pwc-archive/papers-with-abstracts huggingface dataset:
Attribution-driven Causal Analysis for Detection of Adversarial Examples
Authors: Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, Ananthram Swami
Date: 2019-03-14 00:00:00
Tasks: Adversarial Attack, BIG-bench Machine Learning

Attribution methods have been developed to explain the decision of a machine
learning model on a given input. We use the Integrated Gradient method for
finding attributions to define the causal neighborhood of an input by
incrementally masking high attribution features. We study the robustness of
machine learning models on benign and adversarial inputs in this neighborhood.
Our study indicates that benign inputs are robust to the masking of high
attribution features but adversarial inputs generated by the state-of-the-art
adversarial attack methods such as DeepFool, FGSM, CW and PGD, are not robust
to such masking. Further, our study demonstrates that this concentration of
high-attribution features responsible for the incorrect decision is more
pronounced in physically realizable adversarial examples. This difference in
attribution of benign and adversarial inputs can be used to detect adversarial
examples. Such a defense approach is independent of training data and attack
method, and we demonstrate its effectiveness on digital and physically
realizable perturbations.
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Authors: Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning

Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense matrix-vector multiplication, yet each has a specialized and highly efficient (subquadratic) algorithm. We ask to what extent hand-crafting these algorithms and implementations is necessary, what structural priors they encode, and how much knowledge is required to automatically learn a fast algorithm for a provided structured transform. Motivated by a characterization of fast matrix-vector multiplication as products of sparse matrices, we introduce a parameterization of divide-and-conquer methods that is capable of representing a large class of transforms. This generic formulation can automatically learn an efficient algorithm for many important transforms; for example, it recovers the $O(N \log N)$ Cooley-Tukey FFT algorithm to machine precision, for dimensions $N$ up to $1024$. Furthermore, our method can be incorporated as a lightweight replacement of generic matrices in machine learning pipelines to learn efficient and compressible transformations. On a standard task of compressing a single hidden-layer network, our method exceeds the classification accuracy of unconstrained matrices on CIFAR-10 by 3.9 points -- the first time a structured approach has done so -- with 4X faster inference speed and 40X fewer parameters.
High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
Authors: Siqi Wang, Gayathri Ananthanarayanan, Yifan Zeng, Neeraj Goel, Anuj Pathania, Tulika Mitra
Date: 2019-03-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent commercial edge devices. It comprises of single-ISA heterogeneous cores grouped into multiple homogeneous clusters that enable power and performance trade-offs. All cores are expected to be simultaneously employed in inference to attain maximal throughput. However, high communication overhead involved in parallelization of computations from convolution kernels across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs pipelined design to split convolutional layers across clusters while limiting parallelization of their respective kernels to the assigned cluster. We develop a performance-prediction model that utilizes only the convolutional layer descriptors to predict the execution time of each layer individually on all permitted core configurations (type and count). Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in a 39% higher throughput than the highest antecedent throughput.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Authors: Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang, Tie-Yan Liu
Date: 2019-03-14 00:00:00
Tasks: Atari Games, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its direct use may fail to converge even in value iteration. In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning. Experimental results on GridWorld show that the DBS operator enables better estimation of the value function, which rectifies the convergence issue of the softmax operator. Finally, we propose the DBS-DQN algorithm by applying dynamic Boltzmann softmax updates in deep Q-network, which outperforms DQN substantially in 40 out of 49 Atari games.
Low-rank Kernel Learning for Graph-based Clustering
Authors: Zhao Kang, Liangjian Wen, Wenyu Chen, Zenglin Xu
Date: 2019-03-14 00:00:00
Tasks: Clustering, graph construction, Graph Learning

Constructing the adjacency graph is fundamental to graph-based clustering.
Graph learning in kernel space has shown impressive performance on a number of
benchmark data sets. However, its performance is largely determined by the
chosen kernel matrix. To address this issue, the previous multiple kernel
learning algorithm has been applied to learn an optimal kernel from a group of
predefined kernels. This approach might be sensitive to noise and limits the
representation ability of the consensus kernel. In contrast to existing
methods, we propose to learn a low-rank kernel matrix which exploits the
similarity nature of the kernel matrix and seeks an optimal kernel from the
neighborhood of candidate kernels. By formulating graph construction and kernel
learning in a unified framework, the graph and consensus kernel can be
iteratively enhanced by each other. Extensive experimental results validate the
efficacy of the proposed method.
Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness
Authors: Jiawang Bai, Yiming Li, Jiawei Li, Yong Jiang, Shu-Tao Xia
Date: 2019-03-14 00:00:00
Tasks: Knowledge Distillation

How to obtain a model with good interpretability and performance has always been an important research topic. In this paper, we propose rectified decision trees (ReDT), a knowledge distillation based decision trees rectification with high interpretability, small model size, and empirical soundness. Specifically, we extend the impurity calculation and the pure ending condition of the classical decision tree to propose a decision tree extension that allows the use of soft labels generated by a well-trained teacher model in training and prediction process. It is worth noting that for the acquisition of soft labels, we propose a new multiple cross-validation based method to reduce the effects of randomness and overfitting. These approaches ensure that ReDT retains excellent interpretability and even achieves fewer nodes than the decision tree in the aspect of compression while having relatively good performance. Besides, in contrast to traditional knowledge distillation, back propagation of the student model is not necessarily required in ReDT, which is an attempt of a new knowledge distillation approach. Extensive experiments are conducted, which demonstrates the superiority of ReDT in interpretability, compression, and empirical soundness.
Inferring Personalized Bayesian Embeddings for Learning from Heterogeneous Demonstration
Authors: Rohan Paleja, Matthew Gombolay
Date: 2019-03-14 00:00:00
Tasks: Decision Making

For assistive robots and virtual agents to achieve ubiquity, machines will
need to anticipate the needs of their human counterparts. The field of Learning
from Demonstration (LfD) has sought to enable machines to infer predictive
models of human behavior for autonomous robot control. However, humans exhibit
heterogeneity in decision-making, which traditional LfD approaches fail to
capture. To overcome this challenge, we propose a Bayesian LfD framework to
infer an integrated representation of all human task demonstrators by inferring
human-specific embeddings, thereby distilling their unique characteristics. We
validate our approach is able to outperform state-of-the-art techniques on both
synthetic and real-world data sets.
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
Authors: Wouter Kool, Herke van Hoof, Max Welling
Date: 2019-03-14 00:00:00
Tasks: Sentence, Translation

The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample $k$ elements without replacement. We show how to implicitly apply this 'Gumbel-Top-$k$' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Search. Even for exponentially large domains, the number of model evaluations grows only linear in $k$ and the maximum sampled sequence length. The algorithm creates a theoretical connection between sampling and (deterministic) beam search and can be used as a principled intermediate alternative. In a translation task, the proposed method compares favourably against alternatives to obtain diverse yet good quality translations. We show that sequences sampled without replacement can be used to construct low-variance estimators for expected sentence-level BLEU score and model entropy.
Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation
Authors: Giuseppe G. Calvi, Ahmad Moniri, Mahmoud Mahfouz, Qibin Zhao, Danilo P. Mandic
Date: 2019-03-14 00:00:00

This work aims to help resolve the two main stumbling blocks in the application of Deep Neural Networks (DNNs), that is, the exceedingly large number of trainable parameters and their physical interpretability. This is achieved through a tensor valued approach, based on the proposed Tucker Tensor Layer (TTL), as an alternative to the dense weight-matrices of DNNs. This allows us to treat the weight-matrices of general DNNs as a matrix unfolding of a higher order weight-tensor. By virtue of the compression properties of tensor decompositions, this enables us to introduce a novel and efficient framework for exploiting the multi-way nature of the weight-tensor in order to dramatically reduce the number of DNN parameters. We also derive the tensor valued back-propagation algorithm within the TTL framework, by extending the notion of matrix derivatives to tensors. In this way, the physical interpretability of the Tucker decomposition is exploited to gain physical insights into the NN training, through the process of computing gradients with respect to each factor matrix. The proposed framework is validated on both synthetic data, and the benchmark datasets MNIST, Fashion-MNIST, and CIFAR-10. Overall, through the ability to provide the relative importance of each data feature in training, the TTL back-propagation is shown to help mitigate the "black-box" nature inherent to NNs. Experiments also illustrate that the TTL achieves a 66.63-fold compression on MNIST and Fashion-MNIST, while, by simplifying the VGG-16 network, it achieves a 10\% speed up in training time, at a comparable performance.
Deep Switch Networks for Generating Discrete Data and Language
Authors: Payam Delgosha, Naveen Goela
Date: 2019-03-14 00:00:00
Tasks: Decoder, Time Series, Time Series Analysis

Multilayer switch networks are proposed as artificial generators of
high-dimensional discrete data (e.g., binary vectors, categorical data, natural
language, network log files, and discrete-valued time series). Unlike
deconvolution networks which generate continuous-valued data and which consist
of upsampling filters and reverse pooling layers, multilayer switch networks
are composed of adaptive switches which model conditional distributions of
discrete random variables. An interpretable, statistical framework is
introduced for training these nonlinear networks based on a maximum-likelihood
objective function. To learn network parameters, stochastic gradient descent is
applied to the objective. This direct optimization is stable until convergence,
and does not involve back-propagation over separate encoder and decoder
networks, or adversarial training of dueling networks. While training remains
tractable for moderately sized networks, Markov-chain Monte Carlo (MCMC)
approximations of gradients are derived for deep networks which contain latent
variables. The statistical framework is evaluated on synthetic data,
high-dimensional binary data of handwritten digits, and web-crawled natural
language data. Aspects of the model's framework such as interpretability,
computational complexity, and generalization ability are discussed.
Deep Reinforcement Learning with Feedback-based Exploration
Authors: Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober
Date: 2019-03-14 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has enabled the control of increasingly complex
and high-dimensional problems. However, the need of vast amounts of data before
reasonable performance is attained prevents its widespread application. We
employ binary corrective feedback as a general and intuitive manner to
incorporate human intuition and domain knowledge in model-free machine
learning. The uncertainty in the policy and the corrective feedback is combined
directly in the action space as probabilistic conditional exploration. As a
result, the greatest part of the otherwise ignorant learning process can be
avoided. We demonstrate the proposed method, Predictive Probabilistic Merging
of Policies (PPMP), in combination with DDPG. In experiments on continuous
control problems of the OpenAI Gym, we achieve drastic improvements in sample
efficiency, final performance, and robustness to erroneous feedback, both for
human and synthetic feedback. Additionally, we show solutions beyond the
demonstrated knowledge.
A Comprehensive Analysis of 2D&3D Video Watching of EEG Signals by Increasing PLSR and SVM Classification Results
Authors: Negin Manshouri, Temel Kayikcioglu
Date: 2019-03-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

Despite the development of two and three dimensional (2D&3D) technology, it
has attracted the attention of researchers in recent years. This research is
done to reveal the detailed effects of 2D in comparison with 3D technology on
the human brain waves. The impact of 2D&3D video watching using
electroencephalography (EEG) brain signals is studied. A group of eight healthy
volunteers with the average age of 31+-3.06 years old participated in this
three-stage test. EEG signal recording consisted of three stages: After a bit
of relaxation (a), a 2D video was displayed (b), the recording of the signal
continued for a short period of time as rest (c), and finally the trial ended.
Exactly the same steps were repeated for the 3D video. Power spectrum density
(PSD) based on short time Fourier transform (STFT) was used to analyze the
brain signals of 2D&3D video viewers. After testing all the EEG frequency
bands, delta and theta were extracted as the features. Partial least squares
regression (PLSR) and Support vector machine (SVM) classification algorithms
were considered in order to classify EEG signals obtained as the result of
2D&3D video watching. Successful classification results were obtained by
selecting the correct combinations of effective channels representing the brain
regions.
Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
Authors: Sanjay Thakur, Herke van Hoof, Juan Camilo Gamboa Higuera, Doina Precup, David Meger
Date: 2019-03-13 00:00:00
Tasks: Diversity

Diversity of environments is a key challenge that causes learned robotic
controllers to fail due to the discrepancies between the training and
evaluation conditions. Training from demonstrations in various conditions can
mitigate---but not completely prevent---such failures. Learned controllers such
as neural networks typically do not have a notion of uncertainty that allows to
diagnose an offset between training and testing conditions, and potentially
intervene. In this work, we propose to use Bayesian Neural Networks, which have
such a notion of uncertainty. We show that uncertainty can be leveraged to
consistently detect situations in high-dimensional simulated and real robotic
domains in which the performance of the learned controller would be sub-par.
Also, we show that such an uncertainty based solution allows making an informed
decision about when to invoke a fallback strategy. One fallback strategy is to
request more data. We empirically show that providing data only when requested
results in increased data-efficiency.
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
Authors: Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard Harang
Date: 2019-03-13 00:00:00
Tasks: Attribute, Malware Detection, TAG

Malware detection is a popular application of Machine Learning for
Information Security (ML-Sec), in which an ML classifier is trained to predict
whether a given file is malware or benignware. Parameters of this classifier
are typically optimized such that outputs from the model over a set of input
samples most closely match the samples' true malicious/benign (1/0) target
labels. However, there are often a number of other sources of contextual
metadata for each malware sample, beyond an aggregate malicious/benign label,
including multiple labeling sources and malware type information (e.g.,
ransomware, trojan, etc.), which we can feed to the classifier as auxiliary
prediction targets. In this work, we fit deep neural networks to multiple
additional targets derived from metadata in a threat intelligence feed for
Portable Executable (PE) malware and benignware, including a multi-source
malicious/benign loss, a count loss on multi-source detections, and a semantic
malware attribute tag loss. We find that incorporating multiple auxiliary loss
terms yields a marked improvement in performance on the main detection task. We
also demonstrate that these gains likely stem from a more informed neural
network representation and are not due to a regularization artifact of
multi-target learning. Our auxiliary loss architecture yields a significant
reduction in detection error rate (false negatives) of 42.6% at a false
positive rate (FPR) of $10^{-3}$ when compared to a similar model with only one
target, and a decrease of 53.8% at $10^{-5}$ FPR.
Cardinality Estimation in a Virtualized Network Device Using Online Machine Learning
Authors: Reuven Cohen, Yuval Nezri
Date: 2019-03-13 00:00:00
Tasks: BIG-bench Machine Learning, CPU

Cardinality estimation algorithms receive a stream of elements, with possible
repetitions, and return the number of distinct elements in the stream. Such
algorithms seek to minimize the required memory and CPU resource consumption at
the price of inaccuracy in their output. In computer networks, cardinality
estimation algorithms are mainly used for counting the number of distinct
flows, and they are divided into two categories: sketching algorithms and
sampling algorithms. Sketching algorithms require the processing of all
packets, and they are therefore usually implemented by dedicated hardware.
Sampling algorithms do not require processing of all packets, but they are
known for their inaccuracy. In this work we identify one of the major drawbacks
of sampling-based cardinality estimation algorithms: their inability to adapt
to changes in flow size distribution. To address this problem, we propose a new
sampling-based adaptive cardinality estimation framework, which uses online
machine learning. We evaluate our framework using real traffic traces, and show
significantly better accuracy compared to the best known sampling-based
algorithms, for the same fraction of processed packets.
Maybe Deep Neural Networks are the Best Choice for Modeling Source Code
Authors: Rafael-Michael Karampatsis, Charles Sutton
Date: 2019-03-13 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation

Statistical language modeling techniques have successfully been applied to
source code, yielding a variety of new software development tools, such as
tools for code suggestion and improving readability. A major issue with these
techniques is that code introduces new vocabulary at a far higher rate than
natural language, as new identifier names proliferate. But traditional language
models limit the vocabulary to a fixed set of common words. For code, this
strong assumption has been shown to have a significant negative effect on
predictive performance. But the open vocabulary version of the neural network
language models for code have not been introduced in the literature. We present
a new open-vocabulary neural language model for code that is not limited to a
fixed vocabulary of identifier names. We employ a segmentation into subword
units, subsequences of tokens chosen based on a compression criterion,
following previous work in machine translation. Our network achieves best in
class performance, outperforming even the state-of-the-art methods of
Hellendoorn and Devanbu that are designed specifically to model code.
Furthermore, we present a simple method for dynamically adapting the model to a
new test project, resulting in increased performance. We showcase our
methodology on code corpora in three different languages of over a billion
tokens each, hundreds of times larger than in previous work. To our knowledge,
this is the largest neural language model for code that has been reported.