pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative Training of Tensors for Compositional Distributional Semantics
Authors: Tamara Polajnar
Date: 2016-07-08 00:00:00
Tasks: Zero-Shot Learning

Type-based compositional distributional semantic models present an
interesting line of research into functional representations of linguistic
meaning. One of the drawbacks of such models, however, is the lack of training
data required to train each word-type combination. In this paper we address
this by introducing training methods that share parameters between similar
words. We show that these methods enable zero-shot learning for words that have
no training data at all, as well as enabling construction of high-quality
tensors from very few training examples per word.
The GPU-based Parallel Ant Colony System
Authors: Rafał Skinderowicz
Date: 2016-05-09 00:00:00
Tasks: CPU, GPU

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the
MAX-MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms
inspired by the behavior of ants. In this article we present three novel
parallel versions of the ACS for the graphics processing units (GPUs). To the
best of our knowledge, this is the first such work on the ACS which shares many
key elements of the ACO and the MMAS, but differences in the process of
building solutions and updating the pheromone trails make obtaining an
efficient parallel version for the GPUs a difficult task. The proposed parallel
versions of the ACS differ mainly in their implementations of the pheromone
memory. The first two use the standard pheromone matrix, and the third uses a
novel selective pheromone memory. Computational experiments conducted on
several Travelling Salesman Problem (TSP) instances of sizes ranging from 198
to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536
CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS
running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the
selective pheromone memory achieved speedups up to 16.85x, but in most cases
the obtained solutions were of significantly better quality than for the
sequential ACS.
Generalized support vector regression: duality and tensor-kernel representation
Authors: Saverio Salzo, Johan A. K. Suykens
Date: 2016-03-18 00:00:00
Tasks: regression

In this paper we study the variational problem associated to support vector
regression in Banach function spaces. Using the Fenchel-Rockafellar duality
theory, we give explicit formulation of the dual problem as well as of the
related optimality conditions. Moreover, we provide a new computational
framework for solving the problem which relies on a tensor-kernel
representation. This analysis overcomes the typical difficulties connected to
learning in Banach spaces. We finally present a large class of tensor-kernels
to which our theory fully applies: power series tensor kernels. This type of
kernels describe Banach spaces of analytic functions and include
generalizations of the exponential and polynomial kernels as well as, in the
complex case, generalizations of the Szeg\"o and Bergman kernels.
Safety Verification of Deep Neural Networks
Authors: Xiaowei Huang, Marta Kwiatkowska, Sen Wang, Min Wu
Date: 2016-10-21 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, General Classification, image-classification, Image Classification, Self-Driving Cars

Deep neural networks have achieved impressive experimental results in image
classification, but can surprisingly be unstable with respect to adversarial
perturbations, that is, minimal changes to the input image that cause the
network to misclassify it. With potential applications including perception
modules and end-to-end controllers for self-driving cars, this raises concerns
about their safety. We develop a novel automated verification framework for
feed-forward multi-layer neural networks based on Satisfiability Modulo Theory
(SMT). We focus on safety of image classification decisions with respect to
image manipulations, such as scratches or changes to camera angle or lighting
conditions that would result in the same class being assigned by a human, and
define safety for an individual decision in terms of invariance of the
classification within a small neighbourhood of the original image. We enable
exhaustive search of the region by employing discretisation, and propagate the
analysis layer by layer. Our method works directly with the network code and,
in contrast to existing methods, can guarantee that adversarial examples, if
they exist, are found for the given region and family of manipulations. If
found, adversarial examples can be shown to human testers and/or used to
fine-tune the network. We implement the techniques using Z3 and evaluate them
on state-of-the-art networks, including regularised and deep learning networks.
We also compare against existing techniques to search for adversarial examples
and estimate network robustness.
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Authors: Noureldien Hussein, Efstratios Gavves, Arnold W. M. Smeulders
Date: 2017-05-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Event Detection, Metric Learning, Retrieval, Video Retrieval

Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
  Related works train a bank of concept detectors on external data sources.
These detectors predict confidence scores for test videos, which are ranked and
retrieved accordingly. In contrast, we learn a joint space in which the visual
and textual representations are embedded. The space casts a novel event as a
probability of pre-defined events. Also, it learns to measure the distance
between an event and its related videos.
  Our model is trained end-to-end on publicly available EventNet. When applied
to TRECVID Multimedia Event Detection dataset, it outperforms the
state-of-the-art by a considerable margin.
Part-based Deep Hashing for Large-scale Person Re-identification
Authors: Fuqing Zhu, Xiangwei Kong, Liang Zheng, Haiyan Fu, Qi Tian
Date: 2017-05-05 00:00:00
Tasks: Deep Hashing, Large-Scale Person Re-Identification, Person Re-Identification, Triplet

Large-scale is a trend in person re-identification (re-id). It is important
that real-time search be performed in a large gallery. While previous methods
mostly focus on discriminative learning, this paper makes the attempt in
integrating deep learning and hashing into one framework to evaluate the
efficiency and accuracy for large-scale person re-id. We integrate spatial
information for discriminative visual representation by partitioning the
pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing
(PDH) is proposed, in which batches of triplet samples are employed as the
input of the deep hashing architecture. Each triplet sample contains two
pedestrian images (or parts) with the same identity and one pedestrian image
(or part) of the different identity. A triplet loss function is employed with a
constraint that the Hamming distance of pedestrian images (or parts) with the
same identity is smaller than ones with the different identity. In the
experiment, we show that the proposed Part-based Deep Hashing method yields
very competitive re-id accuracy on the large-scale Market-1501 and
Market-1501+500K datasets.
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
Authors: Antonio D'Innocente, Fabio Maria Carlucci, Mirco Colosi, Barbara Caputo
Date: 2017-05-05 00:00:00
Tasks: Data Augmentation, Object, object-detection, Object Detection, Object Recognition

Despite the impressive progress brought by deep network in visual object
recognition, robot vision is still far from being a solved problem. The most
successful convolutional architectures are developed starting from ImageNet, a
large scale collection of images of object categories downloaded from the Web.
This kind of images is very different from the situated and embodied visual
experience of robots deployed in unconstrained settings. To reduce the gap
between these two visual experiences, this paper proposes a simple yet
effective data augmentation layer that zooms on the object of interest and
simulates the object detection outcome of a robot vision system. The layer,
that can be used with any convolutional deep architecture, brings to an
increase in object recognition performance of up to 7\%, in experiments
performed over three different benchmark databases. Upon acceptance of the
paper, our robot data augmentation layer will be made publicly available.
Joint RNN Model for Argument Component Boundary Detection
Authors: Minglan Li, Yang Gao, Hui Wen, Yang Du, Haijing Liu, Hao Wang
Date: 2017-05-05 00:00:00
Tasks: Boundary Detection, Feature Engineering, model, Sentence

Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.
Source-Sensitive Belief Change
Authors: Shahab Ebrahimi
Date: 2017-04-11 00:00:00
Tasks: Position

The AGM model is the most remarkable framework for modeling belief revision.
However, it is not perfect in all aspects. Paraconsistent belief revision,
multi-agent belief revision and non-prioritized belief revision are three
different extensions to AGM to address three important criticisms applied to
it. In this article, we propose a framework based on AGM that takes a position
in each of these categories. Also, we discuss some features of our framework
and study the satisfiability of AGM postulates in this new context.
Phase Congruency Parameter Optimization for Enhanced Detection of Image Features for both Natural and Medical Applications
Authors: Seyed Mohammad Mahdi Alavi, Yunyan Zhang
Date: 2017-05-05 00:00:00

Following the presentation and proof of the hypothesis that image features
are particularly perceived at points where the Fourier components are maximally
in phase, the concept of phase congruency (PC) is introduced. Subsequently, a
two-dimensional multi-scale phase congruency (2D-MSPC) is developed, which has
been an important tool for detecting and evaluation of image features. However,
the 2D-MSPC requires many parameters to be appropriately tuned for optimal
image features detection. In this paper, we defined a criterion for parameter
optimization of the 2D-MSPC, which is a function of its maximum and minimum
moments. We formulated the problem in various optimal and suboptimal
frameworks, and discussed the conditions and features of the suboptimal
solutions. The effectiveness of the proposed method was verified through
several examples, ranging from natural objects to medical images from patients
with a neurological disease, multiple sclerosis.
Characterizing and Improving Stability in Neural Style Transfer
Authors: Agrim Gupta, Justin Johnson, Alexandre Alahi, Li Fei-Fei
Date: 2017-05-05 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Style Transfer, Video Style Transfer

Recent progress in style transfer on images has focused on improving the
quality of stylized images and speed of methods. However, real-time methods are
highly unstable resulting in visible flickering when applied to videos. In this
work we characterize the instability of these methods by examining the solution
set of the style transfer objective. We show that the trace of the Gram matrix
representing style is inversely related to the stability of the method. Then,
we present a recurrent convolutional network for real-time video style transfer
which incorporates a temporal consistency loss and overcomes the instability of
prior methods. Our networks can be applied at any resolution, do not re- quire
optical flow at test time, and produce high quality, temporally consistent
stylized videos in real-time.
Learning to see people like people
Authors: Amanda Song, Linjie Li, Chad Atalla, Garrison Cottrell
Date: 2017-05-05 00:00:00

Humans make complex inferences on faces, ranging from objective properties
(gender, ethnicity, expression, age, identity, etc) to subjective judgments
(facial attractiveness, trustworthiness, sociability, friendliness, etc). While
the objective aspects of face perception have been extensively studied,
relatively fewer computational models have been developed for the social
impressions of faces. Bridging this gap, we develop a method to predict human
impressions of faces in 40 subjective social dimensions, using deep
representations from state-of-the-art neural networks. We find that model
performance grows as the human consensus on a face trait increases, and that
model predictions outperform human groups in correlation with human averages.
This illustrates the learnability of subjective social perception of faces,
especially when there is high human consensus. Our system can be used to decide
which photographs from a personal collection will make the best impression. The
results are significant for the field of social robotics, demonstrating that
robots can learn the subjective judgments defining the underlying fabric of
human interaction.
Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses
Authors: Shravan Vasishth, Nicolas Chopin, Robin Ryder, Bruno Nicenboim
Date: 2017-02-02 00:00:00
Tasks: Retrieval, Sentence

We present a case-study demonstrating the usefulness of Bayesian hierarchical
mixture modelling for investigating cognitive processes. In sentence
comprehension, it is widely assumed that the distance between linguistic
co-dependents affects the latency of dependency resolution: the longer the
distance, the longer the retrieval time (the distance-based account). An
alternative theory, direct-access, assumes that retrieval times are a mixture
of two distributions: one distribution represents successful retrievals (these
are independent of dependency distance) and the other represents an initial
failure to retrieve the correct dependent, followed by a reanalysis that leads
to successful retrieval. We implement both models as Bayesian hierarchical
models and show that the direct-access model explains Chinese relative clause
reading time data better than the distance account.
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
Authors: Wenhui Li, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, Mohan Kankanhalli
Date: 2016-07-21 00:00:00
Tasks: Action Recognition, Benchmarking, Temporal Action Localization

Action recognition has received increasing attention from the computer vision
and machine learning communities in the last decade. To enable the study of
this problem, there exist a vast number of action datasets, which are recorded
under controlled laboratory settings, real-world surveillance environments, or
crawled from the Internet. Apart from the "in-the-wild" datasets, the training
and test split of conventional datasets often possess similar environments
conditions, which leads to close to perfect performance on constrained
datasets. In this paper, we introduce a new dataset, namely Multi-Camera Action
Dataset (MCAD), which is designed to evaluate the open view classification
problem under the surveillance environment. In total, MCAD contains 14,298
action samples from 18 action categories, which are performed by 20 subjects
and independently recorded with 5 cameras. Inspired by the well received
evaluation approach on the LFW dataset, we designed a standard evaluation
protocol and benchmarked MCAD under several scenarios. The benchmark shows that
while an average of 85% accuracy is achieved under the closed-view scenario,
the performance suffers from a significant drop under the cross-view scenario.
In the worst case scenario, the performance of 10-fold cross validation drops
from 87.0% to 47.4%.
Motion Prediction Under Multimodality with Conditional Stochastic Networks
Authors: Katerina Fragkiadaki, Jonathan Huang, Alex Alemi, Sudheendra Vijayanarasimhan, Susanna Ricco, Rahul Sukthankar
Date: 2017-05-05 00:00:00
Tasks: motion prediction, Optical Flow Estimation, Prediction, Trajectory Prediction, Video Prediction

Given a visual history, multiple future outcomes for a video scene are
equally probable, in other words, the distribution of future outcomes has
multiple modes. Multimodality is notoriously hard to handle by standard
regressors or classifiers: the former regress to the mean and the latter
discretize a continuous high dimensional output space. In this work, we present
stochastic neural network architectures that handle such multimodality through
stochasticity: future trajectories of objects, body joints or frames are
represented as deep, non-linear transformations of random (as opposed to
deterministic) variables. Such random variables are sampled from simple
Gaussian distributions whose means and variances are parametrized by the output
of convolutional encoders over the visual history. We introduce novel
convolutional architectures for predicting future body joint trajectories that
outperform fully connected alternatives \cite{DBLP:journals/corr/WalkerDGH16}.
We introduce stochastic spatial transformers through optical flow warping for
predicting future frames, which outperform their deterministic equivalents
\cite{DBLP:journals/corr/PatrauceanHC15}. Training stochastic networks involves
an intractable marginalization over stochastic variables. We compare various
training schemes that handle such marginalization through a) straightforward
sampling from the prior, b) conditional variational autoencoders
\cite{NIPS2015_5775,DBLP:journals/corr/WalkerDGH16}, and, c) a proposed
K-best-sample loss that penalizes the best prediction under a fixed "prediction
budget". We show experimental results on object trajectory prediction, human
body joint trajectory prediction and video prediction under varying future
uncertainty, validating quantitatively and qualitatively our architectural
choices and training schemes.
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Authors: Mengxue Li, Shiqiang Geng, Yang Gao, Haijing Liu, Hao Wang
Date: 2017-05-05 00:00:00

Argumentation mining aims at automatically extracting the premises-claim
discourse structures in natural language texts. There is a great demand for
argumentation corpora for customer reviews. However, due to the controversial
nature of the argumentation annotation task, there exist very few large-scale
argumentation corpora for customer reviews. In this work, we novelly use the
crowdsourcing technique to collect argumentation annotations in Chinese hotel
reviews. As the first Chinese argumentation dataset, our corpus includes 4814
argument component annotations and 411 argument relation annotations, and its
annotations qualities are comparable to some widely used argumentation corpora
in other languages.
Re-ranking Person Re-identification with k-reciprocal Encoding
Authors: Zhun Zhong, Liang Zheng, Donglin Cao, Shaozi Li
Date: 2017-01-29 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification, Re-Ranking, Retrieval

When considering person re-identification (re-ID) as a retrieval process,
re-ranking is a critical step to improve its accuracy. Yet in the re-ID
community, limited effort has been devoted to re-ranking, especially those
fully automatic, unsupervised solutions. In this paper, we propose a
k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is
that if a gallery image is similar to the probe in the k-reciprocal nearest
neighbors, it is more likely to be a true match. Specifically, given an image,
a k-reciprocal feature is calculated by encoding its k-reciprocal nearest
neighbors into a single vector, which is used for re-ranking under the Jaccard
distance. The final distance is computed as the combination of the original
distance and the Jaccard distance. Our re-ranking method does not require any
human interaction or any labeled data, so it is applicable to large-scale
datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW
datasets confirm the effectiveness of our method.
Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption
Authors: Jie Liu, Martin Takac
Date: 2016-12-16 00:00:00
Tasks: BIG-bench Machine Learning

We propose a projected semi-stochastic gradient descent method with
mini-batch for improving both the theoretical complexity and practical
performance of the general stochastic gradient descent method (SGD). We are
able to prove linear convergence under weak strong convexity assumption. This
requires no strong convexity assumption for minimizing the sum of smooth convex
functions subject to a compact polyhedral set, which remains popular across
machine learning community. Our PS2GD preserves the low-cost per iteration and
high optimization accuracy via stochastic gradient variance-reduced technique,
and admits a simple parallel implementation with mini-batches. Moreover, PS2GD
is also applicable to dual problem of SVM with hinge loss.
Spherical Cap Packing Asymptotics and Rank-Extreme Detection
Authors: Kai Zhang
Date: 2015-11-19 00:00:00

We study the spherical cap packing problem with a probabilistic approach.
Such probabilistic considerations result in an asymptotic sharp universal
uniform bound on the maximal inner product between any set of unit vectors and
a stochastically independent uniformly distributed unit vector. When the set of
unit vectors are themselves independently uniformly distributed, we further
develop the extreme value distribution limit of the maximal inner product,
which characterizes its uncertainty around the bound.
  As applications of the above asymptotic results, we derive (1) an asymptotic
sharp universal uniform bound on the maximal spurious correlation, as well as
its uniform convergence in distribution when the explanatory variables are
independently Gaussian distributed; and (2) an asymptotic sharp universal bound
on the maximum norm of a low-rank elliptically distributed vector, as well as
related limiting distributions. With these results, we develop a fast detection
method for a low-rank structure in high-dimensional Gaussian data without using
the spectrum information.