pwc-archive/papers-with-abstracts huggingface dataset:
QBDC: Query by dropout committee for training deep supervised architecture
Authors: Melanie Ducoffe, Frederic Precioso
Date: 2015-11-19 00:00:00
Tasks: Active Learning

While the current trend is to increase the depth of neural networks to
increase their performance, the size of their training database has to grow
accordingly. We notice an emergence of tremendous databases, although providing
labels to build a training set still remains a very expensive task. We tackle
the problem of selecting the samples to be labelled in an online fashion. In
this paper, we present an active learning strategy based on query by committee
and dropout technique to train a Convolutional Neural Network (CNN). We derive
a commmittee of partial CNNs resulting from batchwise dropout runs on the
initial CNN. We evaluate our active learning strategy for CNN on MNIST
benchmark, showing in particular that selecting less than 30 % from the
annotated database is enough to get similar error rate as using the full
training set on MNIST. We also studied the robustness of our method against
adversarial examples.
On randomization of neural networks as a form of post-learning strategy
Authors: K. G. Kapanova, I. Dimov, J. M. Sellier
Date: 2015-11-26 00:00:00
Tasks: Form

Today artificial neural networks are applied in various fields - engineering,
data analysis, robotics. While they represent a successful tool for a variety
of relevant applications, mathematically speaking they are still far from being
conclusive. In particular, they suffer from being unable to find the best
configuration possible during the training process (local minimum problem). In
this paper, we focus on this issue and suggest a simple, but effective,
post-learning strategy to allow the search for improved set of weights at a
relatively small extra computational cost. Therefore, we introduce a novel
technique based on analogy with quantum effects occurring in nature as a way to
improve (and sometimes overcome) this problem. Several numerical experiments
are presented to validate the approach.
Reinforcement Learning with Parameterized Actions
Authors: Warwick Masson, Pravesh Ranchod, George Konidaris
Date: 2015-09-05 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a model-free algorithm for learning in Markov decision processes
with parameterized actions-discrete actions with continuous parameters. At each
step the agent must select both which action to use and which parameters to use
with that action. We introduce the Q-PAMDP algorithm for learning in these
domains, show that it converges to a local optimum, and compare it to direct
policy search in the goal-scoring and Platform domains.
A global Constraint for mining Sequential Patterns with GAP constraint
Authors: Amina Kemmar, Samir Loudni, Yahia Lebbah, Patrice Boizumault, Thierry Charnois
Date: 2015-11-26 00:00:00
Tasks: Sequential Pattern Mining

Sequential pattern mining (SPM) under gap constraint is a challenging task.
Many efficient specialized methods have been developed but they are all
suffering from a lack of genericity. The Constraint Programming (CP) approaches
are not so effective because of the size of their encodings. In[7], we have
proposed the global constraint Prefix-Projection for SPM which remedies to this
drawback. However, this global constraint cannot be directly extended to
support gap constraint. In this paper, we propose the global constraint GAP-SEQ
enabling to handle SPM with or without gap constraint. GAP-SEQ relies on the
principle of right pattern extensions. Experiments show that our approach
clearly outperforms both CP approaches and the state-of-the-art cSpade method
on large datasets.
Deep Neural Network for Real-Time Autonomous Indoor Navigation
Authors: Dong Ki Kim, Tsuhan Chen
Date: 2015-11-15 00:00:00
Tasks: Autonomous Navigation

Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many
challenges. One main reason is that GPS has limited precision in indoor
environments. The additional fact that MAVs are not able to carry heavy weight
or power consuming sensors, such as range finders, makes indoor autonomous
navigation a challenging task. In this paper, we propose a practical system in
which a quadcopter autonomously navigates indoors and finds a specific target,
i.e., a book bag, by using a single camera. A deep learning model,
Convolutional Neural Network (ConvNet), is used to learn a controller strategy
that mimics an expert pilot's choice of action. We show our system's
performance through real-time experiments in diverse indoor locations. To
understand more about our trained network, we use several visualization
techniques.
Hierarchical classification of e-commerce related social media
Authors: Matthew Long, Aditya Jami, Ashutosh Saxena
Date: 2015-11-26 00:00:00
Tasks: Classification, General Classification, Information Retrieval, Retrieval

In this paper, we attempt to classify tweets into root categories of the
Amazon browse node hierarchy using a set of tweets with browse node ID labels,
a much larger set of tweets without labels, and a set of Amazon reviews.
Examining twitter data presents unique challenges in that the samples are short
(under 140 characters) and often contain misspellings or abbreviations that are
trivial for a human to decipher but difficult for a computer to parse. A
variety of query and document expansion techniques are implemented in an effort
to improve information retrieval to modest success.
Welfare of Sequential Allocation Mechanisms for Indivisible Goods
Authors: Haris Aziz, Thomas Kalinowski, Toby Walsh, Lirong Xia
Date: 2015-11-26 00:00:00

Sequential allocation is a simple and attractive mechanism for the allocation
of indivisible goods. Agents take turns, according to a policy, to pick items.
Sequential allocation is guaranteed to return an allocation which is efficient
but may not have an optimal social welfare. We consider therefore the relation
between welfare and efficiency. We study the (computational) questions of what
welfare is possible or necessary depending on the choice of policy. We also
consider a novel control problem in which the chair chooses a policy to improve
social welfare.
A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Authors: Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, Liang Pang, Xue-Qi Cheng
Date: 2015-11-26 00:00:00
Tasks: Information Retrieval, Question Answering, Retrieval, Sentence, Sentence Completion

Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in the matching process. To tackle this problem, we present a new
deep architecture to match two sentences with multiple positional sentence
representations. Specifically, each positional sentence representation is a
sentence representation at this position, generated by a bidirectional long
short term memory (Bi-LSTM). The matching score is finally produced by
aggregating interactions between these different positional sentence
representations, through $k$-Max pooling and a multi-layer perceptron. Our
model has several advantages: (1) By using Bi-LSTM, rich context of the whole
sentence is leveraged to capture the contextualized local information in each
positional sentence representation; (2) By matching with multiple positional
sentence representations, it is flexible to aggregate different important
contextualized local information in a sentence to support the matching; (3)
Experiments on different tasks such as question answering and sentence
completion demonstrate the superiority of our model.
Fingerprint Recognition Using Translation Invariant Scattering Network
Authors: Shervin Minaee, Yao Wang
Date: 2015-09-11 00:00:00
Tasks: Template Matching, Translation

Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.
Hierarchical Latent Semantic Mapping for Automated Topic Generation
Authors: Guorui Zhou, Guang Chen
Date: 2015-11-11 00:00:00
Tasks: Community Detection

Much of information sits in an unprecedented amount of text data. Managing
allocation of these large scale text data is an important problem for many
areas. Topic modeling performs well in this problem. The traditional generative
models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and
most recent research on topic generation has been focusing on improving or
extending these models. However, results of traditional generative models are
sensitive to the number of topics K, which must be specified manually. The
problem of generating topics from corpus resembles community detection in
networks. Many effective algorithms can automatically detect communities from
networks without a manually specified number of the communities. Inspired by
these algorithms, in this paper, we propose a novel method named Hierarchical
Latent Semantic Mapping (HLSM), which automatically generates topics from
corpus. HLSM calculates the association between each pair of words in the
latent topic space, then constructs a unipartite network of words with this
association and hierarchically generates topics from this network. We apply
HLSM to several document collections and the experimental comparisons against
several state-of-the-art approaches demonstrate the promising performance.
On The Power of Joint Wavelet-DCT Features for Multispectral Palmprint Recognition
Authors: Shervin Minaee, Amirali Abdolrashidi
Date: 2014-09-27 00:00:00
Tasks: General Classification

Biometric-based identification has drawn a lot of attention in the recent
years. Among all biometrics, palmprint is known to possess a rich set of
features. In this paper we have proposed to use DCT-based features in parallel
with wavelet-based ones for palmprint identification. PCA is applied to the
features to reduce their dimensionality and the majority voting algorithm is
used to perform classification. The features introduced here result in a
near-perfectly accurate identification. This method is tested on a well-known
multispectral palmprint database and an accuracy rate of 99.97-100\% is
achieved, outperforming all previous methods in similar conditions.
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Authors: Varun Jampani, Martin Kiefel, Peter V. Gehler
Date: 2015-03-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Semantic Segmentation

Bilateral filters have wide spread use due to their edge-preserving
properties. The common use case is to manually choose a parametric filter type,
usually a Gaussian filter. In this paper, we will generalize the
parametrization and in particular derive a gradient descent algorithm so the
filter parameters can be learned from data. This derivation allows to learn
high dimensional linear filters that operate in sparsely populated feature
spaces. We build on the permutohedral lattice construction for efficient
filtering. The ability to learn more general forms of high-dimensional filters
can be used in several diverse applications. First, we demonstrate the use in
applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise
potentials in densely connected conditional random fields and apply these to
different image segmentation tasks. Finally, we introduce layers of bilateral
filters in CNNs and propose bilateral neural networks for the use of
high-dimensional sparse data. This view provides new ways to encode model
structure into network architectures. A diverse set of experiments empirically
validates the usage of general forms of filters.
Multi-view Metric Learning for Multi-view Video Summarization
Authors: Yanwei Fu, Lingbo Wang, Yanwen Guo
Date: 2014-05-25 00:00:00
Tasks: Clustering, Metric Learning, Video Summarization

Traditional methods on video summarization are designed to generate summaries
for single-view video records; and thus they cannot fully exploit the
redundancy in multi-view video records. In this paper, we present a multi-view
metric learning framework for multi-view video summarization that combines the
advantages of maximum margin clustering with the disagreement minimization
criterion. The learning framework thus has the ability to find a metric that
best separates the data, and meanwhile to force the learned metric to maintain
original intrinsic information between data points, for example geometric
information. Facilitated by such a framework, a systematic solution to the
multi-view video summarization problem is developed. To the best of our
knowledge, it is the first time to address multi-view video summarization from
the viewpoint of metric learning. The effectiveness of the proposed method is
demonstrated by experiments.
Deep Kalman Filters
Authors: Rahul G. Krishnan, Uri Shalit, David Sontag
Date: 2015-11-16 00:00:00
Tasks: counterfactual, Counterfactual Inference, Time Series Analysis

Kalman Filters are one of the most influential models of time-varying
phenomena. They admit an intuitive probabilistic interpretation, have a simple
functional form, and enjoy widespread adoption in a variety of disciplines.
Motivated by recent variational methods for learning deep generative models, we
introduce a unified algorithm to efficiently learn a broad spectrum of Kalman
filters. Of particular interest is the use of temporal generative models for
counterfactual inference. We investigate the efficacy of such models for
counterfactual inference, and to that end we introduce the "Healing MNIST"
dataset where long-term structure, noise and actions are applied to sequences
of digits. We show the efficacy of our method for modeling this dataset. We
further show how our model can be used for counterfactual inference for
patients, based on electronic health record data of 8,000 patients over 4.5
years.
Exploring Person Context and Local Scene Context for Object Detection
Authors: Saurabh Gupta, Bharath Hariharan, Jitendra Malik
Date: 2015-11-25 00:00:00
Tasks: Object, object-detection, Object Detection

In this paper we explore two ways of using context for object detection. The
first model focusses on people and the objects they commonly interact with,
such as fashion and sports accessories. The second model considers more general
object detection and uses the spatial relationships between objects and between
objects and scenes. Our models are able to capture precise spatial
relationships between the context and the object of interest, and make
effective use of the appearance of the contextual region. On the newly released
COCO dataset, our models provide relative improvements of up to 5% over
CNN-based state-of-the-art detectors, with the gains concentrated on hard cases
such as small objects (10% relative improvement).
First Step toward Model-Free, Anonymous Object Tracking with Recurrent Neural Networks
Authors: Quan Gan, Qipeng Guo, Zheng Zhang, Kyunghyun Cho
Date: 2015-11-19 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking, Visual Tracking

In this paper, we propose and study a novel visual object tracking approach
based on convolutional networks and recurrent networks. The proposed approach
is distinct from the existing approaches to visual object tracking, such as
filtering-based ones and tracking-by-detection ones, in the sense that the
tracking system is explicitly trained off-line to track anonymous objects in a
noisy environment. The proposed visual tracking model is end-to-end trainable,
minimizing any adversarial effect from mismatches in object representation and
between the true underlying dynamics and learning dynamics. We empirically show
that the proposed tracking approach works well in various scenarios by
generating artificial video sequences with varying conditions; the number of
objects, amount of noise and the match between the training shapes and test
shapes.
Tracking Motion and Proxemics using Thermal-sensor Array
Authors: Chandrayee Basu, Anthony Rowe
Date: 2015-11-25 00:00:00
Tasks: energy management, Management, Marketing, Motion Detection

Indoor tracking has all-pervasive applications beyond mere surveillance, for
example in education, health monitoring, marketing, energy management and so
on. Image and video based tracking systems are intrusive. Thermal array sensors
on the other hand can provide coarse-grained tracking while preserving privacy
of the subjects. The goal of the project is to facilitate motion detection and
group proxemics modeling using an 8 x 8 infrared sensor array. Each of the 8 x
8 pixels is a temperature reading in Fahrenheit. We refer to each 8 x 8 matrix
as a scene. We collected approximately 902 scenes with different configurations
of human groups and different walking directions. We infer direction of motion
of a subject across a set of scenes as left-to-right, right-to-left, up-to-down
and down-to-up using cross-correlation analysis. We used features from
connected component analysis of each background subtracted scene and performed
Support Vector Machine classification to estimate number of instances of human
subjects in the scene.
Barrier Frank-Wolfe for Marginal Inference
Authors: Rahul G. Krishnan, Simon Lacoste-Julien, David Sontag
Date: 2015-11-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Variational Inference

We introduce a globally-convergent algorithm for optimizing the
tree-reweighted (TRW) variational objective over the marginal polytope. The
algorithm is based on the conditional gradient method (Frank-Wolfe) and moves
pseudomarginals within the marginal polytope through repeated maximum a
posteriori (MAP) calls. This modular structure enables us to leverage black-box
MAP solvers (both exact and approximate) for variational inference, and obtains
more accurate results than tree-reweighted algorithms that optimize over the
local consistency relaxation. Theoretically, we bound the sub-optimality for
the proposed algorithm despite the TRW objective having unbounded gradients at
the boundary of the marginal polytope. Empirically, we demonstrate the
increased quality of results found by tightening the relaxation over the
marginal polytope as well as the spanning tree polytope on synthetic and
real-world instances.
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
Authors: Adriana Romero, Carlo Gatta, Gustau Camps-Valls
Date: 2015-11-25 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Land Cover Classification, Remote Sensing Image Classification, Unsupervised Pre-training

This paper introduces the use of single layer and deep convolutional networks
for remote sensing data analysis. Direct application to multi- and
hyper-spectral imagery of supervised (shallow or deep) convolutional networks
is very challenging given the high input data dimensionality and the relatively
small amount of available labeled data. Therefore, we propose the use of greedy
layer-wise unsupervised pre-training coupled with a highly efficient algorithm
for unsupervised learning of sparse features. The algorithm is rooted on sparse
representations and enforces both population and lifetime sparsity of the
extracted features, simultaneously. We successfully illustrate the expressive
power of the extracted representations in several scenarios: classification of
aerial scenes, as well as land-use classification in very high resolution
(VHR), or land-cover classification from multi- and hyper-spectral images. The
proposed algorithm clearly outperforms standard Principal Component Analysis
(PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art
algorithms of aerial classification, while being extremely computationally
efficient at learning representations of data. Results show that single layer
convolutional networks can extract powerful discriminative features only when
the receptive field accounts for neighboring pixels, and are preferred when the
classification requires high resolution and detailed results. However, deep
architectures significantly outperform single layers variants, capturing
increasing levels of abstraction and complexity throughout the feature
hierarchy.
Foundations of Coupled Nonlinear Dimensionality Reduction
Authors: Mehryar Mohri, Afshin Rostamizadeh, Dmitry Storcheus
Date: 2015-09-29 00:00:00
Tasks: Dimensionality Reduction, Generalization Bounds, Supervised dimensionality reduction

In this paper we introduce and analyze the learning scenario of \emph{coupled
nonlinear dimensionality reduction}, which combines two major steps of machine
learning pipeline: projection onto a manifold and subsequent supervised
learning. First, we present new generalization bounds for this scenario and,
second, we introduce an algorithm that follows from these bounds. The
generalization error bound is based on a careful analysis of the empirical
Rademacher complexity of the relevant hypothesis set. In particular, we show an
upper bound on the Rademacher complexity that is in $\widetilde
O(\sqrt{\Lambda_{(r)}/m})$, where $m$ is the sample size and $\Lambda_{(r)}$
the upper bound on the Ky-Fan $r$-norm of the associated kernel matrix. We give
both upper and lower bound guarantees in terms of that Ky-Fan $r$-norm, which
strongly justifies the definition of our hypothesis set. To the best of our
knowledge, these are the first learning guarantees for the problem of coupled
dimensionality reduction. Our analysis and learning guarantees further apply to
several special cases, such as that of using a fixed kernel with supervised
dimensionality reduction or that of unsupervised learning of a kernel for
dimensionality reduction followed by a supervised learning algorithm. Based on
theoretical analysis, we suggest a structural risk minimization algorithm
consisting of the coupled fitting of a low dimensional manifold and a
separation function on that manifold.