pwc-archive/papers-with-abstracts huggingface dataset:
Learning Compact Convolutional Neural Networks with Nested Dropout
Authors: Chelsea Finn, Lisa Anne Hendricks, Trevor Darrell
Date: 2014-12-22 00:00:00

Recently, nested dropout was proposed as a method for ordering representation
units in autoencoders by their information content, without diminishing
reconstruction cost. However, it has only been applied to training
fully-connected autoencoders in an unsupervised setting. We explore the impact
of nested dropout on the convolutional layers in a CNN trained by
backpropagation, investigating whether nested dropout can provide a simple and
systematic way to determine the optimal representation size with respect to the
desired accuracy and desired task and data complexity.
Fast Optimal Transport Averaging of Neuroimaging Data
Authors: Alexandre Gramfort, Gabriel Peyré, Marco Cuturi
Date: 2015-03-30 00:00:00

Knowing how the Human brain is anatomically and functionally organized at the
level of a group of healthy individuals or patients is the primary goal of
neuroimaging research. Yet computing an average of brain imaging data defined
over a voxel grid or a triangulation remains a challenge. Data are large, the
geometry of the brain is complex and the between subjects variability leads to
spatially or temporally non-overlapping effects of interest. To address the
problem of variability, data are commonly smoothed before group linear
averaging. In this work we build on ideas originally introduced by Kantorovich
to propose a new algorithm that can average efficiently non-normalized data
defined over arbitrary discrete domains using transportation metrics. We show
how Kantorovich means can be linked to Wasserstein barycenters in order to take
advantage of an entropic smoothing approach. It leads to a smooth convex
optimization problem and an algorithm with strong convergence guarantees. We
illustrate the versatility of this tool and its empirical behavior on
functional neuroimaging data, functional MRI and magnetoencephalography (MEG)
source estimates, defined on voxel grids and triangulations of the folded
cortical surface.
Learning Arbitrary Statistical Mixtures of Discrete Distributions
Authors: Jian Li, Yuval Rabani, Leonard J. Schulman, Chaitanya Swamy
Date: 2015-04-10 00:00:00
Tasks: Collaborative Filtering, Topic Models

We study the problem of learning from unlabeled samples very general
statistical mixture models on large finite sets. Specifically, the model to be
learned, $\vartheta$, is a probability distribution over probability
distributions $p$, where each such $p$ is a probability distribution over $[n]
= \{1,2,\dots,n\}$. When we sample from $\vartheta$, we do not observe $p$
directly, but only indirectly and in very noisy fashion, by sampling from $[n]$
repeatedly, independently $K$ times from the distribution $p$. The problem is
to infer $\vartheta$ to high accuracy in transportation (earthmover) distance.
  We give the first efficient algorithms for learning this mixture model
without making any restricting assumptions on the structure of the distribution
$\vartheta$. We bound the quality of the solution as a function of the size of
the samples $K$ and the number of samples used. Our model and results have
applications to a variety of unsupervised learning scenarios, including
learning topic models and collaborative filtering.
Linearly Supporting Feature Extraction For Automated Estimation Of Stellar Atmospheric Parameters
Authors: Xiangru Li, Yu Lu, Georges Comte, Ali Luo, Yongheng Zhao, Yongjun Wang
Date: 2015-04-09 00:00:00

We describe a scheme to extract linearly supporting (LSU) features from
stellar spectra to automatically estimate the atmospheric parameters $T_{eff}$,
log$~g$, and [Fe/H]. "Linearly supporting" means that the atmospheric
parameters can be accurately estimated from the extracted features through a
linear model. The successive steps of the process are as follow: first,
decompose the spectrum using a wavelet packet (WP) and represent it by the
derived decomposition coefficients; second, detect representative spectral
features from the decomposition coefficients using the proposed method Least
Absolute Shrinkage and Selection Operator (LARS)$_{bs}$; third, estimate the
atmospheric parameters $T_{eff}$, log$~g$, and [Fe/H] from the detected
features using a linear regression method. One prominent characteristic of this
scheme is its ability to evaluate quantitatively the contribution of each
detected feature to the atmospheric parameter estimate and also to trace back
the physical significance of that feature. This work also shows that the
usefulness of a component depends on both wavelength and frequency. The
proposed scheme has been evaluated on both real spectra from the Sloan Digital
Sky Survey (SDSS)/SEGUE and synthetic spectra calculated from Kurucz's NEWODF
models. On real spectra, we extracted 23 features to estimate $T_{eff}$, 62
features for log$~g$, and 68 features for [Fe/H]. Test consistencies between
our estimates and those provided by the Spectroscopic Sarameter Pipeline of
SDSS show that the mean absolute errors (MAEs) are 0.0062 dex for log$~T_{eff}$
(83 K for $T_{eff}$), 0.2345 dex for log$~g$, and 0.1564 dex for [Fe/H]. For
the synthetic spectra, the MAE test accuracies are 0.0022 dex for log$~T_{eff}$
(32 K for $T_{eff}$), 0.0337 dex for log$~g$, and 0.0268 dex for [Fe/H].
Visual Recognition by Counting Instances: A Multi-Instance Cardinality Potential Kernel
Authors: Hossein Hajimirsadeghi, Wang Yan, Arash Vahdat, Greg Mori
Date: 2015-02-06 00:00:00
Proceeding: CVPR 2015 6
Tasks: Activity Recognition, Event Detection, Human Activity Recognition, Video Summarization

Many visual recognition problems can be approached by counting instances. To
determine whether an event is present in a long internet video, one could count
how many frames seem to contain the activity. Classifying the activity of a
group of people can be done by counting the actions of individual people.
Encoding these cardinality relationships can reduce sensitivity to clutter, in
the form of irrelevant frames or individuals not involved in a group activity.
Learned parameters can encode how many instances tend to occur in a class of
interest. To this end, this paper develops a powerful and flexible framework to
infer any cardinality relation between latent labels in a multi-instance model.
Hard or soft cardinality relations can be encoded to tackle diverse levels of
ambiguity. Experiments on tasks such as human activity recognition, video event
detection, and video summarization demonstrate the effectiveness of using
cardinality relations for improving recognition results.
Towards Deep Neural Network Architectures Robust to Adversarial Examples
Authors: Shixiang Gu, Luca Rigazio
Date: 2014-12-11 00:00:00
Tasks: Denoising

Recent work has shown deep neural networks (DNNs) to be highly susceptible to
well-designed, small perturbations at the input layer, or so-called adversarial
examples. Taking images as an example, such distortions are often
imperceptible, but can result in 100% mis-classification for a state of the art
DNN. We study the structure of adversarial examples and explore network
topology, pre-processing and training strategies to improve the robustness of
DNNs. We perform various experiments to assess the removability of adversarial
examples by corrupting with additional noise and pre-processing with denoising
autoencoders (DAEs). We find that DAEs can remove substantial amounts of the
adversarial noise. How- ever, when stacking the DAE with the original DNN, the
resulting network can again be attacked by new adversarial examples with even
smaller distortion. As a solution, we propose Deep Contractive Network, a model
with a new end-to-end training procedure that includes a smoothness penalty
inspired by the contractive autoencoder (CAE). This increases the network
robustness to adversarial examples, without a significant performance penalty.
Memory Networks
Authors: Jason Weston, Sumit Chopra, Antoine Bordes
Date: 2014-10-15 00:00:00
Tasks: Question Answering

We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in the context of question answering (QA) where the long-term
memory effectively acts as a (dynamic) knowledge base, and the output is a
textual response. We evaluate them on a large-scale QA task, and a smaller, but
more complex, toy task generated from a simulated world. In the latter, we show
the reasoning power of such models by chaining multiple supporting sentences to
answer questions that require understanding the intension of verbs.
Leveraging Twitter for Low-Resource Conversational Speech Language Modeling
Authors: Aaron Jaech, Mari Ostendorf
Date: 2015-04-09 00:00:00
Tasks: Language Modeling, Language Modelling

In applications involving conversational speech, data sparsity is a limiting
factor in building a better language model. We propose a simple,
language-independent method to quickly harvest large amounts of data from
Twitter to supplement a smaller training set that is more closely matched to
the domain. The techniques lead to a significant reduction in perplexity on
four low-resource languages even though the presence on Twitter of these
languages is relatively small. We also find that the Twitter text is more
useful for learning word classes than the in-domain text and that use of these
word classes leads to further reductions in perplexity. Additionally, we
introduce a method of using social and textual information to prioritize the
download queue during the Twitter crawling. This maximizes the amount of useful
data that can be collected, impacting both perplexity and vocabulary coverage.
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories
Authors: Zhengwu Zhang, Jingyong Su, Eric Klassen, Huiling Le, Anuj Srivastava
Date: 2015-03-23 00:00:00
Tasks: Action Recognition, General Classification, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Lip Reading, speech-recognition, Speech Recognition, Temporal Action Localization, Time Series Analysis, Video Classification, Visual Speech Recognition

Statistical classification of actions in videos is mostly performed by
extracting relevant features, particularly covariance features, from image
frames and studying time series associated with temporal evolutions of these
features. A natural mathematical representation of activity videos is in form
of parameterized trajectories on the covariance manifold, i.e. the set of
symmetric, positive-definite matrices (SPDMs). The variable execution-rates of
actions implies variable parameterizations of the resulting trajectories, and
complicates their classification. Since action classes are invariant to
execution rates, one requires rate-invariant metrics for comparing
trajectories. A recent paper represented trajectories using their transported
square-root vector fields (TSRVFs), defined by parallel translating
scaled-velocity vectors of trajectories to a reference tangent space on the
manifold. To avoid arbitrariness of selecting the reference and to reduce
distortion introduced during this mapping, we develop a purely intrinsic
approach where SPDM trajectories are represented by redefining their TSRVFs at
the starting points of the trajectories, and analyzed as elements of a vector
bundle on the manifold. Using a natural Riemannain metric on vector bundles of
SPDMs, we compute geodesic paths and geodesic distances between trajectories in
the quotient space of this vector bundle, with respect to the
re-parameterization group. This makes the resulting comparison of trajectories
invariant to their re-parameterization. We demonstrate this framework on two
applications involving video classification: visual speech recognition or
lip-reading and hand-gesture recognition. In both cases we achieve results
either comparable to or better than the current literature.
What Do Deep CNNs Learn About Objects?
Authors: Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko
Date: 2015-04-09 00:00:00

Deep convolutional neural networks learn extremely powerful image
representations, yet most of that power is hidden in the millions of deep-layer
parameters. What exactly do these parameters represent? Recent work has started
to analyse CNN representations, finding that, e.g., they are invariant to some
2D transformations Fischer et al. (2014), but are confused by particular types
of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how
invariant are CNNs to object-class variations caused by 3D shape, pose, and
photorealism?
Collaborative Feature Learning from Social Media
Authors: Chen Fang, Hailin Jin, Jianchao Yang, Zhe Lin
Date: 2015-02-05 00:00:00
Proceeding: CVPR 2015 6

Image feature representation plays an essential role in image recognition and
related tasks. The current state-of-the-art feature learning paradigm is
supervised learning from labeled data. However, this paradigm requires
large-scale category labels, which limits its applicability to domains where
labels are hard to obtain. In this paper, we propose a new data-driven feature
learning paradigm which does not rely on category labels. Instead, we learn
from user behavior data collected on social media. Concretely, we use the image
relationship discovered in the latent space from the user behavior data to
guide the image feature learning. We collect a large-scale image and user
behavior dataset from Behance.net. The dataset consists of 1.9 million images
and over 300 million view records from 1.9 million users. We validate our
feature learning paradigm on this dataset and find that the learned feature
significantly outperforms the state-of-the-art image features in learning
better image similarities. We also show that the learned feature performs
competitively on various recognition benchmarks.
Ultimate Intelligence Part I: Physical Completeness and Objectivity of Induction
Authors: Eray Özkural
Date: 2015-01-03 00:00:00

We propose that Solomonoff induction is complete in the physical sense via
several strong physical arguments. We also argue that Solomonoff induction is
fully applicable to quantum mechanics. We show how to choose an objective
reference machine for universal induction by defining a physical message
complexity and physical message probability, and argue that this choice
dissolves some well-known objections to universal induction. We also introduce
many more variants of physical message complexity based on energy and action,
and discuss the ramifications of our proposals.
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction
Authors: Maja Temerinac-Ott, Armaghan W. Naik, Robert F. Murphy
Date: 2015-04-09 00:00:00
Tasks: Active Learning, regression

Active learning has shown to reduce the number of experiments needed to
obtain high-confidence drug-target predictions. However, in order to actually
save experiments using active learning, it is crucial to have a method to
evaluate the quality of the current prediction and decide when to stop the
experimentation process. Only by applying reliable stoping criteria to active
learning, time and costs in the experimental process can be actually saved. We
compute active learning traces on simulated drug-target matrices in order to
learn a regression model for the accuracy of the active learner. By analyzing
the performance of the regression model on simulated data, we design stopping
criteria for previously unseen experimental matrices. We demonstrate on four
previously characterized drug effect data sets that applying the stopping
criteria can result in upto 40% savings of the total experiments for highly
accurate predictions.
Real-time Monocular Object SLAM
Authors: Dorian Gálvez-López, Marta Salas, Juan D. Tardós, J. M. M. Montiel
Date: 2015-04-09 00:00:00
Tasks: Object, Object Recognition, Object SLAM

We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of binary words, which provides live detections with a database
of 500 3D objects. The two components work together and benefit each other: the
SLAM algorithm accumulates information from the observations of the objects,
anchors object features to especial map landmarks and sets constrains on the
optimization. At the same time, objects partially or fully located within the
map are used as a prior to guide the recognition algorithm, achieving higher
recall. We evaluate our proposal on five real environments showing improvements
on the accuracy of the map and efficiency with respect to other
state-of-the-art techniques.
A Collection of Challenging Optimization Problems in Science, Engineering and Economics
Authors: Dhagash Mehta, Crina Grosan
Date: 2015-04-09 00:00:00
Tasks: Benchmarking

Function optimization and finding simultaneous solutions of a system of
nonlinear equations (SNE) are two closely related and important optimization
problems. However, unlike in the case of function optimization in which one is
required to find the global minimum and sometimes local minima, a database of
challenging SNEs where one is required to find stationary points (extrama and
saddle points) is not readily available. In this article, we initiate building
such a database of important SNE (which also includes related function
optimization problems), arising from Science, Engineering and Economics. After
providing a short review of the most commonly used mathematical and
computational approaches to find solutions of such systems, we provide a
preliminary list of challenging problems by writing the Mathematical
formulation down, briefly explaning the origin and importance of the problem
and giving a short account on the currently known results, for each of the
problems. We anticipate that this database will not only help benchmarking
novel numerical methods for solving SNEs and function optimization problems but
also will help advancing the corresponding research areas.
RDF annotation of Second Life objects: Knowledge Representation meets Social Virtual reality
Authors: Carlo Bernava, Giacomo Fiumara, Dario Maggiorini, Alessandro Provetti, Laura Ripamonti
Date: 2015-04-09 00:00:00

We have designed and implemented an application running inside Second Life
that supports user annotation of graphical objects and graphical visualization
of concept ontologies, thus providing a formal, machine-accessible description
of objects. As a result, we offer a platform that combines the graphical
knowledge representation that is expected from a MUVE artifact with the
semantic structure given by the Resource Framework Description (RDF)
representation of information.
Exploring EEG for Object Detection and Retrieval
Authors: Eva Mohedano, Amaia Salvador, Sergi Porta, Xavier Giró-i-Nieto, Graham Healy, Kevin McGuinness, Noel O'Connor, Alan F. Smeaton
Date: 2015-04-09 00:00:00
Tasks: Content-Based Image Retrieval, EEG, Electroencephalogram (EEG), Image Retrieval, Object, object-detection, Object Detection, Retrieval

This paper explores the potential for using Brain Computer Interfaces (BCI)
as a relevance feedback mechanism in content-based image retrieval. We
investigate if it is possible to capture useful EEG signals to detect if
relevant objects are present in a dataset of realistic and complex images. We
perform several experiments using a rapid serial visual presentation (RSVP) of
images at different rates (5Hz and 10Hz) on 8 users with different degrees of
familiarization with BCI and the dataset. We then use the feedback from the BCI
and mouse-based interfaces to retrieve localized objects in a subset of TRECVid
images. We show that it is indeed possible to detect such objects in complex
images and, also, that users with previous knowledge on the dataset or
experience with the RSVP outperform others. When the users have limited time to
annotate the images (100 seconds in our experiments) both interfaces are
comparable in performance. Comparing our best users in a retrieval task, we
found that EEG-based relevance feedback outperforms mouse-based feedback. The
realistic and complex image dataset differentiates our work from previous
studies on EEG for image retrieval.
When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition
Authors: Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z. Li, Timothy Hospedales
Date: 2015-04-09 00:00:00
Tasks: Face Recognition, Metric Learning, Open-Ended Question Answering

Deep learning, in particular Convolutional Neural Network (CNN), has achieved
promising results in face recognition recently. However, it remains an open
question: why CNNs work well and how to design a 'good' architecture. The
existing works tend to focus on reporting CNN architectures that work well for
face recognition rather than investigate the reason. In this work, we conduct
an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a
common ground to make our work easily reproducible. Specifically, we use public
database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing
CNNs trained on private databases. We propose three CNN architectures which are
the first reported architectures trained using LFW data. This paper
quantitatively compares the architectures of CNNs and evaluate the effect of
different implementation choices. We identify several useful properties of
CNN-FRS. For instance, the dimensionality of the learned features can be
significantly reduced without adverse effect on face recognition accuracy. In
addition, traditional metric learning method exploiting CNN-learned features is
evaluated. Experiments show two crucial factors to good CNN-FRS performance are
the fusion of multiple CNNs and metric learning. To make our work reproducible,
source code and models will be made publicly available.
Generative Modeling of Convolutional Neural Networks
Authors: Jifeng Dai, Yang Lu, Ying-Nian Wu
Date: 2014-12-19 00:00:00

The convolutional neural networks (CNNs) have proven to be a powerful tool
for discriminative learning. Recently researchers have also started to show
interest in the generative aspects of CNNs in order to gain a deeper
understanding of what they have learned and how to further improve them. This
paper investigates generative modeling of CNNs. The main contributions include:
(1) We construct a generative model for the CNN in the form of exponential
tilting of a reference distribution. (2) We propose a generative gradient for
pre-training CNNs by a non-parametric importance sampling scheme, which is
fundamentally different from the commonly used discriminative gradient, and yet
has the same computational architecture and cost as the latter. (3) We propose
a generative visualization method for the CNNs by sampling from an explicit
parametric image distribution. The proposed visualization method can directly
draw synthetic samples for any given node in a trained CNN by the Hamiltonian
Monte Carlo (HMC) algorithm, without resorting to any extra hold-out images.
Experiments on the challenging ImageNet benchmark show that the proposed
generative gradient pre-training consistently helps improve the performances of
CNNs, and the proposed generative visualization method generates meaningful and
varied samples of synthetic images from a large-scale deep CNN.