pwc-archive/papers-with-abstracts huggingface dataset:
Strategic Dialogue Management via Deep Reinforcement Learning
Authors: Heriberto Cuayáhuitl, Simon Keizer, Oliver Lemon
Date: 2015-11-25 00:00:00
Tasks: Deep Reinforcement Learning, Dialogue Management, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Artificially intelligent agents equipped with strategic skills that can
negotiate during their interactions with other natural or artificial agents are
still underdeveloped. This paper describes a successful application of Deep
Reinforcement Learning (DRL) for training intelligent agents with strategic
conversational skills, in a situated dialogue setting. Previous studies have
modelled the behaviour of strategic agents using supervised learning and
traditional reinforcement learning techniques, the latter using tabular
representations or learning with linear function approximation. In this study,
we apply DRL with a high-dimensional state space to the strategic board game of
Settlers of Catan---where players can offer resources in exchange for others
and they can also reply to offers made by other players. Our experimental
results report that the DRL-based learnt policies significantly outperformed
several baselines including random, rule-based, and supervised-based
behaviours. The DRL-based policy has a 53% win rate versus 3 automated players
(`bots'), whereas a supervised player trained on a dialogue corpus in this
setting achieved only 27%, versus the same 3 bots. This result supports the
claim that DRL is a promising framework for training dialogue systems, and
strategic agents with negotiation abilities.
Relaxed Majorization-Minimization for Non-smooth and Non-convex Optimization
Authors: Chen Xu, Zhouchen Lin, Zhenyu Zhao, Hongbin Zha
Date: 2015-11-25 00:00:00

We propose a new majorization-minimization (MM) method for non-smooth and
non-convex programs, which is general enough to include the existing MM
methods. Besides the local majorization condition, we only require that the
difference between the directional derivatives of the objective function and
its surrogate function vanishes when the number of iterations approaches
infinity, which is a very weak condition. So our method can use a surrogate
function that directly approximates the non-smooth objective function. In
comparison, all the existing MM methods construct the surrogate function by
approximating the smooth component of the objective function. We apply our
relaxed MM methods to the robust matrix factorization (RMF) problem with
different regularizations, where our locally majorant algorithm shows
advantages over the state-of-the-art approaches for RMF. This is the first
algorithm for RMF ensuring, without extra assumptions, that any limit point of
the iterates is a stationary point.
Pedestrian Detection Inspired by Appearance Constancy and Shape Symmetry
Authors: Jiale Cao, Yanwei Pang, Xuelong. Li
Date: 2015-11-25 00:00:00
Proceeding: CVPR 2016 6
Tasks: Pedestrian Detection

The discrimination and simplicity of features are very important for
effective and efficient pedestrian detection. However, most state-of-the-art
methods are unable to achieve good tradeoff between accuracy and efficiency.
Inspired by some simple inherent attributes of pedestrians (i.e., appearance
constancy and shape symmetry), we propose two new types of non-neighboring
features (NNF): side-inner difference features (SIDF) and symmetrical
similarity features (SSF). SIDF can characterize the difference between the
background and pedestrian and the difference between the pedestrian contour and
its inner part. SSF can capture the symmetrical similarity of pedestrian shape.
However, it's difficult for neighboring features to have such above
characterization abilities. Finally, we propose to combine both non-neighboring
and neighboring features for pedestrian detection. It's found that
non-neighboring features can further decrease the average miss rate by 4.44%.
Experimental results on INRIA and Caltech pedestrian datasets demonstrate the
effectiveness and efficiency of the proposed method. Compared to the
state-of-the-art methods without using CNN, our method achieves the best
detection performance on Caltech, outperforming the second best method (i.e.,
Checkboards) by 1.63%.
Learning to detect video events from zero or very few video examples
Authors: Christos Tzelepis, Damianos Galanopoulos, Vasileios Mezaris, Ioannis Patras
Date: 2015-11-25 00:00:00
Tasks: Event Detection

In this work we deal with the problem of high-level event detection in video.
Specifically, we study the challenging problems of i) learning to detect video
events from solely a textual description of the event, without using any
positive video examples, and ii) additionally exploiting very few positive
training samples together with a small number of ``related'' videos. For
learning only from an event's textual description, we first identify a general
learning framework and then study the impact of different design choices for
various stages of this framework. For additionally learning from example
videos, when true positive training samples are scarce, we employ an extension
of the Support Vector Machine that allows us to exploit ``related'' event
videos by automatically introducing different weights for subsets of the videos
in the overall training set. Experimental evaluations performed on the
large-scale TRECVID MED 2014 video dataset provide insight on the effectiveness
of the proposed methods.
Fast and Simple PCA via Convex Optimization
Authors: Dan Garber, Elad Hazan
Date: 2015-09-18 00:00:00

The problem of principle component analysis (PCA) is traditionally solved by
spectral or algebraic methods. We show how computing the leading principal
component could be reduced to solving a \textit{small} number of
well-conditioned {\it convex} optimization problems. This gives rise to a new
efficient method for PCA based on recent advances in stochastic methods for
convex optimization.
  In particular we show that given a $d\times d$ matrix $\X =
\frac{1}{n}\sum_{i=1}^n\x_i\x_i^{\top}$ with top eigenvector $\u$ and top
eigenvalue $\lambda_1$ it is possible to: \begin{itemize} \item compute a unit
vector $\w$ such that $(\w^{\top}\u)^2 \geq 1-\epsilon$ in
$\tilde{O}\left({\frac{d}{\delta^2}+N}\right)$ time, where $\delta = \lambda_1
- \lambda_2$ and $N$ is the total number of non-zero entries in
$\x_1,...,\x_n$,
  \item compute a unit vector $\w$ such that $\w^{\top}\X\w \geq
\lambda_1-\epsilon$ in $\tilde{O}(d/\epsilon^2)$ time. \end{itemize} To the
best of our knowledge, these bounds are the fastest to date for a wide regime
of parameters. These results could be further accelerated when $\delta$ (in the
first case) and $\epsilon$ (in the second case) are smaller than $\sqrt{d/N}$.
Cross Modal Distillation for Supervision Transfer
Authors: Saurabh Gupta, Judy Hoffman, Jitendra Malik
Date: 2015-07-02 00:00:00
Proceeding: CVPR 2016 6
Tasks: Optical Flow Estimation

In this work we propose a technique that transfers supervision between images
from different modalities. We use learned representations from a large labeled
modality as a supervisory signal for training representations for a new
unlabeled paired modality. Our method enables learning of rich representations
for unlabeled modalities and can be used as a pre-training procedure for new
modalities with limited labeled data. We show experimental results where we
transfer supervision from labeled RGB images to unlabeled depth and optical
flow images and demonstrate large improvements for both these cross modal
supervision transfers. Code, data and pre-trained models are available at
https://github.com/s-gupta/fast-rcnn/tree/distillation
A Short Survey on Data Clustering Algorithms
Authors: Ka-Chun Wong
Date: 2015-11-25 00:00:00
Tasks: Clustering, Financial Analysis, speech-recognition, Speech Recognition, Survey

With rapidly increasing data, clustering algorithms are important tools for
data analytics in modern research. They have been successfully applied to a
wide range of domains; for instance, bioinformatics, speech recognition, and
financial analysis. Formally speaking, given a set of data instances, a
clustering algorithm is expected to divide the set of data instances into the
subsets which maximize the intra-subset similarity and inter-subset
dissimilarity, where a similarity measure is defined beforehand. In this work,
the state-of-the-arts clustering algorithms are reviewed from design concept to
methodology; Different clustering paradigms are discussed. Advanced clustering
algorithms are also discussed. After that, the existing clustering evaluation
metrics are reviewed. A summary with future insights is provided at the end.
Calculate distance to object in the area where car, using video analysis
Authors: Elena Legchekova, Oleg Titov
Date: 2015-11-25 00:00:00
Tasks: Object

The method of using video cameras installed on the car, to calculate the
distance to the object in its area of movement.
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
Authors: Jiazhen He, Benjamin I. P. Rubinstein, James Bailey, Rui Zhang, Sandra Milligan, Jeffrey Chan
Date: 2015-11-25 00:00:00
Tasks: Topic Models

This paper adapts topic models to the psychometric testing of MOOC students
based on their online forum postings. Measurement theory from education and
psychology provides statistical models for quantifying a person's attainment of
intangible attributes such as attitudes, abilities or intelligence. Such models
infer latent skill levels by relating them to individuals' observed responses
on a series of items such as quiz questions. The set of items can be used to
measure a latent skill if individuals' responses on them conform to a Guttman
scale. Such well-scaled items differentiate between individuals and inferred
levels span the entire range from most basic to the advanced. In practice,
education researchers manually devise items (quiz questions) while optimising
well-scaled conformance. Due to the costly nature and expert requirements of
this process, psychometric testing has found limited use in everyday teaching.
We aim to develop usable measurement models for highly-instrumented MOOC
delivery platforms, by using participation in automatically-extracted online
forum topics as items. The challenge is to formalise the Guttman scale
educational constraint and incorporate it into topic models. To favour topics
that automatically conform to a Guttman scale, we introduce a novel
regularisation into non-negative matrix factorisation-based topic modelling. We
demonstrate the suitability of our approach with both quantitative experiments
on three Coursera MOOCs, and with a qualitative survey of topic
interpretability on two MOOCs by domain expert interviews.
Exploring Correlation between Labels to improve Multi-Label Classification
Authors: Amit Garg, Jonathan Noyola, Romil Verma, Ashutosh Saxena, Aditya Jami
Date: 2015-11-25 00:00:00
Tasks: Binary Classification, Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, regression

This paper attempts multi-label classification by extending the idea of
independent binary classification models for each output label, and exploring
how the inherent correlation between output labels can be used to improve
predictions. Logistic Regression, Naive Bayes, Random Forest, and SVM models
were constructed, with SVM giving the best results: an improvement of 12.9\%
over binary models was achieved for hold out cross validation by augmenting
with pairwise correlation probabilities of the labels.
PASCAL Boundaries: A Class-Agnostic Semantic Boundary Dataset
Authors: Vittal Premachandran, Boyan Bonev, Alan L. Yuille
Date: 2015-11-25 00:00:00
Tasks: Boundary Detection, Edge Detection

In this paper, we address the boundary detection task motivated by the
ambiguities in current definition of edge detection. To this end, we generate a
large database consisting of more than 10k images (which is 20x bigger than
existing edge detection databases) along with ground truth boundaries between
459 semantic classes including both foreground objects and different types of
background, and call it the PASCAL Boundaries dataset, which will be released
to the community. In addition, we propose a novel deep network-based
multi-scale semantic boundary detector and name it Multi-scale Deep Semantic
Boundary Detector (M-DSBD). We provide baselines using models that were trained
on edge detection and show that they transfer reasonably to the task of
boundary detection. Finally, we point to various important research problems
that this dataset can be used for.
Learning Halfspaces and Neural Networks with Random Initialization
Authors: Yuchen Zhang, Jason D. Lee, Martin J. Wainwright, Michael. I. Jordan
Date: 2015-11-25 00:00:00

We study non-convex empirical risk minimization for learning halfspaces and
neural networks. For loss functions that are $L$-Lipschitz continuous, we
present algorithms to learn halfspaces and multi-layer neural networks that
achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is
polynomial in the input dimension $d$ and the sample size $n$, but exponential
in the quantity $(L/\epsilon^2)\log(L/\epsilon)$. These algorithms run multiple
rounds of random initialization followed by arbitrary optimization steps. We
further show that if the data is separable by some neural network with constant
margin $\gamma>0$, then there is a polynomial-time algorithm for learning a
neural network that separates the training data with margin $\Omega(\gamma)$.
As a consequence, the algorithm achieves arbitrary generalization error
$\epsilon>0$ with ${\rm poly}(d,1/\epsilon)$ sample and time complexity. We
establish the same learnability result when the labels are randomly flipped
with probability $\eta<1/2$.
Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering
Authors: Dekang Zhu, Dan P. Guralnik, Xuezhi Wang, Xiang Li, Bill Moran
Date: 2015-11-25 00:00:00
Tasks: Clustering, Small Data Image Classification

We derive a statistical model for estimation of a dendrogram from single
linkage hierarchical clustering (SLHC) that takes account of uncertainty
through noise or corruption in the measurements of separation of data. Our
focus is on just the estimation of the hierarchy of partitions afforded by the
dendrogram, rather than the heights in the latter. The concept of estimating
this "dendrogram structure'' is introduced, and an approximate maximum
likelihood estimator (MLE) for the dendrogram structure is described. These
ideas are illustrated by a simple Monte Carlo simulation that, at least for
small data sets, suggests the method outperforms SLHC in the presence of noise.
Video Tracking Using Learned Hierarchical Features
Authors: Li Wang, Ting Liu, Gang Wang, Kap Luk Chan, Qingxiong Yang
Date: 2015-11-25 00:00:00
Tasks: Domain Adaptation, Network Embedding, Object, Object Tracking, Visual Object Tracking

In this paper, we propose an approach to learn hierarchical features for
visual object tracking. First, we offline learn features robust to diverse
motion patterns from auxiliary video sequences. The hierarchical features are
learned via a two-layer convolutional neural network. Embedding the temporal
slowness constraint in the stacked architecture makes the learned features
robust to complicated motion transformations, which is important for visual
object tracking. Then, given a target video sequence, we propose a domain
adaptation module to online adapt the pre-learned features according to the
specific target object. The adaptation is conducted in both layers of the deep
feature learning module so as to include appearance information of the specific
target object. As a result, the learned hierarchical features can be robust to
both complicated motion transformations and appearance changes of target
objects. We integrate our feature learning algorithm into three tracking
methods. Experimental results demonstrate that significant improvement can be
achieved using our learned hierarchical features, especially on video sequences
with complicated motion transformations.
Difference Target Propagation
Authors: Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio
Date: 2014-12-23 00:00:00

Back-propagation has been the workhorse of recent successes of deep learning
but it relies on infinitesimal effects (partial derivatives) in order to
perform credit assignment. This could become a serious issue as one considers
deeper and more non-linear functions, e.g., consider the extreme case of
nonlinearity where the relation between parameters and cost is actually
discrete. Inspired by the biological implausibility of back-propagation, a few
approaches have been proposed in the past that could play a similar credit
assignment role. In this spirit, we explore a novel approach to credit
assignment in deep networks that we call target propagation. The main idea is
to compute targets rather than gradients, at each layer. Like gradients, they
are propagated backwards. In a way that is related but different from
previously proposed proxies for back-propagation which rely on a backwards
network with symmetric weights, target propagation relies on auto-encoders at
each layer. Unlike back-propagation, it can be applied even when units exchange
stochastic bits rather than real numbers. We show that a linear correction for
the imperfectness of the auto-encoders, called difference target propagation,
is very effective to make target propagation actually work, leading to results
comparable to back-propagation for deep networks with discrete and continuous
units and denoising auto-encoders and achieving state of the art for stochastic
networks.
Pose-Guided Human Parsing with Deep Learned Features
Authors: Fangting Xia, Jun Zhu, Peng Wang, Alan Yuille
Date: 2015-08-17 00:00:00
Tasks: Human Parsing

Parsing human body into semantic regions is crucial to human-centric
analysis. In this paper, we propose a segment-based parsing pipeline that
explores human pose information, i.e. the joint location of a human model,
which improves the part proposal, accelerates the inference and regularizes the
parsing process at the same time. Specifically, we first generate part segment
proposals with respect to human joints predicted by a deep model, then part-
specific ranking models are trained for segment selection using both pose-based
features and deep-learned part potential features. Finally, the best ensemble
of the proposed part segments are inferred though an And-Or Graph.
  We evaluate our approach on the popular Penn-Fudan pedestrian parsing
dataset, and demonstrate the effectiveness of using the pose information for
each stage of the parsing pipeline. Finally, we show that our approach yields
superior part segmentation accuracy comparing to the state-of-the-art methods.
Shape and Symmetry Induction for 3D Objects
Authors: Shubham Tulsiani, Abhishek Kar, Qi-Xing Huang, João Carreira, Jitendra Malik
Date: 2015-11-24 00:00:00
Tasks: General Classification, Object

Actions as simple as grasping an object or navigating around it require a
rich understanding of that object's 3D shape from a given viewpoint. In this
paper we repurpose powerful learning machinery, originally developed for object
classification, to discover image cues relevant for recovering the 3D shape of
potentially unfamiliar objects. We cast the problem as one of local prediction
of surface normals and global detection of 3D reflection symmetry planes, which
open the door for extrapolating occluded surfaces from visible ones. We
demonstrate that our method is able to recover accurate 3D shape information
for classes of objects it was not trained on, in both synthetic and real
images.
Principal Basis Analysis in Sparse Representation
Authors: Hong Sun, Cheng-Wei Sang, Chen-Guang Liu
Date: 2015-11-25 00:00:00
Tasks: Denoising, Image Denoising

This article introduces a new signal analysis method, which can be
interpreted as a principal component analysis in sparse decomposition of the
signal. The method, called principal basis analysis, is based on a novel
criterion: reproducibility of component which is an intrinsic characteristic of
regularity in natural signals. We show how to measure reproducibility. Then we
present the principal basis analysis method, which chooses, in a sparse
representation of the signal, the components optimizing the reproducibility
degree to build the so-called principal basis. With this principal basis, we
show that the underlying signal pattern could be effectively extracted from
corrupted data. As illustration, we apply the principal basis analysis to image
denoising corrupted by Gaussian and non-Gaussian noises, showing better
performances than some reference methods at suppressing strong noise and at
preserving signal details.
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
Authors: Fayao Liu, Chunhua Shen, Guosheng Lin, Ian Reid
Date: 2015-02-26 00:00:00
Tasks: Depth Estimation

In this article, we tackle the problem of depth estimation from single
monocular images. Compared with depth estimation using multiple images such as
stereo depth perception, depth from monocular images is much more challenging.
Prior work typically focuses on exploiting geometric priors or additional
sources of information, most using hand-crafted features. Recently, there is
mounting evidence that features from deep convolutional neural networks (CNN)
set new records for various vision applications. On the other hand, considering
the continuous characteristic of the depth values, depth estimations can be
naturally formulated as a continuous conditional random field (CRF) learning
problem. Therefore, here we present a deep convolutional neural field model for
estimating depths from single monocular images, aiming to jointly explore the
capacity of deep CNN and continuous CRF. In particular, we propose a deep
structured learning scheme which learns the unary and pairwise potentials of
continuous CRF in a unified deep CNN framework. We then further propose an
equally effective model based on fully convolutional networks and a novel
superpixel pooling method, which is $\sim 10$ times faster, to speedup the
patch-wise convolutions in the deep model. With this more efficient model, we
are able to design deeper networks to pursue better performance. Experiments on
both indoor and outdoor scene datasets demonstrate that the proposed method
outperforms state-of-the-art depth estimation approaches.
Context-aware CNNs for person head detection
Authors: Tuan-Hung Vu, Anton Osokin, Ivan Laptev
Date: 2015-11-24 00:00:00
Proceeding: ICCV 2015 12
Tasks: Face Detection, Head Detection, Human Detection

Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.