pwc-archive/papers-with-abstracts huggingface dataset:
Estimating Buildings' Parameters over Time Including Prior Knowledge
Authors: Nilavra Pathak, James Foulds, Nirmalya Roy, Nilanjan Banerjee, Ryan Robucci
Date: 2019-01-09 00:00:00
Tasks: Causal Inference, parameter estimation, Transfer Learning, Variational Inference

Modeling buildings' heat dynamics is a complex process which depends on
various factors including weather, building thermal capacity, insulation
preservation, and residents' behavior. Gray-box models offer a causal inference
of those dynamics expressed in few parameters specific to built environments.
These parameters can provide compelling insights into the characteristics of
building artifacts and have various applications such as forecasting HVAC
usage, indoor temperature control monitoring of built environments, etc. In
this paper, we present a systematic study of modeling buildings' thermal
characteristics and thus derive the parameters of built conditions with a
Bayesian approach. We build a Bayesian state-space model that can adapt and
incorporate buildings' thermal equations and propose a generalized solution
that can easily adapt prior knowledge regarding the parameters. We show that a
faster approximate approach using variational inference for parameter
estimation can provide similar parameters as that of a more time-consuming
Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on
two datasets to understand the generative process and show that the Bayesian
approach is more interpretable. We further study the effects of prior selection
for the model parameters and transfer learning, where we learn parameters from
one season and use them to fit the model in the other. We perform extensive
evaluations on controlled and real data traces to enumerate buildings'
parameter within a 95% credible interval.
SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detection
Authors: Faiq Khalid, Syed Rafay Hasan, Osman Hasan, Muhammad Shafique
Date: 2018-11-04 00:00:00

Timely detection of Hardware Trojans (HTs) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom. The main idea is to model the communication using multiple side-channel information like the Hurst exponent, the standard deviation of the injection distribution, and the hop distribution jointly to accurately identify HT-based online anomalies (that affects the communication without affecting the protocols or control signals). At design time, our methodology employs a "property specification language" to define and embed assertions in the RTL, specifying the correct communication behavior of a given SoC. At run-time, it monitors the anomalies in the communication behavior by checking the execution patterns against these assertions. For illustration, we evaluate SIMCom for three SoCs, i.e., SoC1 ( four single-core MC8051 and UART modules), SoC2 (four single-core MC8051, AES, ethernet, memctrl, BasicRSA, RS232 modules), and SoC3 (four single-core LEON3 connected with each other and AES, ethernet, memctrl, BasicRSA, RS23s modules microcontrollers). The experimental results show that with the combined analysis of multiple statistical parameters, SIMCom is able to detect all the benchmark Trojans (available on trust-hub) with less than 1% area and power overhead.
Malware Detection Using Dynamic Birthmarks
Authors: Swapna Vemparala, Fabio Di Troia, Corrado A. Visaggio, Thomas H. Austin, Mark Stamp
Date: 2019-01-06 00:00:00
Tasks: Malware Detection

In this paper, we explore the effectiveness of dynamic analysis techniques
for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden
Markov Models (PHMMs), both trained on sequences of API calls. We contrast our
results to static analysis using HMMs trained on sequences of opcodes, and show
that dynamic analysis achieves significantly stronger results in many cases.
Furthermore, in contrasting our two dynamic analysis techniques, we find that
using PHMMs consistently outperforms our analysis based on HMMs.
Rank consistent ordinal regression for neural networks with application to age estimation
Authors: Wenzhi Cao, Vahid Mirjalili, Sebastian Raschka
Date: 2019-01-20 00:00:00
Tasks: Age And Gender Classification, Age Estimation, Binary Classification, Gender Prediction, General Classification, Prediction, regression

In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.
Bottom-up Object Detection by Grouping Extreme and Center Points
Authors: Xingyi Zhou, Jiacheng Zhuo, Philipp Krähenbühl
Date: 2019-01-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Keypoint Estimation, Object, object-detection, Object Detection

With the advent of deep learning, object detection drifted from a bottom-up
to a top-down recognition problem. State of the art algorithms enumerate a
near-exhaustive list of object locations and classify each into: object or not.
In this paper, we show that bottom-up approaches still perform competitively.
We detect four extreme points (top-most, left-most, bottom-most, right-most)
and one center point of objects using a standard keypoint estimation network.
We group the five keypoints into a bounding box if they are geometrically
aligned. Object detection is then a purely appearance-based keypoint estimation
problem, without region classification or implicit feature learning. The
proposed method performs on-par with the state-of-the-art region based
detection methods, with a bounding box AP of 43.2% on COCO test-dev. In
addition, our estimated extreme points directly span a coarse octagonal mask,
with a COCO Mask AP of 18.9%, much better than the Mask AP of vanilla bounding
boxes. Extreme point guided segmentation further improves this to 34.6% Mask
AP.
Analysis of Financial Credit Risk Using Machine Learning
Authors: Jacky C. K. Chow
Date: 2018-02-14 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes

Corporate insolvency can have a devastating effect on the economy. With an
increasing number of companies making expansion overseas to capitalize on
foreign resources, a multinational corporate bankruptcy can disrupt the world's
financial ecosystem. Corporations do not fail instantaneously; objective
measures and rigorous analysis of qualitative (e.g. brand) and quantitative
(e.g. econometric factors) data can help identify a company's financial risk.
Gathering and storage of data about a corporation has become less difficult
with recent advancements in communication and information technologies. The
remaining challenge lies in mining relevant information about a company's
health hidden under the vast amounts of data, and using it to forecast
insolvency so that managers and stakeholders have time to react. In recent
years, machine learning has become a popular field in big data analytics
because of its success in learning complicated models. Methods such as support
vector machines, adaptive boosting, artificial neural networks, and Gaussian
processes can be used for recognizing patterns in the data (with a high degree
of accuracy) that may not be apparent to human analysts. This thesis studied
corporate bankruptcy of manufacturing companies in Korea and Poland using
experts' opinions and financial measures, respectively. Using publicly
available datasets, several machine learning methods were applied to learn the
relationship between the company's current state and its fate in the near
future. Results showed that predictions with accuracy greater than 95% were
achievable using any machine learning technique when informative features like
experts' assessment were used. However, when using purely financial factors to
predict whether or not a company will go bankrupt, the correlation is not as
strong.
Hamiltonian Monte-Carlo for Orthogonal Matrices
Authors: Viktor Yanush, Dmitry Kropotov
Date: 2019-01-23 00:00:00
Tasks: Riemannian optimization

We consider the problem of sampling from posterior distributions for Bayesian
models where some parameters are restricted to be orthogonal matrices. Such
matrices are sometimes used in neural networks models for reasons of
regularization and stabilization of training procedures, and also can
parameterize matrices of bounded rank, positive-definite matrices and others.
In \citet{byrne2013geodesic} authors have already considered sampling from
distributions over manifolds using exact geodesic flows in a scheme similar to
Hamiltonian Monte Carlo (HMC). We propose new sampling scheme for a set of
orthogonal matrices that is based on the same approach, uses ideas of
Riemannian optimization and does not require exact computation of geodesic
flows. The method is theoretically justified by proof of symplecticity for the
proposed iteration. In experiments we show that the new scheme is comparable or
faster in time per iteration and more sample-efficient comparing to
conventional HMC with explicit orthogonal parameterization and Geodesic
Monte-Carlo. We also provide promising results of Bayesian ensembling for
orthogonal neural networks and low-rank matrix factorization.
Learning to Collaborate in Markov Decision Processes
Authors: Goran Radanovic, Rati Devidze, David C. Parkes, Adish Singla
Date: 2019-01-23 00:00:00

We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting. We study the problem of designing a learning algorithm for the first agent (A1) that facilitates a successful collaboration even in cases when the second agent (A2) is adapting its policy in an unknown way. The key challenge in our setting is that the first agent faces non-stationarity in rewards and transitions because of the adaptive behavior of the second agent. We design novel online learning algorithms for agent A1 whose regret decays as $O(T^{\max\{1-\frac{3}{7} \cdot \alpha, \frac{1}{4}\}})$ with $T$ learning episodes provided that the magnitude of agent A2's policy changes between any two consecutive episodes are upper bounded by $O(T^{-\alpha})$. Here, the parameter $\alpha$ is assumed to be strictly greater than $0$, and we show that this assumption is necessary provided that the learning parity with noise problem is computationally hard. We show that sub-linear regret of agent A1 further implies near-optimality of the agents' joint return for MDPs that manifest the properties of a smooth game.
A Universally Optimal Multistage Accelerated Stochastic Gradient Method
Authors: Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar
Date: 2019-01-23 00:00:00
Proceeding: NeurIPS 2019 12

We study the problem of minimizing a strongly convex, smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is universally optimal in the sense that it achieves the optimal rate both in the deterministic and stochastic case and operates without knowledge of noise characteristics. The algorithm consists of stages that use a stochastic version of Nesterov's method with a specific restart and parameters selected to achieve the fastest reduction in the bias-variance terms in the convergence rate bounds.
Robust Temporal Difference Learning for Critical Domains
Authors: Richard Klima, Daan Bloembergen, Michael Kaisers, Karl Tuyls
Date: 2019-01-23 00:00:00

We present a new Q-function operator for temporal difference (TD) learning
methods that explicitly encodes robustness against significant rare events
(SRE) in critical domains. The operator, which we call the $\kappa$-operator,
allows to learn a robust policy in a model-based fashion without actually
observing the SRE. We introduce single- and multi-agent robust TD methods using
the operator $\kappa$. We prove convergence of the operator to the optimal
robust Q-function with respect to the model using the theory of Generalized
Markov Decision Processes. In addition we prove convergence to the optimal
Q-function of the original MDP given that the probability of SREs vanishes.
Empirical evaluations demonstrate the superior performance of $\kappa$-based TD
methods both in the early learning phase as well as in the final converged
stage. In addition we show robustness of the proposed method to small model
errors, as well as its applicability in a multi-agent context.
An information theoretic approach to the autoencoder
Authors: Vincenzo Crescimanna, Bruce Graham
Date: 2019-01-23 00:00:00
Tasks: Denoising

We present a variation of the Autoencoder (AE) that explicitly maximizes the
mutual information between the input data and the hidden representation. The
proposed model, the InfoMax Autoencoder (IMAE), by construction is able to
learn a robust representation and good prototypes of the data. IMAE is compared
both theoretically and then computationally with the state of the art models:
the Denoising and Contractive Autoencoders in the one-hidden layer setting and
the Variational Autoencoder in the multi-layer case. Computational experiments
are performed with the MNIST and Fashion-MNIST datasets and demonstrate
particularly the strong clusterization performance of IMAE.
Sentiment and Sarcasm Classification with Multitask Learning
Authors: Navonil Majumder, Soujanya Poria, Haiyun Peng, Niyati Chhaya, Erik Cambria, Alexander Gelbukh
Date: 2019-01-23 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Sarcasm Detection, Sentiment Analysis, Sentiment Classification

Sentiment classification and sarcasm detection are both important natural
language processing (NLP) tasks. Sentiment is always coupled with sarcasm where
intensive emotion is expressed. Nevertheless, most literature considers them as
two separate tasks. We argue that knowledge in sarcasm detection can also be
beneficial to sentiment classification and vice versa. We show that these two
tasks are correlated, and present a multi-task learning-based framework using a
deep neural network that models this correlation to improve the performance of
both tasks in a multi-task learning setting. Our method outperforms the state
of the art by 3-4% in the benchmark dataset.
Hierarchical Reinforcement Learning for Multi-agent MOBA Game
Authors: Zhijian Zhang, Haozheng Li, Luo Zhang, Tianyin Zheng, Ting Zhang, Xiong Hao, Xiaoxin Chen, Min Chen, Fangxu Xiao, Wei Zhou
Date: 2019-01-23 00:00:00
Tasks: Hierarchical Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Learning

Real Time Strategy (RTS) games require macro strategies as well as micro strategies to obtain satisfactory performance since it has large state space, action space, and hidden information. This paper presents a novel hierarchical reinforcement learning model for mastering Multiplayer Online Battle Arena (MOBA) games, a sub-genre of RTS games. The novelty of this work are: (1) proposing a hierarchical framework, where agents execute macro strategies by imitation learning and carry out micromanipulations through reinforcement learning, (2) developing a simple self-learning method to get better sample efficiency for training, and (3) designing a dense reward function for multi-agent cooperation in the absence of game engine or Application Programming Interface (API). Finally, various experiments have been performed to validate the superior performance of the proposed method over other state-of-the-art reinforcement learning algorithms. Agent successfully learns to combat and defeat bronze-level built-in AI with 100% win rate, and experiments show that our method can create a competitive multi-agent for a kind of mobile MOBA game {\it King of Glory} in 5v5 mode.
Removing Stripes, Scratches, and Curtaining with Non-Recoverable Compressed Sensing
Authors: Jonathan Schwartz, Yi Jiang, Yongjie Wang, Anthony Aiello, Pallab Bhattacharya, Hui Yuan, Zetian Mi, Nabil Bassim, Robert Hovden
Date: 2019-01-23 00:00:00
Tasks: compressed sensing

Highly-directional image artifacts such as ion mill curtaining, mechanical
scratches, or image striping from beam instability degrade the interpretability
of micrographs. These unwanted, aperiodic features extend the image along a
primary direction and occupy a small wedge of information in Fourier space.
Deleting this wedge of data replaces stripes, scratches, or curtaining, with
more complex streaking and blurring artifacts-known within the tomography
community as missing wedge artifacts. Here, we overcome this problem by
recovering the missing region using total variation minimization, which
leverages image sparsity based reconstruction techniques-colloquially referred
to as compressed sensing-to reliably restore images corrupted by stripe like
features. Our approach removes beam instability, ion mill curtaining,
mechanical scratches, or any stripe features and remains robust at low
signal-to-noise. The success of this approach is achieved by exploiting
compressed sensings inability to recover directional structures that are highly
localized and missing in Fourier Space.
Backprop with Approximate Activations for Memory-efficient Network Training
Authors: Ayan Chakrabarti, Benjamin Moseley
Date: 2019-01-23 00:00:00
Proceeding: ICLR 2019 5

Training convolutional neural network models is memory intensive since back-propagation requires storing activations of all intermediate layers. This presents a practical concern when seeking to deploy very deep architectures in production, especially when models need to be frequently re-trained on updated datasets. In this paper, we propose a new implementation for back-propagation that significantly reduces memory usage, by enabling the use of approximations with negligible computational cost and minimal effect on training performance. The algorithm reuses common buffers to temporarily store full activations and compute the forward pass exactly. It also stores approximate per-layer copies of activations, at significant memory savings, that are used in the backward pass. Compared to simply approximating activations within standard back-propagation, our method limits accumulation of errors across layers. This allows the use of much lower-precision approximations without affecting training accuracy. Experiments on CIFAR-10, CIFAR-100, and ImageNet show that our method yields performance close to exact training, while storing activations compactly with as low as 4-bit precision.
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks
Authors: Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir
Date: 2019-01-23 00:00:00

Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponential family of probability distributions. We integrate the recently developed Stein variational Newton (SVN) method (Detommaso et al., 2018) and BOCPD to offer a full online Bayesian treatment for a large number of situations with significant importance in practice. We apply the resulting method to two challenging and novel applications: Hawkes processes and long short-term memory (LSTM) neural networks. In both cases, we successfully demonstrate the efficacy of our method on real data.
PD-ML-Lite: Private Distributed Machine Learning from Lighweight Cryptography
Authors: Maksim Tsikhanovich, Malik Magdon-Ismail, Muhammad Ishaq, Vassilis Zikas
Date: 2019-01-23 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Privacy is a major issue in learning from distributed data. Recently the
cryptographic literature has provided several tools for this task. However,
these tools either reduce the quality/accuracy of the learning
algorithm---e.g., by adding noise---or they incur a high performance penalty
and/or involve trusting external authorities.
  We propose a methodology for {\sl private distributed machine learning from
light-weight cryptography} (in short, PD-ML-Lite). We apply our methodology to
two major ML algorithms, namely non-negative matrix factorization (NMF) and
singular value decomposition (SVD). Our resulting protocols are communication
optimal, achieve the same accuracy as their non-private counterparts, and
satisfy a notion of privacy---which we define---that is both intuitive and
measurable. Our approach is to use lightweight cryptographic protocols (secure
sum and normalized secure sum) to build learning algorithms rather than wrap
complex learning algorithms in a heavy-cost MPC framework.
  We showcase our algorithms' utility and privacy on several applications: for
NMF we consider topic modeling and recommender systems, and for SVD, principal
component regression, and low rank approximation.
Typed Graph Networks
Authors: Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos, Marco Gori, Luis Lamb
Date: 2019-01-23 00:00:00
Tasks: Graph Neural Network

Recently, the deep learning community has given growing attention to neural
architectures engineered to learn problems in relational domains. Convolutional
Neural Networks employ parameter sharing over the image domain, tying the
weights of neural connections on a grid topology and thus enforcing the
learning of a number of convolutional kernels. By instantiating trainable
neural modules and assembling them in varied configurations (apart from grids),
one can enforce parameter sharing over graphs, yielding models which can
effectively be fed with relational data. In this context, vertices in a graph
can be projected into a hyperdimensional real space and iteratively refined
over many message-passing iterations in an end-to-end differentiable
architecture. Architectures of this family have been referred to with several
definitions in the literature, such as Graph Neural Networks, Message-passing
Neural Networks, Relational Networks and Graph Networks. In this paper, we
revisit the original Graph Neural Network model and show that it generalises
many of the recent models, which in turn benefit from the insight of thinking
about vertex \textbf{types}. To illustrate the generality of the original
model, we present a Graph Neural Network formalisation, which partitions the
vertices of a graph into a number of types. Each type represents an entity in
the ontology of the problem one wants to learn. This allows - for instance -
one to assign embeddings to edges, hyperedges, and any number of global
attributes of the graph. As a companion to this paper we provide a
Python/Tensorflow library to facilitate the development of such architectures,
with which we instantiate the formalisation to reproduce a number of models
proposed in the current literature.