pwc-archive/papers-with-abstracts huggingface dataset:
A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
Authors: Shahbaz Rezaei, Xin Liu
Date: 2019-04-08 00:00:00
Proceeding: ICLR 2020 1
Tasks: Face Recognition, Image Classification, speech-recognition, Speech Recognition, Transfer Learning

Due to insufficient training data and the high computational cost to train a deep neural network from scratch, transfer learning has been extensively used in many deep-neural-network-based applications. A commonly used transfer learning approach involves taking a part of a pre-trained model, adding a few layers at the end, and re-training the new layers with a small dataset. This approach, while efficient and widely used, imposes a security vulnerability because the pre-trained model used in transfer learning is usually publicly available, including to potential attackers. In this paper, we show that without any additional knowledge other than the pre-trained model, an attacker can launch an effective and efficient brute force attack that can craft instances of input to trigger each target class with high confidence. We assume that the attacker has no access to any target-specific information, including samples from target classes, re-trained model, and probabilities assigned by Softmax to each class, and thus making the attack target-agnostic. These assumptions render all previous attack models inapplicable, to the best of our knowledge. To evaluate the proposed attack, we perform a set of experiments on face recognition and speech recognition tasks and show the effectiveness of the attack. Our work reveals a fundamental security weakness of the Softmax layer when used in transfer learning settings.
Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
Authors: Xiaowei Jia, Ankush Khandelwal, Vipin Kumar
Date: 2019-04-08 00:00:00
Tasks: BIG-bench Machine Learning

This paper provides an overview of how recent advances in machine learning
and the availability of data from earth observing satellites can dramatically
improve our ability to automatically map croplands over long period and over
large regions. It discusses three applications in the domain of crop monitoring
where ML approaches are beginning to show great promise. For each application,
it highlights machine learning challenges, proposed approaches, and recent
results. The paper concludes with discussion of major challenges that need to
be addressed before ML approaches will reach their full potential for this
problem of great societal relevance.
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics
Authors: Weinan E, Chao Ma, Lei Wu
Date: 2019-04-08 00:00:00

A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network width and training data size are considered. In the over-parametrized regime, it is shown that gradient descent dynamics can achieve zero training loss exponentially fast regardless of the quality of the labels. In addition, it is proved that throughout the training process the functions represented by the neural network model are uniformly close to that of a kernel method. For general values of the network width and training data size, sharp estimates of the generalization error is established for target functions in the appropriate reproducing kernel Hilbert space.
Data adaptation in HANDY economy-ideology model
Authors: Marcin Sendera
Date: 2019-04-08 00:00:00
Tasks: model

The concept of mathematical modeling is widespread across almost all of the
fields of contemporary science and engineering. Because of the existing
necessity of predictions the behavior of natural phenomena, the researchers
develop more and more complex models. However, despite their ability to better
forecasting, the problem of an appropriate fitting ground truth data to those,
high-dimensional and nonlinear models seems to be inevitable. In order to deal
with this demanding problem the entire discipline of data assimilation has been
developed. Basing on the Human and Nature Dynamics (HANDY) model, we have
presented a detailed and comprehensive comparison of Approximate Bayesian
Computation (classic data assimilation method) and a novelty approach of
Supermodeling. Furthermore, with the usage of Sensitivity Analysis, we have
proposed the methodology to reduce the number of coupling coefficients between
submodels and as a consequence to increase the speed of the Supermodel
converging. In addition, we have demonstrated that usage of Approximate
Bayesian Computation method with the knowledge about parameters' sensitivities
could result with satisfactory estimation of the initial parameters. However,
we have also presented the mentioned methodology as unable to achieve similar
predictions to Approximate Bayesian Computation. Finally, we have proved that
Supermodeling with synchronization via the most sensitive variable could effect
with the better forecasting for chaotic as well as more stable systems than the
Approximate Bayesian Computation. What is more, we have proposed the adequate
methodologies.
Word Similarity Datasets for Thai: Construction and Evaluation
Authors: Ponrudee Netisopakul, Gerhard Wohlgenannt, Aleksei Pulich
Date: 2019-04-08 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Task 2, Word Embeddings, Word Similarity

Distributional semantics in the form of word embeddings are an essential
ingredient to many modern natural language processing systems. The
quantification of semantic similarity between words can be used to evaluate the
ability of a system to perform semantic interpretation. To this end, a number
of word similarity datasets have been created for the English language over the
last decades. For Thai language few such resources are available. In this work,
we create three Thai word similarity datasets by translating and re-rating the
popular WordSim-353, SimLex-999 and SemEval-2017-Task-2 datasets. The three
datasets contain 1852 word pairs in total and have different characteristics in
terms of difficulty, domain coverage, and notion of similarity (relatedness
vs.~similarity). These features help to gain a broader picture of the
properties of an evaluated word embedding model. We include baseline
evaluations with existing Thai embedding models, and identify the high ratio of
out-of-vocabulary words as one of the biggest challenges. All datasets,
evaluation results, and a tool for easy evaluation of new Thai embedding models
are available to the NLP community online.
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection
Authors: Bilal Taha, Munawar Hayat, Stefano Berretti, Naoufel Werghi
Date: 2019-04-08 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection, Transfer Learning

This paper proposes an approach to learn generic multi-modal mesh surface
representations using a novel scheme for fusing texture and geometric data. Our
approach defines an inverse mapping between different geometric descriptors
computed on the mesh surface or its down-sampled version, and the corresponding
2D texture image of the mesh, allowing the construction of fused geometrically
augmented images (FGAI). This new fused modality enables us to learn feature
representations from 3D data in a highly efficient manner by simply employing
standard convolutional neural networks in a transfer-learning mode. In contrast
to existing methods, the proposed approach is both computationally and memory
efficient, preserves intrinsic geometric information and learns highly
discriminative feature representation by effectively fusing shape and texture
information at data level. The efficacy of our approach is demonstrated for the
tasks of facial action unit detection and expression classification. The
extensive experiments conducted on the Bosphorus and BU-4DFE datasets, show
that our method produces a significant boost in the performance when compared
to state-of-the-art solutions
Exploring Methods for the Automatic Detection of Errors in Manual Transcription
Authors: Xiaofei Wang, Jinyi Yang, Ruizhi Li, Samik Sadhu, Hynek Hermansky
Date: 2019-04-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Quality of data plays an important role in most deep learning tasks. In the speech community, transcription of speech recording is indispensable. Since the transcription is usually generated artificially, automatically finding errors in manual transcriptions not only saves time and labors but benefits the performance of tasks that need the training process. Inspired by the success of hybrid automatic speech recognition using both language model and acoustic model, two approaches of automatic error detection in the transcriptions have been explored in this work. Previous study using a biased language model approach, relying on a strong transcription-dependent language model, has been reviewed. In this work, we propose a novel acoustic model based approach, focusing on the phonetic sequence of speech. Both methods have been evaluated on a completely real dataset, which was originally transcribed with errors and strictly corrected manually afterwards.
SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition
Authors: Bruno Korbar, Du Tran, Lorenzo Torresani
Date: 2019-04-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Recognition, Temporal Action Localization

While many action recognition datasets consist of collections of brief, trimmed videos each containing a relevant action, videos in the real-world (e.g., on YouTube) exhibit very different properties: they are often several minutes long, where brief relevant clips are often interleaved with segments of extended duration containing little change. Applying densely an action recognition system to every temporal clip within such videos is prohibitively expensive. Furthermore, as we show in our experiments, this results in suboptimal recognition accuracy as informative predictions from relevant clips are outnumbered by meaningless classification outputs over long uninformative sections of the video. In this paper we introduce a lightweight "clip-sampling" model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly/uniformly selected clips. On Sports1M, our clip sampling scheme elevates the accuracy of an already state-of-the-art action classifier by 7% and reduces by more than 15 times its computational cost.
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Authors: Lin Liu, Rajarshi Mukherjee, James M. Robins
Date: 2019-04-08 00:00:00
Tasks: BIG-bench Machine Learning, valid

For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators $\hat{\psi}_{1}$ are the state-of-the-art, incorporating the good prediction performance of machine learning; the decreased bias of doubly robust estimators; and the analytic tractability and bias reduction of sample splitting with cross fitting. Nonetheless, even in the absence of confounding by unmeasured factors, the nominal $(1 - \alpha)$ Wald confidence interval $\hat{\psi}_{1} \pm z_{\alpha / 2} \widehat{\mathsf{se}} [\hat{\psi}_{1}]$ may still undercover even in large samples, because the bias of $\hat{\psi}_{1}$ may be of the same or even larger order than its standard error of order $n^{-1/2}$. In this paper, we introduce essentially assumption-free tests that (i) can falsify the null hypothesis that the bias of $\hat{\psi}_{1}$ is of smaller order than its standard error, (ii) can provide an upper confidence bound on the true coverage of the Wald interval, and (iii) are valid under the null under no smoothness/sparsity assumptions on the nuisance parameters. The tests, which we refer to as \underline{A}ssumption \underline{F}ree \underline{E}mpirical \underline{C}overage \underline{T}ests (AFECTs), are based on a U-statistic that estimates part of the bias of $\hat{\psi}_{1}$.
Kinematic Synthesis of Parallel Manipulator via Neural Network Approach
Authors: J. Ghasemi, R. Moradinezhad, M. A. Hosseini
Date: 2019-04-08 00:00:00

In this research, Artificial Neural Networks (ANNs) have been used as a
powerful tool to solve the inverse kinematic equations of a parallel robot. For
this purpose, we have developed the kinematic equations of a Tricept parallel
kinematic mechanism with two rotational and one translational degrees of
freedom (DoF). Using the analytical method, the inverse kinematic equations are
solved for specific trajectory, and used as inputs for the applied ANNs. The
results of both applied networks (Multi-Layer Perceptron and Redial Basis
Function) satisfied the required performance in solving complex inverse
kinematics with proper accuracy and speed.
Surface Defect Classification in Real-Time Using Convolutional Neural Networks
Authors: Selim Arikan, Kiran varanasi, Didier Stricker
Date: 2019-04-07 00:00:00
Tasks: Classification, Data Augmentation, Defect Detection, General Classification

Surface inspection systems are an important application domain for computer
vision, as they are used for defect detection and classification in the
manufacturing industry. Existing systems use hand-crafted features which
require extensive domain knowledge to create. Even though Convolutional neural
networks (CNNs) have proven successful in many large-scale challenges,
industrial inspection systems have yet barely realized their potential due to
two significant challenges: real-time processing speed requirements and
specialized narrow domain-specific datasets which are sometimes limited in
size. In this paper, we propose CNN models that are specifically designed to
handle capacity and real-time speed requirements of surface inspection systems.
To train and evaluate our network models, we created a surface image dataset
containing more than 22000 labeled images with many types of surface materials
and achieved 98.0% accuracy in binary defect classification. To solve the class
imbalance problem in our datasets, we introduce neural data augmentation
methods which are also applicable to similar domains that suffer from the same
problem. Our results show that deep learning based methods are feasible to be
used in surface inspection systems and outperform traditional methods in
accuracy and inference time by considerable margins.
Identity-preserving Face Recovery from Stylized Portraits
Authors: Fatemeh Shiri, Xin Yu, Fatih Porikli, Richard Hartley, Piotr Koniusz
Date: 2019-04-07 00:00:00

Given an artistic portrait, recovering the latent photorealistic face that
preserves the subject's identity is challenging because the facial details are
often distorted or fully lost in artistic portraits. We develop an
Identity-preserving Face Recovery from Portraits (IFRP) method that utilizes a
Style Removal network (SRN) and a Discriminative Network (DN). Our SRN,
composed of an autoencoder with residual block-embedded skip connections, is
designed to transfer feature maps of stylized images to the feature maps of the
corresponding photorealistic faces. Owing to the Spatial Transformer Network
(STN), SRN automatically compensates for misalignments of stylized portraits to
output aligned realistic face images. To ensure the identity preservation, we
promote the recovered and ground truth faces to share similar visual features
via a distance measure which compares features of recovered and ground truth
faces extracted from a pre-trained FaceNet network. DN has multiple
convolutional and fully-connected layers, and its role is to enforce recovered
faces to be similar to authentic faces. Thus, we can recover high-quality
photorealistic faces from unaligned portraits while preserving the identity of
the face in an image. By conducting extensive evaluations on a large-scale
synthesized dataset and a hand-drawn sketch dataset, we demonstrate that our
method achieves superior face recovery and attains state-of-the-art results. In
addition, our method can recover photorealistic faces from unseen stylized
portraits, artistic paintings, and hand-drawn sketches.
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
Authors: Lu Bail, Lixin Cui, Yuhang Jiao, Luca Rossi, Edwin R. Hancock
Date: 2019-04-06 00:00:00
Tasks: General Classification, Graph Classification

In this paper, we develop a novel Backtrackless Aligned-Spatial Graph Convolutional Network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed BASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Furthermore, the proposed BASGCN model can both adaptively discriminate the importance between specified vertices during the convolution process and reduce the notorious tottering problem of existing spatially-based GCNs related to the Weisfeiler-Lehman algorithm, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.
Convex optimization for the densest subgraph and densest submatrix problems
Authors: Polina Bombina, Brendan Ames
Date: 2019-04-05 00:00:00

We consider the densest $k$-subgraph problem, which seeks to identify the
$k$-node subgraph of a given input graph with maximum number of edges. This
problem is well-known to be NP-hard, by reduction to the maximum clique
problem. We propose a new convex relaxation for the densest $k$-subgraph
problem, based on a nuclear norm relaxation of a low-rank plus sparse
decomposition of the adjacency matrices of $k$-node subgraphs to partially
address this intractability. We establish that the densest $k$-subgraph can be
recovered with high probability from the optimal solution of this convex
relaxation if the input graph is randomly sampled from a distribution of random
graphs constructed to contain an especially dense $k$-node subgraph with high
probability. Specifically, the relaxation is exact when the edges of the input
graph are added independently at random, with edges within a particular
$k$-node subgraph added with higher probability than other edges in the graph.
We provide a sufficient condition on the size of this subgraph $k$ and the
expected density under which the optimal solution of the proposed relaxation
recovers this $k$-node subgraph with high probability. Further, we propose a
first-order method for solving this relaxation based on the alternating
direction method of multipliers, and empirically confirm our predicted recovery
thresholds using simulations involving randomly generated graphs, as well as
graphs drawn from social and collaborative networks.
Combined State and Parameter Estimation in Level-Set Methods
Authors: Hans Yu, Matthew P. Juniper, Luca Magri
Date: 2019-03-01 00:00:00
Tasks: parameter estimation, State Estimation, Uncertainty Quantification

Reduced-order models based on level-set methods are widely used tools to qualitatively capture and track the nonlinear dynamics of an interface. The aim of this paper is to develop a physics-informed, data-driven, statistically rigorous learning algorithm for state and parameter estimation with level-set methods. A Bayesian approach based on data assimilation is introduced. Data assimilation is enabled by the ensemble Kalman filter and smoother, which are used in their probabilistic formulations. The level-set data assimilation framework is verified in onedimensional and two-dimensional test cases, where state estimation, parameter estimation and uncertainty quantification are performed. The statistical performance of the proposed ensemble Kalman filter and smoother is quantified by twin experiments. In the twin experiments, the combined state and parameter estimation fully recovers the reference solution, which validates the proposed algorithm. The level-set data assimilation framework is then applied to the prediction of the nonlinear dynamics of a forced premixed flame, which exhibits the formation of sharp cusps and intricate topological changes, such as pinch-off events. The proposed physics-informed statistical learning algorithm opens up new possibilities for making reduced-order models of interfaces quantitatively predictive, any time that reference data is available.
H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions
Authors: Bugra Tekin, Federica Bogo, Marc Pollefeys
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object

We present a unified framework for understanding 3D hand and object
interactions in raw image sequences from egocentric RGB cameras. Given a single
RGB image, our model jointly estimates the 3D hand and object poses, models
their interactions, and recognizes the object and action classes with a single
feed-forward pass through a neural network. We propose a single architecture
that does not rely on external detection algorithms but rather is trained
end-to-end on single images. We further merge and propagate information in the
temporal domain to infer interactions between hand and object trajectories and
recognize actions. The complete model takes as input a sequence of frames and
outputs per-frame 3D hand and object pose predictions along with the estimates
of object and action categories for the entire sequence. We demonstrate
state-of-the-art performance of our algorithm even in comparison to the
approaches that work on depth data and ground-truth annotations.
Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels
Authors: John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy
Date: 2019-04-10 00:00:00

Over recent years heterogeneous systems have become more prevalent across HPC
systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other
accelerators. These hardware platforms have different performance
characteristics and optimization requirements. In order to make the most of
multiple accelerators a developer has to provide implementations of their
algorithms tuned for each device. Hardware vendors provide libraries targeting
their devices specifically, which provide good performance but frequently have
different API designs, hampering portability.
  The SYCL programming model allows users to write heterogeneous programs using
completely standard C++, and so developers have access to the power of C++
templates when developing compute kernels. In this paper we show that by
writing highly parameterized kernels for matrix multiplies and convolutions we
achieve performance competitive with vendor implementations across different
architectures. Furthermore, tuning for new devices amounts to choosing the
combinations of kernel parameters that perform best on the hardware.