pwc-archive/papers-with-abstracts huggingface dataset:
Robust Hashing for Multi-View Data: Jointly Learning Low-Rank Kernelized Similarity Consensus and Hash Functions
Authors: Lin Wu, Yang Wang
Date: 2016-11-17 00:00:00
Tasks: graph construction

Learning hash functions/codes for similarity search over multi-view data is
attracting increasing attention, where similar hash codes are assigned to the
data objects characterizing consistently neighborhood relationship across
views. Traditional methods in this category inherently suffer three
limitations: 1) they commonly adopt a two-stage scheme where similarity matrix
is first constructed, followed by a subsequent hash function learning; 2) these
methods are commonly developed on the assumption that data samples with
multiple representations are noise-free,which is not practical in real-life
applications; 3) they often incur cumbersome training model caused by the
neighborhood graph construction using all $N$ points in the database ($O(N)$).
In this paper, we motivate the problem of jointly and efficiently training the
robust hash functions over data objects with multi-feature representations
which may be noise corrupted. To achieve both the robustness and training
efficiency, we propose an approach to effectively and efficiently learning
low-rank kernelized \footnote{We use kernelized similarity rather than kernel,
as it is not a squared symmetric matrix for data-landmark affinity matrix.}
hash functions shared across views. Specifically, we utilize landmark graphs to
construct tractable similarity matrices in multi-views to automatically
discover neighborhood structure in the data. To learn robust hash functions, a
latent low-rank kernel function is used to construct hash functions in order to
accommodate linearly inseparable data. In particular, a latent kernelized
similarity matrix is recovered by rank minimization on multiple kernel-based
similarity matrices. Extensive experiments on real-world multi-view datasets
validate the efficacy of our method in the presence of error corruptions.
Moving Target Defense for Web Applications using Bayesian Stackelberg Games
Authors: Sailik Sengupta, Satya Gautam Vadlamudi, Subbarao Kambhampati, Marthony Taguinod, Adam Doupé, Ziming Zhao, Gail-Joon Ahn
Date: 2016-02-23 00:00:00

The present complexity in designing web applications makes software security
a difficult goal to achieve. An attacker can explore a deployed service on the
web and attack at his/her own leisure. Moving Target Defense (MTD) in web
applications is an effective mechanism to nullify this advantage of their
reconnaissance but the framework demands a good switching strategy when
switching between multiple configurations for its web-stack. To address this
issue, we propose modeling of a real-world MTD web application as a repeated
Bayesian game. We then formulate an optimization problem that generates an
effective switching strategy while considering the cost of switching between
different web-stack configurations. To incorporate this model into a developed
MTD system, we develop an automated system for generating attack sets of Common
Vulnerabilities and Exposures (CVEs) for input attacker types with predefined
capabilities. Our framework obtains realistic reward values for the players
(defenders and attackers) in this game by using security domain expertise on
CVEs obtained from the National Vulnerability Database (NVD). We also address
the issue of prioritizing vulnerabilities that when fixed, improves the
security of the MTD system. Lastly, we demonstrate the robustness of our
proposed model by evaluating its performance when there is uncertainty about
input attacker information.
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Authors: Yu Liu, Yanming Guo, Michael S. Lew
Date: 2016-11-16 00:00:00
Tasks: Image Retrieval, Retrieval, Scene Recognition

Despite recent advances in multi-scale deep representations, their
limitations are attributed to expensive parameters and weak fusion modules.
Hence, we propose an efficient approach to fuse multi-scale deep
representations, called convolutional fusion networks (CFN). Owing to using
1$\times$1 convolution and global average pooling, CFN can efficiently generate
the side branches while adding few parameters. In addition, we present a
locally-connected fusion module, which can learn adaptive weights for the side
branches and form a discriminatively fused feature. CFN models trained on the
CIFAR and ImageNet datasets demonstrate remarkable improvements over the plain
CNNs. Furthermore, we generalize CFN to three new tasks, including scene
recognition, fine-grained recognition and image retrieval. Our experiments show
that it can obtain consistent improvements towards the transferring tasks.
Semantic Regularisation for Recurrent Image Annotation
Authors: Feng Liu, Tao Xiang, Timothy M. Hospedales, Wankou Yang, Changyin Sun
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: General Classification, Image Captioning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

The "CNN-RNN" design pattern is increasingly widely applied in a variety of
image annotation tasks including multi-label classification and captioning.
Existing models use the weakly semantic CNN hidden layer or its transform as
the image embedding that provides the interface between the CNN and RNN. This
leaves the RNN overstretched with two jobs: predicting the visual concepts and
modelling their correlations for generating structured annotation output.
Importantly this makes the end-to-end training of the CNN and RNN slow and
ineffective due to the difficulty of back propagating gradients through the RNN
to train the CNN. We propose a simple modification to the design pattern that
makes learning more effective and efficient. Specifically, we propose to use a
semantically regularised embedding layer as the interface between the CNN and
RNN. Regularising the interface can partially or completely decouple the
learning problems, allowing each to be more effectively trained and jointly
training much more efficient. Extensive experiments show that state-of-the art
performance is achieved on multi-label classification as well as image
captioning.
Solving Cold-Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
Authors: Jianbo Yuan, Walid Shalaby, Mohammed Korayem, David Lin, Khalifeh Aljadda, Jiebo Luo
Date: 2016-11-16 00:00:00
Tasks: Collaborative Filtering

Collaborative Filtering (CF) is widely used in large-scale recommendation
engines because of its efficiency, accuracy and scalability. However, in
practice, the fact that recommendation engines based on CF require interactions
between users and items before making recommendations, make it inappropriate
for new items which haven't been exposed to the end users to interact with.
This is known as the cold-start problem. In this paper we introduce a novel
approach which employs deep learning to tackle this problem in any CF based
recommendation engine. One of the most important features of the proposed
technique is the fact that it can be applied on top of any existing CF based
recommendation engine without changing the CF core. We successfully applied
this technique to overcome the item cold-start problem in Careerbuilder's CF
based recommendation engine. Our experiments show that the proposed technique
is very efficient to resolve the cold-start problem while maintaining high
accuracy of the CF recommendations.
Probabilistic Fluorescence-Based Synapse Detection
Authors: Anish K. Simhal, Cecilia Aguerrebere, Forrest Collman, Joshua T. Vogelstein, Kristina D. Micheva, Richard J. Weinberg, Stephen J. Smith, Guillermo Sapiro
Date: 2016-11-16 00:00:00

Brain function results from communication between neurons connected by
complex synaptic networks. Synapses are themselves highly complex and diverse
signaling machines, containing protein products of hundreds of different genes,
some in hundreds of copies, arranged in precise lattice at each individual
synapse. Synapses are fundamental not only to synaptic network function but
also to network development, adaptation, and memory. In addition, abnormalities
of synapse numbers or molecular components are implicated in most mental and
neurological disorders. Despite their obvious importance, mammalian synapse
populations have so far resisted detailed quantitative study. In human brains
and most animal nervous systems, synapses are very small and very densely
packed: there are approximately 1 billion synapses per cubic millimeter of
human cortex. This volumetric density poses very substantial challenges to
proteometric analysis at the critical level of the individual synapse. The
present work describes new probabilistic image analysis methods for
single-synapse analysis of synapse populations in both animal and human brains.
Self-calibration-based Approach to Critical Motion Sequences of Rolling-shutter Structure from Motion
Authors: Eisuke Ito, Takayuki Okatani
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7

In this paper we consider critical motion sequences (CMSs) of rolling-shutter
(RS) SfM. Employing an RS camera model with linearized pure rotation, we show
that the RS distortion can be approximately expressed by two internal
parameters of an "imaginary" camera plus one-parameter nonlinear transformation
similar to lens distortion. We then reformulate the problem as self-calibration
of the imaginary camera, in which its skew and aspect ratio are unknown and
varying in the image sequence. In the formulation, we derive a general
representation of CMSs. We also show that our method can explain the CMS that
was recently reported in the literature, and then present a new remedy to deal
with the degeneracy. Our theoretical results agree well with experimental
results; it explains degeneracies observed when we employ naive bundle
adjustment, and how they are resolved by our method.
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Authors: Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B. Viégas, Martin Wattenberg
Date: 2016-11-16 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Embeddings are ubiquitous in machine learning, appearing in recommender
systems, NLP, and many other applications. Researchers and developers often
need to explore the properties of a specific embedding, and one way to analyze
embeddings is to visualize them. We present the Embedding Projector, a tool for
interactive visualization and interpretation of embeddings.
ROS Regression: Integrating Regularization and Optimal Scaling Regression
Authors: Jacqueline J. Meulman, Anita J. van der Kooij
Date: 2016-11-16 00:00:00
Tasks: regression

In this paper we combine two important extensions of ordinary least squares
regression: regularization and optimal scaling. Optimal scaling (sometimes also
called optimal scoring) has originally been developed for categorical data, and
the process finds quantifications for the categories that are optimal for the
regression model in the sense that they maximize the multiple correlation.
Although the optimal scaling method was developed initially for variables with
a limited number of categories, optimal transformations of continuous variables
are a special case. We will consider a variety of transformation types;
typically we use step functions for categorical variables, and smooth (spline)
functions for continuous variables. Both types of functions can be restricted
to be monotonic, preserving the ordinal information in the data. In addition to
optimal scaling, three regularization methods will be considered: Ridge
regression, the Lasso, and the Elastic Net. The resulting method will be called
ROS Regression (Regularized Optimal Scaling Regression. We will show that the
basic OS algorithm provides straightforward and efficient estimation of the
regularized regression coefficients, automatically gives the Group Lasso and
Blockwise Sparse Regression, and extends them with monotonicity properties. We
will show that Optimal Scaling linearizes nonlinear relationships between
predictors and outcome, and improves upon the condition of the predictor
correlation matrix, increasing (on average) the conditional independence of the
predictors. Alternative options for regularization of either regression
coefficients or category quantifications are mentioned. Extended examples are
provided.
  Keywords: Categorical Data, Optimal Scaling, Conditional Independence, Step
Functions, Splines, Monotonic Transformations, Regularization, Lasso, Elastic
Net, Group Lasso, Blockwise Sparse Regression.
ProjE: Embedding Projection for Knowledge Graph Completion
Authors: Baoxu Shi, Tim Weninger
Date: 2016-11-16 00:00:00
Tasks: Fact Checking, Feature Engineering, Knowledge Graph Completion

With the large volume of new information created every day, determining the
validity of information in a knowledge graph and filling in its missing parts
are crucial tasks for many researchers and practitioners. To address this
challenge, a number of knowledge graph completion methods have been developed
using low-dimensional graph embeddings. Although researchers continue to
improve these models using an increasingly complex feature space, we show that
simple changes in the architecture of the underlying model can outperform
state-of-the-art models without the need for complex feature engineering. In
this work, we present a shared variable neural network model called ProjE that
fills-in missing information in a knowledge graph by learning joint embeddings
of the knowledge graph's entities and edges, and through subtle, but important,
changes to the standard loss function. In doing so, ProjE has a parameter size
that is smaller than 11 out of 15 existing methods while performing $37\%$
better than the current-best method on standard datasets. We also show, via a
new fact checking task, that ProjE is capable of accurately determining the
veracity of many declarative statements.
ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels
Authors: Huaijin Chen, Suren Jayasuriya, Jiyue Yang, Judy Stephen, Sriram Sivaramakrishnan, Ashok Veeraraghavan, Alyosha Molnar
Date: 2016-05-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Face Identification, Object Recognition

Deep learning using convolutional neural networks (CNNs) is quickly becoming
the state-of-the-art for challenging computer vision applications. However,
deep learning's power consumption and bandwidth requirements currently limit
its application in embedded and mobile systems with tight energy budgets. In
this paper, we explore the energy savings of optically computing the first
layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs),
custom CMOS diffractive image sensors which act similar to Gabor filter banks
in the V1 layer of the human visual cortex. ASPs replace both image sensing and
the first layer of a conventional CNN by directly performing optical edge
filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our
experimental results (both on synthetic data and a hardware prototype) for a
variety of vision tasks such as digit recognition, object recognition, and face
identification demonstrate using ASPs while achieving similar performance
compared to traditional deep learning pipelines.
A Semidefinite Program for Structured Blockmodels
Authors: David Choi
Date: 2016-11-16 00:00:00
Tasks: Community Detection

Semidefinite programs have recently been developed for the problem of
community detection, which may be viewed as a special case of the stochastic
blockmodel. Here, we develop a semidefinite program that can be tailored to
other instances of the blockmodel, such as non-assortative networks and
overlapping communities. We establish label recovery in sparse settings, with
conditions that are analogous to recent results for community detection. In
settings where the data is not generated by a blockmodel, we give an oracle
inequality that bounds excess risk relative to the best blockmodel
approximation. Simulations are presented for community detection, for
overlapping communities, and for latent space models.
Reinforcement Learning with Unsupervised Auxiliary Tasks
Authors: Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu
Date: 2016-11-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning agents have achieved state-of-the-art results by
directly maximising cumulative reward. However, environments contain a much
wider variety of possible training signals. In this paper, we introduce an
agent that also maximises many other pseudo-reward functions simultaneously by
reinforcement learning. All of these tasks share a common representation that,
like unsupervised learning, continues to develop in the absence of extrinsic
rewards. We also introduce a novel mechanism for focusing this representation
upon extrinsic rewards, so that learning can rapidly adapt to the most relevant
aspects of the actual task. Our agent significantly outperforms the previous
state-of-the-art on Atari, averaging 880\% expert human performance, and a
challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks
leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert
human performance on Labyrinth.
PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems
Authors: Prof. Roger K. Moore
Date: 2016-11-16 00:00:00

Recent years have witnessed increasing interest in the potential benefits of
`intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot,
iRobot's Roomba robot vacuum cleaner and Google's driverless cars have fired
the imagination of the general public, and social media buzz with speculation
about a utopian world of helpful robot assistants or the coming robot
apocalypse! However, there is a long way to go before autonomous systems reach
the level of capabilities required for even the simplest of tasks involving
human-robot interaction - especially if it involves communicative behaviour
such as speech and language. Of course the field of Artificial Intelligence
(AI) has made great strides in these areas, and has moved on from abstract
high-level rule-based paradigms to embodied architectures whose operations are
grounded in real physical environments. What is still missing, however, is an
overarching theory of intelligent communicative behaviour that informs
system-level design decisions in order to provide a more coherent approach to
system integration. This chapter introduces the beginnings of such a framework
inspired by the principles of Perceptual Control Theory (PCT). In particular,
it is observed that PCT has hitherto tended to view perceptual processes as a
relatively straightforward series of transformations from sensation to
perception, and has overlooked the potential of powerful generative model-based
solutions that have emerged in practical fields such as visual or auditory
scene analysis. Starting from first principles, a sequence of arguments is
presented which not only shows how these ideas might be integrated into PCT,
but which also extend PCT towards a remarkably symmetric architecture for a
needs-driven communicative agent. It is concluded that, if behaviour is the
control of perception, then perception is the simulation of behaviour.
Spectral Convolution Networks
Authors: Maria Francesca, Arthur Hughes, David Gregg
Date: 2016-11-16 00:00:00

Previous research has shown that computation of convolution in the frequency
domain provides a significant speedup versus traditional convolution network
implementations. However, this performance increase comes at the expense of
repeatedly computing the transform and its inverse in order to apply other
network operations such as activation, pooling, and dropout. We show,
mathematically, how convolution and activation can both be implemented in the
frequency domain using either the Fourier or Laplace transformation. The main
contributions are a description of spectral activation under the Fourier
transform and a further description of an efficient algorithm for computing
both convolution and activation under the Laplace transform. By computing both
the convolution and activation functions in the frequency domain, we can reduce
the number of transforms required, as well as reducing overall complexity. Our
description of a spectral activation function, together with existing spectral
analogs of other network functions may then be used to compose a fully spectral
implementation of a convolution network.
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
Authors: Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogerio Feris
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Attribute, General Classification, Multi-Task Learning

Multi-task learning aims to improve generalization performance of multiple
prediction tasks by appropriately sharing relevant information across them. In
the context of deep neural networks, this idea is often realized by
hand-designed network architectures with layers that are shared across tasks
and branches that encode task-specific features. However, the space of possible
multi-task deep architectures is combinatorially large and often the final
architecture is arrived at by manual exploration of this space subject to
designer's bias, which can be both error-prone and tedious. In this work, we
propose a principled approach for designing compact multi-task deep learning
architectures. Our approach starts with a thin network and dynamically widens
it in a greedy manner during training using a novel criterion that promotes
grouping of similar tasks together. Our Extensive evaluation on person
attributes classification tasks involving facial and clothing attributes
suggests that the models produced by the proposed method are fast, compact and
can closely match or exceed the state-of-the-art accuracy from strong baselines
by much more expensive models.
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Scarcity
Authors: Vinayak Athavale, Shreenivas Bharadwaj, Monik Pamecha, Ameya Prabhu, Manish Shrivastava
Date: 2016-10-31 00:00:00
Tasks: Morphological Analysis, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper we describe an end to end Neural Model for Named Entity
Recognition NER) which is based on Bi-Directional RNN-LSTM. Almost all NER
systems for Hindi use Language Specific features and handcrafted rules with
gazetteers. Our model is language independent and uses no domain specific
features or any handcrafted rules. Our models rely on semantic information in
the form of word vectors which are learnt by an unsupervised learning algorithm
on an unannotated corpus. Our model attained state of the art performance in
both English and Hindi without the use of any morphological analysis or without
using gazetteers of any sort.