pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Texts with Gradient Methods
Authors: Zhitao Gong, Wenlu Wang, Bo Li, Dawn Song, Wei-Shinn Ku
Date: 2018-01-22 00:00:00

Adversarial samples for images have been extensively studied in the
literature. Among many of the attacking methods, gradient-based methods are
both effective and easy to compute. In this work, we propose a framework to
adapt the gradient attacking methods on images to text domain. The main
difficulties for generating adversarial texts with gradient methods are i) the
input space is discrete, which makes it difficult to accumulate small noise
directly in the inputs, and ii) the measurement of the quality of the
adversarial texts is difficult. We tackle the first problem by searching for
adversarials in the embedding space and then reconstruct the adversarial texts
via nearest neighbor search. For the latter problem, we employ the Word Mover's
Distance (WMD) to quantify the quality of adversarial texts. Through extensive
experiments on three datasets, IMDB movie reviews, Reuters-2 and Reuters-5
newswires, we show that our framework can leverage gradient attacking methods
to generate very high-quality adversarial texts that are only a few words
different from the original texts. There are many cases where we can change one
word to alter the label of the whole piece of text. We successfully incorporate
FGM and DeepFool into our framework. In addition, we empirically show that WMD
is closely related to the quality of adversarial texts.
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs
Authors: Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
Date: 2017-11-19 00:00:00
Tasks: Bayesian Optimization, Vocal Bursts Intensity Prediction

This paper presents a novel decentralized high-dimensional Bayesian
optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms,
can exploit the interdependent effects of various input components on the
output of the unknown objective function f for boosting the BO performance and
still preserve scalability in the number of input dimensions without requiring
prior knowledge or the existence of a low (effective) dimension of the input
space. To realize this, we propose a sparse yet rich factor graph
representation of f to be exploited for designing an acquisition function that
can be similarly represented by a sparse factor graph and hence be efficiently
optimized in a decentralized manner using distributed message passing. Despite
richly characterizing the interdependent effects of the input components on the
output of f with a factor graph, DEC-HBO can still guarantee no-regret
performance asymptotically. Empirical evaluation on synthetic and real-world
experiments (e.g., sparse Gaussian process model with 1811 hyperparameters)
shows that DEC-HBO outperforms the state-of-the-art HBO algorithms.
Free Energy Minimization Using the 2-D Cluster Variation Method: Initial Code Verification and Validation
Authors: Alianna J. Maren
Date: 2018-01-24 00:00:00

A new approach for general artificial intelligence (GAI), building on neural network deep learning architectures, can make use of one or more hidden layers that have the ability to continuously reach a free energy minimum even after input stimulus is removed, allowing for a variety of possible behaviors. One reason that this approach has not been developed until now has been the lack of a suitable free energy equation. The Cluster Variation Method (CVM) offers a means for characterizing 2-D local pattern distributions, or configuration variables, and provides a free energy formalism in terms of these configuration variables. The equilibrium distribution of these configuration variables is defined in terms of a single interaction enthalpy parameter, h, for the case of equiprobable distribution of bistate units. For non-equiprobable distributions, the equilibrium distribution can be characterized by providing a fixed value for the fraction of units in the active state (x1), corresponding to the influence of a per-unit activation enthalpy, together with the pairwise interaction enthalpy parameter h. This paper provides verification and validation (V&V) for code that computes the configuration variable and thermodynamic values for 2-D CVM grids characterized by different interaction enthalpy parameters, or h-values. This work provides a foundation for experimenting with a 2-D CVM-based hidden layer that can, as an alternative to responding strictly to inputs, also now independently come to its own free energy minimum and also return to a free energy-minimized state after perturbations, which will enable a range of input-independent behaviors. A further use of this 2-D CVM grid is that by characterizing local patterns in terms of their corresponding h-values (together with their x1 values), we have a means for quantitatively characterizing different kinds of neural topographies.
The challenge of simultaneous object detection and pose estimation: a comparative study
Authors: Daniel Oñoro-Rubio, Roberto J. López-Sastre, Carolina Redondo-Cabrera, Pedro Gil-Jiménez
Date: 2018-01-24 00:00:00
Tasks: object-detection, Object Detection, Open-Ended Question Answering, Pose Estimation

Detecting objects and estimating their pose remains as one of the major
challenges of the computer vision research community. There exists a compromise
between localizing the objects and estimating their viewpoints. The detector
ideally needs to be view-invariant, while the pose estimation process should be
able to generalize towards the category-level. This work is an exploration of
using deep learning models for solving both problems simultaneously. For doing
so, we propose three novel deep learning architectures, which are able to
perform a joint detection and pose estimation, where we gradually decouple the
two tasks. We also investigate whether the pose estimation problem should be
solved as a classification or regression problem, being this still an open
question in the computer vision community. We detail a comparative analysis of
all our solutions and the methods that currently define the state of the art
for this problem. We use PASCAL3D+ and ObjectNet3D datasets to present the
thorough experimental evaluation and main results. With the proposed models we
achieve the state-of-the-art performance in both datasets.
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks
Authors: Jen-Cheng Hou, Syu-Siang Wang, Ying-Hui Lai, Yu Tsao, Hsiu-Wen Chang, Hsin-Min Wang
Date: 2017-03-30 00:00:00
Tasks: Decoder, Multi-Task Learning, Speech Enhancement

Speech enhancement (SE) aims to reduce noise in speech signals. Most SE
techniques focus only on addressing audio information. In this work, inspired
by multimodal learning, which utilizes data from different modalities, and the
recent success of convolutional neural networks (CNNs) in SE, we propose an
audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual
streams into a unified network model. We also propose a multi-task learning
framework for reconstructing audio and visual signals at the output layer.
Precisely speaking, the proposed AVDCNN model is structured as an audio-visual
encoder-decoder network, in which audio and visual data are first processed
using individual CNNs, and then fused into a joint network to generate enhanced
speech (the primary task) and reconstructed images (the secondary task) at the
output layer. The model is trained in an end-to-end manner, and parameters are
jointly learned through back-propagation. We evaluate enhanced speech using
five instrumental criteria. Results show that the AVDCNN model yields a notably
superior performance compared with an audio-only CNN-based SE model and two
conventional SE approaches, confirming the effectiveness of integrating visual
information into the SE process. In addition, the AVDCNN model also outperforms
an existing audio-visual SE model, confirming its capability of effectively
combining audio and visual information in SE.
Unsupervised learning from videos using temporal coherency deep networks
Authors: Carolina Redondo-Cabrera, Roberto J. López-Sastre
Date: 2018-01-24 00:00:00

In this work we address the challenging problem of unsupervised learning from
videos. Existing methods utilize the spatio-temporal continuity in contiguous
video frames as regularization for the learning process. Typically, this
temporal coherence of close frames is used as a free form of annotation,
encouraging the learned representations to exhibit small differences between
these frames. But this type of approach fails to capture the dissimilarity
between videos with different content, hence learning less discriminative
features. We here propose two Siamese architectures for Convolutional Neural
Networks, and their corresponding novel loss functions, to learn from unlabeled
videos, which jointly exploit the local temporal coherence between contiguous
frames, and a global discriminative margin used to separate representations of
different videos. An extensive experimental evaluation is presented, where we
validate the proposed models on various tasks. First, we show how the learned
features can be used to discover actions and scenes in video collections.
Second, we show the benefits of such an unsupervised learning from just
unlabeled videos, which can be directly used as a prior for the supervised
recognition tasks of actions and objects in images, where our results further
show that our features can even surpass a traditional and heavily supervised
pre-training plus fine-tunning strategy.
Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
Authors: Konda Reddy Mopuri, Aditya Ganeshan, R. Venkatesh Babu
Date: 2018-01-24 00:00:00
Tasks: Adversarial Attack, Depth Estimation, Object Recognition, Semantic Segmentation

Machine learning models are susceptible to adversarial perturbations: small
changes to input that can cause large changes in output. It is also
demonstrated that there exist input-agnostic perturbations, called universal
adversarial perturbations, which can change the inference of target model on
most of the data samples. However, existing methods to craft universal
perturbations are (i) task specific, (ii) require samples from the training
data distribution, and (iii) perform complex optimizations. Additionally,
because of the data dependence, fooling ability of the crafted perturbations is
proportional to the available training data. In this paper, we present a novel,
generalizable and data-free approaches for crafting universal adversarial
perturbations. Independent of the underlying task, our objective achieves
fooling via corrupting the extracted features at multiple layers. Therefore,
the proposed objective is generalizable to craft image-agnostic perturbations
across multiple vision tasks such as object recognition, semantic segmentation,
and depth estimation. In the practical setting of black-box attack scenario
(when the attacker does not have access to the target model and it's training
data), we show that our objective outperforms the data dependent objectives to
fool the learned models. Further, via exploiting simple priors related to the
data distribution, our objective remarkably boosts the fooling ability of the
crafted perturbations. Significant fooling rates achieved by our objective
emphasize that the current deep learning models are now at an increased risk,
since our objective generalizes across multiple tasks without the requirement
of training data for crafting the perturbations. To encourage reproducible
research, we have released the codes for our proposed algorithm.
The Geometric Block Model
Authors: Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha
Date: 2017-09-16 00:00:00
Tasks: Community Detection, model, Stochastic Block Model

To capture the inherent geometric features of many community detection
problems, we propose to use a new random graph model of communities that we
call a Geometric Block Model. The geometric block model generalizes the random
geometric graphs in the same way that the well-studied stochastic block model
generalizes the Erdos-Renyi random graphs. It is also a natural extension of
random community models inspired by the recent theoretical and practical
advancement in community detection. While being a topic of fundamental
theoretical interest, our main contribution is to show that many practical
community structures are better explained by the geometric block model. We also
show that a simple triangle-counting algorithm to detect communities in the
geometric block model is near-optimal. Indeed, even in the regime where the
average degree of the graph grows only logarithmically with the number of
vertices (sparse-graph), we show that this algorithm performs extremely well,
both theoretically and practically. In contrast, the triangle-counting
algorithm is far from being optimum for the stochastic block model. We simulate
our results on both real and synthetic datasets to show superior performance of
both the new model as well as our algorithm.
Detecting and counting tiny faces
Authors: Alexandre Attia, Sharone Dayan
Date: 2018-01-19 00:00:00

Finding Tiny Faces (by Hu and Ramanan) proposes a novel approach to find
small objects in an image. Our contribution consists in deeply understanding
the choices of the paper together with applying and extending a similar method
to a real world subject which is the counting of people in a public
demonstration.
On Scale-out Deep Learning Training for Cloud and HPC
Authors: Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
Date: 2018-01-24 00:00:00
Tasks: Deep Learning, Philosophy

The exponential growth in use of large deep neural networks has accelerated
the need for training these deep neural networks in hours or even minutes. This
can only be achieved through scalable and efficient distributed training, since
a single node/card cannot satisfy the compute, memory, and I/O requirements of
today's state-of-the-art deep neural networks. However, scaling synchronous
Stochastic Gradient Descent (SGD) is still a challenging problem and requires
continued research/development. This entails innovations spanning algorithms,
frameworks, communication libraries, and system design. In this paper, we
describe the philosophy, design, and implementation of Intel Machine Learning
Scalability Library (MLSL) and present proof-points demonstrating scaling DL
training on 100s to 1000s of nodes across Cloud and HPC systems.
Training Set Debugging Using Trusted Items
Authors: Xuezhou Zhang, Xiaojin Zhu, Stephen J. Wright
Date: 2018-01-24 00:00:00
Tasks: BIG-bench Machine Learning, Bilevel Optimization

Training set bugs are flaws in the data that adversely affect machine
learning. The training set is usually too large for man- ual inspection, but
one may have the resources to verify a few trusted items. The set of trusted
items may not by itself be adequate for learning, so we propose an algorithm
that uses these items to identify bugs in the training set and thus im- proves
learning. Specifically, our approach seeks the smallest set of changes to the
training set labels such that the model learned from this corrected training
set predicts labels of the trusted items correctly. We flag the items whose
labels are changed as potential bugs, whose labels can be checked for veracity
by human experts. To find the bugs in this way is a challenging combinatorial
bilevel optimization problem, but it can be relaxed into a continuous
optimization problem. Ex- periments on toy and real data demonstrate that our
approach can identify training set bugs effectively and suggest appro- priate
changes to the labels. Our algorithm is a step toward trustworthy machine
learning.
Unsupervised object discovery for instance recognition
Authors: Oriane Siméoni, Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum
Date: 2017-09-14 00:00:00
Tasks: Image Retrieval, Object, Object Discovery, Retrieval

Severe background clutter is challenging in many computer vision tasks,
including large-scale image retrieval. Global descriptors, that are popular due
to their memory and search efficiency, are especially prone to corruption by
such a clutter. Eliminating the impact of the clutter on the image descriptor
increases the chance of retrieving relevant images and prevents topic drift due
to actually retrieving the clutter in the case of query expansion. In this
work, we propose a novel salient region detection method. It captures, in an
unsupervised manner, patterns that are both discriminative and common in the
dataset. Saliency is based on a centrality measure of a nearest neighbor graph
constructed from regional CNN representations of dataset images. The
descriptors derived from the salient regions improve particular object
retrieval, most noticeably in a large collections containing small objects.
Intrinsic Dimension of Geometric Data Sets
Authors: Tom Hanika, Friedrich Martin Schneider, Gerd Stumme
Date: 2018-01-24 00:00:00

The curse of dimensionality is a phenomenon frequently observed in machine learning (ML) and knowledge discovery (KD). There is a large body of literature investigating its origin and impact, using methods from mathematics as well as from computer science. Among the mathematical insights into data dimensionality, there is an intimate link between the dimension curse and the phenomenon of measure concentration, which makes the former accessible to methods of geometric analysis. The present work provides a comprehensive study of the intrinsic geometry of a data set, based on Gromov's metric measure geometry and Pestov's axiomatic approach to intrinsic dimension. In detail, we define a concept of geometric data set and introduce a metric as well as a partial order on the set of isomorphism classes of such data sets. Based on these objects, we propose and investigate an axiomatic approach to the intrinsic dimension of geometric data sets and establish a concrete dimension function with the desired properties. Our model for data sets and their intrinsic dimension is computationally feasible and, moreover, adaptable to specific ML/KD-algorithms, as illustrated by various experiments.
Identifying emergency stages in Facebook posts of police departments with convolutional and recurrent neural networks and support vector machines
Authors: Nicolai Pogrebnyakov, Edgar Maldonado
Date: 2018-01-02 00:00:00
Tasks: General Classification

Classification of social media posts in emergency response is an important
practical problem: accurate classification can help automate processing of such
messages and help other responders and the public react to emergencies in a
timely fashion. This research focused on classifying Facebook messages of US
police departments. Randomly selected 5,000 messages were used to train
classifiers that distinguished between four categories of messages: emergency
preparedness, response and recovery, as well as general engagement messages.
Features were represented with bag-of-words and word2vec, and models were
constructed using support vector machines (SVMs) and convolutional (CNNs) and
recurrent neural networks (RNNs). The best performing classifier was an RNN
with a custom-trained word2vec model to represent features, which achieved the
F1 measure of 0.839.
Evaluation of Interactive Machine Learning Systems
Authors: Nadia Boukhelifa, Anastasia Bezerianos, Evelyne Lutton
Date: 2018-01-24 00:00:00
Tasks: BIG-bench Machine Learning

The evaluation of interactive machine learning systems remains a difficult
task. These systems learn from and adapt to the human, but at the same time,
the human receives feedback and adapts to the system. Getting a clear
understanding of these subtle mechanisms of co-operation and co-adaptation is
challenging. In this chapter, we report on our experience in designing and
evaluating various interactive machine learning applications from different
domains. We argue for coupling two types of validation: algorithm-centered
analysis, to study the computational behaviour of the system; and
human-centered evaluation, to observe the utility and effectiveness of the
application for end-users. We use a visual analytics application for guided
search, built using an interactive evolutionary approach, as an exemplar of our
work. Our observation is that human-centered design and evaluation complement
algorithmic analysis, and can play an important role in addressing the
"black-box" effect of machine learning. Finally, we discuss research
opportunities that require human-computer interaction methodologies, in order
to support both the visible and hidden roles that humans play in interactive
machine learning.
An LSTM Network for Highway Trajectory Prediction
Authors: Florent Altché, Arnaud de La Fortelle
Date: 2018-01-24 00:00:00
Tasks: Autonomous Vehicles, Prediction, Trajectory Prediction

In order to drive safely and efficiently on public roads, autonomous vehicles
will have to understand the intentions of surrounding vehicles, and adapt their
own behavior accordingly. If experienced human drivers are generally good at
inferring other vehicles' motion up to a few seconds in the future, most
current Advanced Driving Assistance Systems (ADAS) are unable to perform such
medium-term forecasts, and are usually limited to high-likelihood situations
such as emergency braking. In this article, we present a first step towards
consistent trajectory prediction by introducing a long short-term memory (LSTM)
neural network, which is capable of accurately predicting future longitudinal
and lateral trajectories for vehicles on highway. Unlike previous work focusing
on a low number of trajectories collected from a few drivers, our network was
trained and validated on the NGSIM US-101 dataset, which contains a total of
800 hours of recorded trajectories in various traffic densities, representing
more than 6000 individual drivers.
Deep Structured Energy-Based Image Inpainting
Authors: Fazil Altinel, Mete Ozay, Takayuki Okatani
Date: 2018-01-24 00:00:00
Tasks: Image Inpainting, Structured Prediction

In this paper, we propose a structured image inpainting method employing an
energy based model. In order to learn structural relationship between patterns
observed in images and missing regions of the images, we employ an energy-based
structured prediction method. The structural relationship is learned by
minimizing an energy function which is defined by a simple convolutional neural
network. The experimental results on various benchmark datasets show that our
proposed method significantly outperforms the state-of-the-art methods which
use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared
error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the
state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise
ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared
to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively.
The code is publicly available.