape of the estimated Gaussian functions
by adjusting their covariance matrices. Our method is of linear complexity with
respect to the number of pixels. The proposed algorithm is inherently parallel
and can get faster speed by adding simple OpenMP directives to our
implementation. According to our experiments, our algorithm outperforms the
state-of-the-art superpixel algorithms in accuracy and presents a competitive
performance in computational efficiency.
Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions
Authors: Vira Semenova, Victor Chernozhukov
Date: 2017-02-21 00:00:00
Tasks: BIG-bench Machine Learning

This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques. We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularization bias. We then project the signal onto a set of basis functions, growing with sample size, which gives us the best linear predictor of the structural function. We derive a complete set of results for estimation and simultaneous inference on all parameters of the best linear predictor, conducting inference by Gaussian bootstrap. When the structural function is smooth and the basis is sufficiently rich, our estimation and inference result automatically targets this function. When basis functions are group indicators, the best linear predictor reduces to group average treatment/structural effect, and our inference automatically targets these parameters. We demonstrate our method by estimating uniform confidence bands for the average price elasticity of gasoline demand conditional on income.
Reinforcement Learning Based Argument Component Detection
Authors: Yang Gao, Hao Wang, Chen Zhang, Wei Wang
Date: 2017-02-21 00:00:00
Tasks: General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Argument component detection (ACD) is an important sub-task in argumentation
mining. ACD aims at detecting and classifying different argument components in
natural language texts. Historical annotations (HAs) are important features the
human annotators consider when they manually perform the ACD task. However, HAs
are largely ignored by existing automatic ACD techniques. Reinforcement
learning (RL) has proven to be an effective method for using HAs in some
natural language processing tasks. In this work, we propose a RL-based ACD
technique, and evaluate its performance on two well-annotated corpora. Results
suggest that, in terms of classification accuracy, HAs-augmented RL outperforms
plain RL by at most 17.85%, and outperforms the state-of-the-art supervised
learning algorithm by at most 11.94%.
Learning to generate one-sentence biographies from Wikidata
Authors: Andrew Chisholm, Will Radford, Ben Hachey
Date: 2017-02-21 00:00:00
Proceeding: EACL 2017 4
Tasks: Sentence

We investigate the generation of one-sentence Wikipedia biographies from
facts derived from Wikidata slot-value pairs. We train a recurrent neural
network sequence-to-sequence model with attention to select facts and generate
textual summaries. Our model incorporates a novel secondary objective that
helps ensure it generates sentences that contain the input facts. The model
achieves a BLEU score of 41, improving significantly upon the vanilla
sequence-to-sequence model and scoring roughly twice that of a simple template
baseline. Human preference evaluation suggests the model is nearly as good as
the Wikipedia reference. Manual analysis explores content selection, suggesting
the model can trade the ability to infer knowledge against the risk of
hallucinating incorrect information.
Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models
Authors: Yu-ting Qiang, Yanwei Fu, Xiao Yu, Yanwen Guo, Zhi-Hua Zhou, Leonid Sigal
Date: 2017-02-21 00:00:00

Researchers often summarize their work in the form of scientific posters.
Posters provide a coherent and efficient way to convey core ideas expressed in
scientific papers. Generating a good scientific poster, however, is a complex
and time consuming cognitive task, since such posters need to be readable,
informative, and visually aesthetic. In this paper, for the first time, we
study the challenging problem of learning to generate posters from scientific
papers. To this end, a data-driven framework, that utilizes graphical models,
is proposed. Specifically, given content to display, the key elements of a good
poster, including attributes of each panel and arrangements of graphical
elements are learned and inferred from data. During the inference stage, an MAP
inference framework is employed to incorporate some design principles. In order
to bridge the gap between panel attributes and the composition within each
panel, we also propose a recursive page splitting algorithm to generate the
panel layout for a poster. To learn and validate our model, we collect and
release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which
consists of scientific papers and corresponding posters with exhaustively
labelled panels and attributes. Qualitative and quantitative results indicate
the effectiveness of our approach.