pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Authors: Mehdi Noroozi, Paolo Favaro
Date: 2016-03-30 00:00:00
Tasks: Representation Learning, Transfer Learning

In this paper we study the problem of image representation learning without
human annotation. By following the principles of self-supervision, we build a
convolutional neural network (CNN) that can be trained to solve Jigsaw puzzles
as a pretext task, which requires no manual labeling, and then later repurposed
to solve object classification and detection. To maintain the compatibility
across tasks we introduce the context-free network (CFN), a siamese-ennead CNN.
The CFN takes image tiles as input and explicitly limits the receptive field
(or context) of its early processing units to one tile at a time. We show that
the CFN includes fewer parameters than AlexNet while preserving the same
semantic learning capabilities. By training the CFN to solve Jigsaw puzzles, we
learn both a feature mapping of object parts as well as their correct spatial
arrangement. Our experimental evaluations show that the learned features
capture semantically relevant content. Our proposed method for learning visual
representations outperforms state of the art methods in several transfer
learning benchmarks.
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Authors: Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg
Date: 2017-08-22 00:00:00
Tasks: BIG-bench Machine Learning

Deep learning-based techniques have achieved state-of-the-art performance on
a wide variety of recognition and classification tasks. However, these networks
are typically computationally expensive to train, requiring weeks of
computation on many GPUs; as a result, many users outsource the training
procedure to the cloud or rely on pre-trained models that are then fine-tuned
for a specific task. In this paper we show that outsourced training introduces
new security risks: an adversary can create a maliciously trained network (a
backdoored neural network, or a \emph{BadNet}) that has state-of-the-art
performance on the user's training and validation samples, but behaves badly on
specific attacker-chosen inputs. We first explore the properties of BadNets in
a toy example, by creating a backdoored handwritten digit classifier. Next, we
demonstrate backdoors in a more realistic scenario by creating a U.S. street
sign classifier that identifies stop signs as speed limits when a special
sticker is added to the stop sign; we then show in addition that the backdoor
in our US street sign detector can persist even if the network is later
retrained for another task and cause a drop in accuracy of {25}\% on average
when the backdoor trigger is present. These results demonstrate that backdoors
in neural networks are both powerful and---because the behavior of neural
networks is difficult to explicate---stealthy. This work provides motivation
for further research into techniques for verifying and inspecting neural
networks, just as we have developed tools for verifying and debugging software.
WordSup: Exploiting Word Annotations for Character based Text Detection
Authors: Han Hu, Chengquan Zhang, Yuxuan Luo, Yuzhuo Wang, Junyu Han, Errui Ding
Date: 2017-08-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Math, Scene Text Detection, Text Detection

Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.
What caused what? A quantitative account of actual causation using dynamical causal networks
Authors: Larissa Albantakis, William Marshall, Erik Hoel, Giulio Tononi
Date: 2017-08-22 00:00:00
Tasks: counterfactual

Actual causation is concerned with the question "what caused what?" Consider
a transition between two states within a system of interacting elements, such
as an artificial neural network, or a biological brain circuit. Which
combination of synapses caused the neuron to fire? Which image features caused
the classifier to misinterpret the picture? Even detailed knowledge of the
system's causal network, its elements, their states, connectivity, and dynamics
does not automatically provide a straightforward answer to the "what caused
what?" question. Counterfactual accounts of actual causation based on graphical
models, paired with system interventions, have demonstrated initial success in
addressing specific problem cases in line with intuitive causal judgments.
Here, we start from a set of basic requirements for causation (realization,
composition, information, integration, and exclusion) and develop a rigorous,
quantitative account of actual causation that is generally applicable to
discrete dynamical systems. We present a formal framework to evaluate these
causal requirements that is based on system interventions and partitions, and
considers all counterfactuals of a state transition. This framework is used to
provide a complete causal account of the transition by identifying and
quantifying the strength of all actual causes and effects linking the two
consecutive system states. Finally, we examine several exemplary cases and
paradoxes of causation and show that they can be illuminated by the proposed
framework for quantifying actual causation.
A Deterministic Nonsmooth Frank Wolfe Algorithm with Coreset Guarantees
Authors: Sathya N. Ravi, Maxwell D. Collins, Vikas Singh
Date: 2017-08-22 00:00:00

We present a new Frank-Wolfe (FW) type algorithm that is applicable to
minimization problems with a nonsmooth convex objective. We provide convergence
bounds and show that the scheme yields so-called coreset results for various
Machine Learning problems including 1-median, Balanced Development, Sparse PCA,
Graph Cuts, and the $\ell_1$-norm-regularized Support Vector Machine (SVM)
among others. This means that the algorithm provides approximate solutions to
these problems in time complexity bounds that are not dependent on the size of
the input problem. Our framework, motivated by a growing body of work on
sublinear algorithms for various data analysis problems, is entirely
deterministic and makes no use of smoothing or proximal operators. Apart from
these theoretical results, we show experimentally that the algorithm is very
practical and in some cases also offers significant computational advantages on
large problem instances. We provide an open source implementation that can be
adapted for other problems that fit the overall structure.
A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition
Authors: Isma Hadji, Richard P. Wildes
Date: 2017-08-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Dynamic Texture Recognition

This paper presents a novel hierarchical spatiotemporal orientation
representation for spacetime image analysis. It is designed to combine the
benefits of the multilayer architecture of ConvNets and a more controlled
approach to spacetime analysis. A distinguishing aspect of the approach is that
unlike most contemporary convolutional networks no learning is involved;
rather, all design decisions are specified analytically with theoretical
motivations. This approach makes it possible to understand what information is
being extracted at each stage and layer of processing as well as to minimize
heuristic choices in design. Another key aspect of the network is its recurrent
nature, whereby the output of each layer of processing feeds back to the input.
To keep the network size manageable across layers, a novel cross-channel
feature pooling is proposed. The multilayer architecture that results
systematically reveals hierarchical image structure in terms of multiscale,
multiorientation properties of visual spacetime. To illustrate its utility, the
network has been applied to the task of dynamic texture recognition. Empirical
evaluation on multiple standard datasets shows that it sets a new
state-of-the-art.
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products
Authors: Kush R. Varshney, Homa Alemzadeh
Date: 2016-10-05 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning algorithms increasingly influence our decisions and interact
with us in all parts of our daily lives. Therefore, just as we consider the
safety of power plants, highways, and a variety of other engineered
socio-technical systems, we must also take into account the safety of systems
involving machine learning. Heretofore, the definition of safety has not been
formalized in a machine learning context. In this paper, we do so by defining
machine learning safety in terms of risk, epistemic uncertainty, and the harm
incurred by unwanted outcomes. We then use this definition to examine safety in
all sorts of applications in cyber-physical systems, decision sciences, and
data products. We find that the foundational principle of modern statistical
machine learning, empirical risk minimization, is not always a sufficient
objective. Finally, we discuss how four different categories of strategies for
achieving safety in engineering, including inherently safe design, safety
reserves, safe fail, and procedural safeguards can be mapped to a machine
learning context. We then discuss example techniques that can be adopted in
each category, such as considering interpretability and causality of predictive
models, objective functions beyond expected prediction accuracy, human
involvement for labeling difficult or rare examples, and user experience design
of software and open data.
Causally Regularized Learning with Agnostic Data Selection Bias
Authors: Zheyan Shen, Peng Cui, Kun Kuang, Bo Li, Peixuan Chen
Date: 2017-08-22 00:00:00
Tasks: regression, Selection bias, Transfer Learning

Most of previous machine learning algorithms are proposed based on the i.i.d.
hypothesis. However, this ideal assumption is often violated in real
applications, where selection bias may arise between training and testing
process. Moreover, in many scenarios, the testing data is not even available
during the training process, which makes the traditional methods like transfer
learning infeasible due to their need on prior of test distribution. Therefore,
how to address the agnostic selection bias for robust model learning is of
paramount importance for both academic research and real applications. In this
paper, under the assumption that causal relationships among variables are
robust across domains, we incorporate causal technique into predictive modeling
and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
by jointly optimize global confounder balancing and weighted logistic
regression. Global confounder balancing helps to identify causal features,
whose causal effect on outcome are stable across domains, then performing
logistic regression on those causal features constructs a robust predictive
model against the agnostic bias. To validate the effectiveness of our CRLR
algorithm, we conduct comprehensive experiments on both synthetic and real
world datasets. Experimental results clearly demonstrate that our CRLR
algorithm outperforms the state-of-the-art methods, and the interpretability of
our method can be fully depicted by the feature visualization.
Activity Recognition based on a Magnitude-Orientation Stream Network
Authors: Carlos Caetano, Victor H. C. de Melo, Jefersson A. dos Santos, William Robson Schwartz
Date: 2017-08-22 00:00:00
Tasks: Activity Recognition, Optical Flow Estimation

The temporal component of videos provides an important clue for activity
recognition, as a number of activities can be reliably recognized based on the
motion information. In view of that, this work proposes a novel temporal stream
for two-stream convolutional networks based on images computed from the optical
flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to
learn the motion in a better and richer manner. Our method applies simple
nonlinear transformations on the vertical and horizontal components of the
optical flow to generate input images for the temporal stream. Experimental
results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate
that using our proposed temporal stream as input to existing neural network
architectures can improve their performance for activity recognition. Results
demonstrate that our temporal stream provides complementary information able to
improve the classical two-stream methods, indicating the suitability of our
approach to be used as a temporal video representation.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
Authors: Christian Rupprecht, Iro Laina, Robert DiPietro, Maximilian Baust, Federico Tombari, Nassir Navab, Gregory D. Hager
Date: 2016-12-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: Future prediction, image-classification, Image Classification, object-detection, Object Detection, Pose Estimation, Prediction, valid

Many prediction tasks contain uncertainty. In some cases, uncertainty is
inherent in the task itself. In future prediction, for example, many distinct
outcomes are equally valid. In other cases, uncertainty arises from the way
data is labeled. For example, in object detection, many objects of interest
often go unlabeled, and in human pose estimation, occluded joints are often
labeled with ambiguous values. In this work we focus on a principled approach
for handling such scenarios. In particular, we propose a framework for
reformulating existing single-prediction models as multiple hypothesis
prediction (MHP) models and an associated meta loss and optimization procedure
to train them. To demonstrate our approach, we consider four diverse
applications: human pose estimation, future prediction, image classification
and segmentation. We find that MHP models outperform their single-hypothesis
counterparts in all cases, and that MHP models simultaneously expose valuable
insights into the variability of predictions.
The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces
Authors: Benjamin Paaßen, Barbara Hammer, Thomas William Price, Tiffany Barnes, Sebastian Gross, Niels Pinkwart
Date: 2017-08-22 00:00:00

Intelligent tutoring systems can support students in solving multi-step tasks
by providing hints regarding what to do next. However, engineering such
next-step hints manually or via an expert model becomes infeasible if the space
of possible states is too large. Therefore, several approaches have emerged to
infer next-step hints automatically, relying on past students' data. In
particular, the Hint Factory (Barnes & Stamper, 2008) recommends edits that are
most likely to guide students from their current state towards a correct
solution, based on what successful students in the past have done in the same
situation. Still, the Hint Factory relies on student data being available for
any state a student might visit while solving the task, which is not the case
for some learning tasks, such as open-ended programming tasks. In this
contribution we provide a mathematical framework for edit-based hint policies
and, based on this theory, propose a novel hint policy to provide edit hints in
vast and sparsely populated state spaces. In particular, we extend the Hint
Factory by considering data of past students in all states which are similar to
the student's current state and creating hints approximating the weighted
average of all these reference states. Because the space of possible weighted
averages is continuous, we call this approach the Continuous Hint Factory. In
our experimental evaluation, we demonstrate that the Continuous Hint Factory
can predict more accurately what capable students would do compared to existing
prediction schemes on two learning tasks, especially in an open-ended
programming task, and that the Continuous Hint Factory is comparable to
existing hint policies at reproducing tutor hints on a simple UML diagram task.
Color and Gradient Features for Text Segmentation from Video Frames
Authors: P. Shivakumara, D. S. Guru, H. T. Basavaraju
Date: 2017-08-22 00:00:00
Tasks: Clustering, Text Segmentation

Text segmentation in a video is drawing attention of researchers in the field
of image processing, pattern recognition and document image analysis because it
helps in annotating and labeling video events accurately. We propose a novel
idea of generating an enhanced frame from the R, G, and B channels of an input
frame by grouping high and low values using Min-Max clustering criteria. We
also perform sliding window on enhanced frame to group high and low values from
the neighboring pixel values to further enhance the frame. Subsequently, we use
k-means with k=2 clustering algorithm to separate text and non-text regions.
The fully connected components will be identified in the skeleton of the frame
obtained by k-means clustering. Concept of connected component analysis based
on gradient feature has been adapted for the purpose of symmetry verification.
The components which satisfy symmetric verification are selected to be the
representatives of text regions and they are permitted to grow to cover their
respective region fully containing text. The method is tested on variety of
video frames to evaluate the performance of the method in terms of recall,
precision and f-measure. The results show that method is promising and
encouraging.
Long-Short Range Context Neural Networks for Language Modeling
Authors: Youssef Oualil, Mittul Singh, Clayton Greenberg, Dietrich Klakow
Date: 2017-08-22 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Language Modeling, Language Modelling, Text Compression

The goal of language modeling techniques is to capture the statistical and
structural properties of natural languages from training corpora. This task
typically involves the learning of short range dependencies, which generally
model the syntactic properties of a language and/or long range dependencies,
which are semantic in nature. We propose in this paper a new multi-span
architecture, which separately models the short and long context information
while it dynamically merges them to perform the language modeling task. This is
done through a novel recurrent Long-Short Range Context (LSRC) network, which
explicitly models the local (short) and global (long) context using two
separate hidden states that evolve in time. This new architecture is an
adaptation of the Long-Short Term Memory network (LSTM) to take into account
the linguistic properties. Extensive experiments conducted on the Penn Treebank
(PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
language modeling techniques.
Golden Years, Golden Shores: A Study of Elders in Online Travel Communities
Authors: Bartłomiej Balcerzak, Radosław Nielek
Date: 2017-08-22 00:00:00

In this paper we present our exploratory findings related to extracting
knowledge and experiences from a community of senior tourists. By using tools
of qualitative analysis as well as review of literature, we managed to verify a
set of hypotheses related to the content created by senior tourists when
participating in on-line communities. We also produced a codebook, representing
various themes one may encounter in such communities. This codebook, derived
from our own qualitative research, as well a literature review will serve as a
basis for further development of automated tools of knowledge extraction. We
also managed to find that older adults more often than other poster in tourists
forums, mention their age in discussion, more often share their experiences and
motivation to travel, however they do not differ in relation to describing
barriers encountered while traveling.
A Batch Noise Contrastive Estimation Approach for Training Large Vocabulary Language Models
Authors: Youssef Oualil, Dietrich Klakow
Date: 2017-08-20 00:00:00
Tasks: GPU, Text Compression

Training large vocabulary Neural Network Language Models (NNLMs) is a
difficult task due to the explicit requirement of the output layer
normalization, which typically involves the evaluation of the full softmax
function over the complete vocabulary. This paper proposes a Batch Noise
Contrastive Estimation (B-NCE) approach to alleviate this problem. This is
achieved by reducing the vocabulary, at each time step, to the target words in
the batch and then replacing the softmax by the noise contrastive estimation
approach, where these words play the role of targets and noise samples at the
same time. In doing so, the proposed approach can be fully formulated and
implemented using optimal dense matrix operations. Applying B-NCE to train
different NNLMs on the Large Text Compression Benchmark (LTCB) and the One
Billion Word Benchmark (OBWB) shows a significant reduction of the training
time with no noticeable degradation of the models performance. This paper also
presents a new baseline comparative study of different standard NNLMs on the
large OBWB on a single Titan-X GPU.
Stacked transfer learning for tropical cyclone intensity prediction
Authors: Ratneel Vikash Deo, Rohitash Chandra, Anuraganand Sharma
Date: 2017-08-22 00:00:00
Tasks: Ensemble Learning, Prediction, Transfer Learning

Tropical cyclone wind-intensity prediction is a challenging task considering
drastic changes climate patterns over the last few decades. In order to develop
robust prediction models, one needs to consider different characteristics of
cyclones in terms of spatial and temporal characteristics. Transfer learning
incorporates knowledge from a related source dataset to compliment a target
datasets especially in cases where there is lack or data. Stacking is a form of
ensemble learning focused for improving generalization that has been recently
used for transfer learning problems which is referred to as transfer stacking.
In this paper, we employ transfer stacking as a means of studying the effects
of cyclones whereby we evaluate if cyclones in different geographic locations
can be helpful for improving generalization performs. Moreover, we use
conventional neural networks for evaluating the effects of duration on cyclones
in prediction performance. Therefore, we develop an effective strategy that
evaluates the relationships between different types of cyclones through
transfer learning and conventional learning methods via neural networks.