pwc-archive/papers-with-abstracts huggingface dataset:
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
Authors: Weiwei Hu, Ying Tan
Date: 2017-02-20 00:00:00
Tasks: BIG-bench Machine Learning, Generative Adversarial Network, Malware Detection

Machine learning has been used to detect new malware in recent years, while
malware authors have strong motivation to attack such algorithms. Malware
authors usually have no access to the detailed structures and parameters of the
machine learning models used by malware detection systems, and therefore they
can only perform black-box attacks. This paper proposes a generative
adversarial network (GAN) based algorithm named MalGAN to generate adversarial
malware examples, which are able to bypass black-box machine learning based
detection models. MalGAN uses a substitute detector to fit the black-box
malware detection system. A generative network is trained to minimize the
generated adversarial examples' malicious probabilities predicted by the
substitute detector. The superiority of MalGAN over traditional gradient based
adversarial example generation algorithms is that MalGAN is able to decrease
the detection rate to nearly zero and make the retraining based defensive
method against adversarial examples hard to work.
Rapid parametric density estimation
Authors: Jarek Duda
Date: 2017-02-07 00:00:00
Tasks: Clustering, Density Estimation

Parametric density estimation, for example as Gaussian distribution, is the
base of the field of statistics. Machine learning requires inexpensive
estimation of much more complex densities, and the basic approach is relatively
costly maximum likelihood estimation (MLE). There will be discussed inexpensive
density estimation, for example literally fitting a polynomial (or Fourier
series) to the sample, which coefficients are calculated by just averaging
monomials (or sine/cosine) over the sample. Another discussed basic application
is fitting distortion to some standard distribution like Gaussian - analogously
to ICA, but additionally allowing to reconstruct the disturbed density.
Finally, by using weighted average, it can be also applied for estimation of
non-probabilistic densities, like modelling mass distribution, or for various
clustering problems by using negative (or complex) weights: fitting a function
which sign (or argument) determines clusters. The estimated parameters are
approaching the optimal values with error dropping like $1/\sqrt{n}$, where $n$
is the sample size.
Latent Variable Dialogue Models and their Diversity
Authors: Kris Cao, Stephen Clark
Date: 2017-02-20 00:00:00
Proceeding: EACL 2017 4
Tasks: Decoder, Dialogue Generation, Diversity

We present a dialogue generation model that directly captures the variability
in possible responses to a given input, which reduces the `boring output' issue
of deterministic dialogue models. Experiments show that our model generates
more diverse outputs than baseline models, and also generates more consistently
acceptable output than sampling from a deterministic encoder-decoder model.
Distribution-dependent concentration inequalities for tighter generalization bounds
Authors: Xinxing Wu, Junping Zhang
Date: 2016-07-19 00:00:00
Tasks: Generalization Bounds, Learning Theory

Concentration inequalities are indispensable tools for studying the
generalization capacity of learning models. Hoeffding's and McDiarmid's
inequalities are commonly used, giving bounds independent of the data
distribution. Although this makes them widely applicable, a drawback is that
the bounds can be too loose in some specific cases. Although efforts have been
devoted to improving the bounds, we find that the bounds can be further
tightened in some distribution-dependent scenarios and conditions for the
inequalities can be relaxed. In particular, we propose four types of conditions
for probabilistic boundedness and bounded differences, and derive several
distribution-dependent extensions of Hoeffding's and McDiarmid's inequalities.
These extensions provide bounds for functions not satisfying the conditions of
the existing inequalities, and in some special cases, tighter bounds.
Furthermore, we obtain generalization bounds for unbounded and
hierarchy-bounded loss functions. Finally we discuss the potential applications
of our extensions to learning theory.
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
Authors: Patrick Ferdinand Christ, Florian Ettlinger, Georgios Kaissis, Sebastian Schlecht, Freba Ahmaddy, Felix Grün, Alexander Valentinitsch, Seyed-Ahmad Ahmadi, Rickmer Braren, Bjoern Menze
Date: 2017-02-20 00:00:00
Tasks: Classification, General Classification, Lesion Segmentation, Tumor Segmentation

Automatic non-invasive assessment of hepatocellular carcinoma (HCC)
malignancy has the potential to substantially enhance tumor treatment
strategies for HCC patients. In this work we present a novel framework to
automatically characterize the malignancy of HCC lesions from DWI images. We
predict HCC malignancy in two steps: As a first step we automatically segment
HCC tumor lesions using cascaded fully convolutional neural networks (CFCN). A
3D neural network (SurvivalNet) then predicts the HCC lesions' malignancy from
the HCC tumor segmentation. We formulate this task as a classification problem
with classes being "low risk" and "high risk" represented by longer or shorter
survival times than the median survival. We evaluated our method on DWI of 31
HCC patients. Our proposed framework achieves an end-to-end accuracy of 65%
with a Dice score for the automatic lesion segmentation of 69% and an accuracy
of 68% for tumor malignancy classification based on expert annotations. We
compared the SurvivalNet to classical handcrafted features such as Histogram
and Haralick and show experimentally that SurvivalNet outperforms the
handcrafted features in HCC malignancy classification. End-to-end assessment of
tumor malignancy based on our proposed fully automatic framework corresponds to
assessment based on expert annotations with high significance (p>0.95).
An Efficient Method for online Detection of Polychronous Patterns in Spiking Neural Network
Authors: Joseph Chrol-Cannon, Yaochu Jin, André Grüning
Date: 2017-02-20 00:00:00
Tasks: Computational Efficiency, TAG

Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.
Correlation Hashing Network for Efficient Cross-Modal Retrieval
Authors: Yue Cao, Mingsheng Long, Jian-Min Wang, Philip S. Yu
Date: 2016-02-22 00:00:00
Tasks: Cross-Modal Retrieval, Quantization, Retrieval

Hashing is widely applied to approximate nearest neighbor search for
large-scale multimodal retrieval with storage and computation efficiency.
Cross-modal hashing improves the quality of hash coding by exploiting semantic
correlations across different modalities. Existing cross-modal hashing methods
first transform data into low-dimensional feature vectors, and then generate
binary codes by another separate quantization step. However, suboptimal hash
codes may be generated since the quantization error is not explicitly minimized
and the feature representation is not jointly optimized with the binary codes.
This paper presents a Correlation Hashing Network (CHN) approach to cross-modal
hashing, which jointly learns good data representation tailored to hash coding
and formally controls the quantization error. The proposed CHN is a hybrid deep
architecture that constitutes a convolutional neural network for learning good
image representations, a multilayer perception for learning good text
representations, two hashing layers for generating compact binary codes, and a
structured max-margin loss that integrates all things together to enable
learning similarity-preserving and high-quality hash codes. Extensive empirical
study shows that CHN yields state of the art cross-modal retrieval performance
on standard benchmarks.
On the Consistency of $k$-means++ algorithm
Authors: Mieczysław A. Kłopotek
Date: 2017-02-20 00:00:00
Tasks: Clustering

We prove in this paper that the expected value of the objective function of
the $k$-means++ algorithm for samples converges to population expected value.
As $k$-means++, for samples, provides with constant factor approximation for
$k$-means objectives, such an approximation can be achieved for the population
with increase of the sample size.
  This result is of potential practical relevance when one is considering using
subsampling when clustering large data sets (large data bases).
Efficient Large-scale Approximate Nearest Neighbor Search on the GPU
Authors: Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung, Hendrik P. A. Lensch
Date: 2017-02-20 00:00:00
Proceeding: CVPR 2016 6
Tasks: CPU, GPU, Quantization, Re-Ranking

We present a new approach for efficient approximate nearest neighbor (ANN)
search in high dimensional spaces, extending the idea of Product Quantization.
We propose a two-level product and vector quantization tree that reduces the
number of vector comparisons required during tree traversal. Our approach also
includes a novel highly parallelizable re-ranking method for candidate vectors
by efficiently reusing already computed intermediate values. Due to its small
memory footprint during traversal, the method lends itself to an efficient,
parallel GPU implementation. This Product Quantization Tree (PQT) approach
significantly outperforms recent state of the art methods for high dimensional
nearest neighbor queries on standard reference datasets. Ours is the first work
that demonstrates GPU performance superior to CPU performance on high
dimensional, large scale ANN problems in time-critical real-world applications,
like loop-closing in videos.
Memory Efficient Max Flow for Multi-label Submodular MRFs
Authors: Thalaiyasingam Ajanthan, Richard Hartley, Mathieu Salzmann
Date: 2017-02-20 00:00:00
Proceeding: CVPR 2016 6

Multi-label submodular Markov Random Fields (MRFs) have been shown to be
solvable using max-flow based on an encoding of the labels proposed by
Ishikawa, in which each variable $X_i$ is represented by $\ell$ nodes (where
$\ell$ is the number of labels) arranged in a column. However, this method in
general requires $2\,\ell^2$ edges for each pair of neighbouring variables.
This makes it inapplicable to realistic problems with many variables and
labels, due to excessive memory requirement. In this paper, we introduce a
variant of the max-flow algorithm that requires much less storage.
Consequently, our algorithm makes it possible to optimally solve multi-label
submodular problems involving large numbers of variables and labels on a
standard computer.
Probabilistic Saliency Estimation
Authors: Caglar Aytekin, Alexandros Iosifidis, Moncef Gabbouj
Date: 2016-09-13 00:00:00
Tasks: Object, object-detection, Object Detection, One-Class Classification, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection

In this paper, we model the salient object detection problem under a
probabilistic framework encoding the boundary connectivity saliency cue and
smoothness constraints in an optimization problem. We show that this problem
has a closed form global optimum which estimates the salient object. We further
show that along with the probabilistic framework, the proposed method also
enjoys a wide range of interpretations, i.e. graph cut, diffusion maps and
one-class classification. With an analysis according to these interpretations,
we also find that our proposed method provides approximations to the global
optimum to another criterion that integrates local/global contrast and large
area saliency cues. The proposed approach achieves mostly leading performance
compared to the state-of-the-art algorithms over a large set of salient object
detection datasets including around 17k images for several evaluation metrics.
Furthermore, the computational complexity of the proposed method is
favorable/comparable to many state-of-the-art techniques.
From Photo Streams to Evolving Situations
Authors: Mengfan Tang, Feiping Nie, Siripen Pongpaichet, Ramesh Jain
Date: 2017-02-20 00:00:00

Photos are becoming spontaneous, objective, and universal sources of
information. This paper develops evolving situation recognition using photo
streams coming from disparate sources combined with the advances of deep
learning. Using visual concepts in photos together with space and time
information, we formulate the situation detection into a semi-supervised
learning framework and propose new graph-based models to solve the problem. To
extend the method for unknown situations, we introduce a soft label method
which enables the traditional semi-supervised learning framework to accurately
predict predefined labels as well as effectively form new clusters. To overcome
the noisy data which degrades graph quality, leading to poor recognition
results, we take advantage of two kinds of noise-robust norms which can
eliminate the adverse effects of outliers in visual concepts and improve the
accuracy of situation recognition. Finally, we demonstrate the idea and the
effectiveness of the proposed model on Yahoo Flickr Creative Commons 100
Million.
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Authors: Song Han, Junlong Kang, Huizi Mao, Yiming Hu, Xin Li, Yubin Li, Dongliang Xie, Hong Luo, Song Yao, Yu Wang, Huazhong Yang, William J. Dally
Date: 2016-12-01 00:00:00
Tasks: CPU, GPU, Quantization, speech-recognition, Speech Recognition

Long Short-Term Memory (LSTM) is widely used in speech recognition. In order
to achieve higher prediction accuracy, machine learning scientists have built
larger and larger models. Such large model is both computation intensive and
memory intensive. Deploying such bulky model results in high power consumption
and leads to high total cost of ownership (TCO) of a data center. In order to
speedup the prediction and make it energy efficient, we first propose a
load-balance-aware pruning method that can compress the LSTM model size by 20x
(10x from pruning and 2x from quantization) with negligible loss of the
prediction accuracy. The pruned model is friendly for parallel processing.
Next, we propose scheduler that encodes and partitions the compressed model to
each PE for parallelism, and schedule the complicated LSTM data flow. Finally,
we design the hardware architecture, named Efficient Speech Recognition Engine
(ESE) that works directly on the compressed model. Implemented on Xilinx
XCKU060 FPGA running at 200MHz, ESE has a performance of 282 GOPS working
directly on the compressed LSTM network, corresponding to 2.52 TOPS on the
uncompressed one, and processes a full LSTM for speech recognition with a power
dissipation of 41 Watts. Evaluated on the LSTM for speech recognition
benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X
GPU implementations. It achieves 40x and 11.5x higher energy efficiency
compared with the CPU and GPU respectively.
Hemingway: Modeling Distributed Optimization Algorithms
Authors: Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez
Date: 2017-02-20 00:00:00
Tasks: Distributed Optimization

Distributed optimization algorithms are widely used in many industrial
machine learning applications. However choosing the appropriate algorithm and
cluster size is often difficult for users as the performance and convergence
rate of optimization algorithms vary with the size of the cluster. In this
paper we make the case for an ML-optimizer that can select the appropriate
algorithm and cluster size to use for a given problem. To do this we propose
building two models: one that captures the system level characteristics of how
computation, communication change as we increase cluster sizes and another that
captures how convergence rates change with cluster sizes. We present
preliminary results from our prototype implementation called Hemingway and
discuss some of the challenges involved in developing such a system.
Progressively Diffused Networks for Semantic Image Segmentation
Authors: Ruimao Zhang, Wei Yang, Zhanglin Peng, Xiaogang Wang, Liang Lin
Date: 2017-02-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper introduces Progressively Diffused Networks (PDNs) for unifying
multi-scale context modeling with deep feature learning, by taking semantic
image segmentation as an exemplar application. Prior neural networks, such as
ResNet, tend to enhance representational power by increasing the depth of
architectures and driving the training objective across layers. However, we
argue that spatial dependencies in different layers, which generally represent
the rich contexts among data elements, are also critical to building deep and
discriminative representations. To this end, our PDNs enables to progressively
broadcast information over the learned feature maps by inserting a stack of
information diffusion layers, each of which exploits multi-dimensional
convolutional LSTMs (Long-Short-Term Memory Structures). In each LSTM unit, a
special type of atrous filters are designed to capture the short range and long
range dependencies from various neighbors to a certain site of the feature map
and pass the accumulated information to the next layer. From the extensive
experiments on semantic image segmentation benchmarks (e.g., ImageNet Parsing,
PASCAL VOC2012 and PASCAL-Part), our framework demonstrates the effectiveness
to substantially improve the performances over the popular existing neural
network models, and achieves state-of-the-art on ImageNet Parsing for large
scale semantic segmentation.
Post-edit Analysis of Collective Biography Generation
Authors: Bo Han, Will Radford, Anaïs Cadilhac, Art Harol, Andrew Chisholm, Ben Hachey
Date: 2017-02-20 00:00:00
Tasks: Text Generation

Text generation is increasingly common but often requires manual post-editing
where high precision is critical to end users. However, manual editing is
expensive so we want to ensure this effort is focused on high-value tasks. And
we want to maintain stylistic consistency, a particular challenge in crowd
settings. We present a case study, analysing human post-editing in the context
of a template-based biography generation system. An edit flow visualisation
combined with manual characterisation of edits helps identify and prioritise
work for improving end-to-end efficiency and accuracy.
Compressive Embedding and Visualization using Graphs
Authors: Johan Paratte, Nathanaël Perraudin, Pierre Vandergheynst
Date: 2017-02-19 00:00:00

Visualizing high-dimensional data has been a focus in data analysis
communities for decades, which has led to the design of many algorithms, some
of which are now considered references (such as t-SNE for example). In our era
of overwhelming data volumes, the scalability of such methods have become more
and more important. In this work, we present a method which allows to apply any
visualization or embedding algorithm on very large datasets by considering only
a fraction of the data as input and then extending the information to all data
points using a graph encoding its global similarity. We show that in most
cases, using only $\mathcal{O}(\log(N))$ samples is sufficient to diffuse the
information to all $N$ data points. In addition, we propose quantitative
methods to measure the quality of embeddings and demonstrate the validity of
our technique on both synthetic and real-world datasets.
Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
Authors: Babak Ehteshami Bejnordi, Jimmy Linz, Ben Glass, Maeve Mullooly, Gretchen L Gierach, Mark E Sherman, Nico Karssemeijer, Jeroen van der Laak, Andrew H. Beck
Date: 2017-02-19 00:00:00
Tasks: Diagnostic, General Classification

Diagnosis of breast carcinomas has so far been limited to the morphological
interpretation of epithelial cells and the assessment of epithelial tissue
architecture. Consequently, most of the automated systems have focused on
characterizing the epithelial regions of the breast to detect cancer. In this
paper, we propose a system for classification of hematoxylin and eosin (H&E)
stained breast specimens based on convolutional neural networks that primarily
targets the assessment of tumor-associated stroma to diagnose breast cancer
patients. We evaluate the performance of our proposed system using a large
cohort containing 646 breast tissue biopsies. Our evaluations show that the
proposed system achieves an area under ROC of 0.92, demonstrating the
discriminative power of previously neglected tumor-associated stroma as a
diagnostic biomarker.
Collaborative Deep Reinforcement Learning
Authors: Kaixiang Lin, Shu Wang, Jiayu Zhou
Date: 2017-02-19 00:00:00
Tasks: Deep Reinforcement Learning, Knowledge Distillation, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Besides independent learning, human learning process is highly improved by
summarizing what has been learned, communicating it with peers, and
subsequently fusing knowledge from different sources to assist the current
learning goal. This collaborative learning procedure ensures that the knowledge
is shared, continuously refined, and concluded from different perspectives to
construct a more profound understanding. The idea of knowledge transfer has led
to many advances in machine learning and data mining, but significant
challenges remain, especially when it comes to reinforcement learning,
heterogeneous model structures, and different learning tasks. Motivated by
human collaborative learning, in this paper we propose a collaborative deep
reinforcement learning (CDRL) framework that performs adaptive knowledge
transfer among heterogeneous learning agents. Specifically, the proposed CDRL
conducts a novel deep knowledge distillation method to address the
heterogeneity among different learning tasks with a deep alignment network.
Furthermore, we present an efficient collaborative Asynchronous Advantage
Actor-Critic (cA3C) algorithm to incorporate deep knowledge distillation into
the online training of agents, and demonstrate the effectiveness of the CDRL
framework using extensive empirical evaluation on OpenAI gym.