pwc-archive/papers-with-abstracts huggingface dataset:
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Authors: Tommaso Dreossi, Alexandre Donzé, Sanjit A. Seshia
Date: 2017-03-02 00:00:00
Tasks: BIG-bench Machine Learning

Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks.
EMFET: E-mail Features Extraction Tool
Authors: Wadi' Hijawi, Hossam Faris, Ja'far Alqatawna, Ibrahim Aljarah, Ala' M. Al-Zoubi, Maria Habib
Date: 2017-11-22 00:00:00
Tasks: BIG-bench Machine Learning, Spam detection

EMFET is an open source and flexible tool that can be used to extract a large
number of features from any email corpus with emails saved in EML format. The
extracted features can be categorized into three main groups: header features,
payload (body) features, and attachment features. The purpose of the tool is to
help practitioners and researchers to build datasets that can be used for
training machine learning models for spam detection. So far, 140 features can
be extracted using EMFET. EMFET is extensible and easy to use. The source code
of EMFET is publicly available at GitHub
(https://github.com/WadeaHijjawi/EmailFeaturesExtraction)
Ensemble Sampling
Authors: Xiuyuan Lu, Benjamin Van Roy
Date: 2017-05-20 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Thompson Sampling

Thompson sampling has emerged as an effective heuristic for a broad range of online decision problems. In its basic form, the algorithm requires computing and sampling from a posterior distribution over models, which is tractable only for simple special cases. This paper develops ensemble sampling, which aims to approximate Thompson sampling while maintaining tractability even in the face of complex models such as neural networks. Ensemble sampling dramatically expands on the range of applications for which Thompson sampling is viable. We establish a theoretical basis that supports the approach and present computational results that offer further insight.
Deep Q-learning from Demonstrations
Authors: Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, Joel Z. Leibo, Audrunas Gruslys
Date: 2017-04-12 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Imitation Learning, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) has achieved several high profile successes
in difficult decision-making problems. However, these algorithms typically
require a huge amount of data before they reach reasonable performance. In
fact, their performance during learning can be extremely poor. This may be
acceptable for a simulator, but it severely limits the applicability of deep RL
to many real-world tasks, where the agent must learn in the real environment.
In this paper we study a setting where the agent may access data from previous
control of the system. We present an algorithm, Deep Q-learning from
Demonstrations (DQfD), that leverages small sets of demonstration data to
massively accelerate the learning process even from relatively small amounts of
demonstration data and is able to automatically assess the necessary ratio of
demonstration data while learning thanks to a prioritized replay mechanism.
DQfD works by combining temporal difference updates with supervised
classification of the demonstrator's actions. We show that DQfD has better
initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN)
as it starts with better scores on the first million steps on 41 of 42 games
and on average it takes PDD DQN 83 million steps to catch up to DQfD's
performance. DQfD learns to out-perform the best demonstration given in 14 of
42 games. In addition, DQfD leverages human demonstrations to achieve
state-of-the-art results for 11 games. Finally, we show that DQfD performs
better than three related algorithms for incorporating demonstration data into
DQN.
W-Net: A Deep Model for Fully Unsupervised Image Segmentation
Authors: Xide Xia, Brian Kulis
Date: 2017-11-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

While significant attention has been recently focused on designing supervised
deep semantic segmentation algorithms for vision tasks, there are many domains
in which sufficient supervised pixel-level labels are difficult to obtain. In
this paper, we revisit the problem of purely unsupervised image segmentation
and propose a novel deep architecture for this problem. We borrow recent ideas
from supervised semantic segmentation methods, in particular by concatenating
two fully convolutional networks together into an autoencoder--one for encoding
and one for decoding. The encoding layer produces a k-way pixelwise prediction,
and both the reconstruction error of the autoencoder as well as the normalized
cut produced by the encoder are jointly minimized during training. When
combined with suitable postprocessing involving conditional random field
smoothing and hierarchical segmentation, our resulting algorithm achieves
impressive results on the benchmark Berkeley Segmentation Data Set,
outperforming a number of competing methods.
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition
Authors: Jingxuan Hou, Tae Soo Kim, Austin Reiter
Date: 2017-11-22 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Decoder, Fine-grained Action Recognition, Temporal Action Localization, Time Series, Time Series Analysis

Despite the growing discriminative capabilities of modern deep learning
methods for recognition tasks, the inner workings of the state-of-art models
still remain mostly black-boxes. In this paper, we propose a systematic
interpretation of model parameters and hidden representations of Residual
Temporal Convolutional Networks (Res-TCN) for action recognition in time-series
data. We also propose a Feature Map Decoder as part of the interpretation
analysis, which outputs a representation of model's hidden variables in the
same domain as the input. Such analysis empowers us to expose model's
characteristic learning patterns in an interpretable way. For example, through
the diagnosis analysis, we discovered that our model has learned to achieve
view-point invariance by implicitly learning to perform rotational
normalization of the input to a more discriminative view. Based on the findings
from the model interpretation analysis, we propose a targeted refinement
technique, which can generalize to various other recognition models. The
proposed work introduces a three-stage paradigm for model learning: training,
interpretable diagnosis and targeted refinement. We validate our approach on
skeleton based 3D human action recognition benchmark of NTU RGB+D. We show that
the proposed workflow is an effective model learning strategy and the resulting
Multi-stream Residual Temporal Convolutional Network (MS-Res-TCN) achieves the
state-of-the-art performance on NTU RGB+D.
Temporal Relational Reasoning in Videos
Authors: Bolei Zhou, Alex Andonian, Aude Oliva, Antonio Torralba
Date: 2017-11-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Activity Recognition, Common Sense Reasoning, Human-Object Interaction Detection, Relational Reasoning, Relation Network

Temporal relational reasoning, the ability to link meaningful transformations
of objects or entities over time, is a fundamental property of intelligent
species. In this paper, we introduce an effective and interpretable network
module, the Temporal Relation Network (TRN), designed to learn and reason about
temporal dependencies between video frames at multiple time scales. We evaluate
TRN-equipped networks on activity recognition tasks using three recent video
datasets - Something-Something, Jester, and Charades - which fundamentally
depend on temporal relational reasoning. Our results demonstrate that the
proposed TRN gives convolutional neural networks a remarkable capacity to
discover temporal relations in videos. Through only sparsely sampled video
frames, TRN-equipped networks can accurately predict human-object interactions
in the Something-Something dataset and identify various human gestures on the
Jester dataset with very competitive performance. TRN-equipped networks also
outperform two-stream networks and 3D convolution networks in recognizing daily
activities in the Charades dataset. Further analyses show that the models learn
intuitive and interpretable visual common sense knowledge in videos.
Customized Nonlinear Bandits for Online Response Selection in Neural Conversation Models
Authors: Bing Liu, Tong Yu, Ian Lane, Ole J. Mengshoel
Date: 2017-11-22 00:00:00
Tasks: Multi-Armed Bandits, Response Generation, Retrieval, Thompson Sampling

Dialog response selection is an important step towards natural response
generation in conversational agents. Existing work on neural conversational
models mainly focuses on offline supervised learning using a large set of
context-response pairs. In this paper, we focus on online learning of response
selection in retrieval-based dialog systems. We propose a contextual
multi-armed bandit model with a nonlinear reward function that uses distributed
representation of text for online response selection. A bidirectional LSTM is
used to produce the distributed representations of dialog context and
responses, which serve as the input to a contextual bandit. In learning the
bandit, we propose a customized Thompson sampling method that is applied to a
polynomial feature space in approximating the reward. Experimental results on
the Ubuntu Dialogue Corpus demonstrate significant performance gains of the
proposed method over conventional linear contextual bandits. Moreover, we
report encouraging response selection performance of the proposed neural bandit
model using the Recall@k metric for a small set of online training samples.
Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition
Authors: Shizhong Han, Zibo Meng, Zhiyuan Li, James O'Reilly, Jie Cai, Xiao-Feng Wang, Yan Tong
Date: 2017-07-26 00:00:00
Proceeding: CVPR 2018 6
Tasks: Facial Action Unit Detection

Recognizing facial action units (AUs) during spontaneous facial displays is a
challenging problem. Most recently, Convolutional Neural Networks (CNNs) have
shown promise for facial AU recognition, where predefined and fixed convolution
filter sizes are employed. In order to achieve the best performance, the
optimal filter size is often empirically found by conducting extensive
experimental validation. Such a training process suffers from expensive
training cost, especially as the network becomes deeper.
  This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the
filter sizes and weights of all convolutional layers are learned simultaneously
from the training data along with learning convolution filters. Specifically,
the filter size is defined as a continuous variable, which is optimized by
minimizing the training loss. Experimental results on two AU-coded spontaneous
databases have shown that the proposed OFS-CNN is capable of estimating optimal
filter size for varying image resolution and outperforms traditional CNNs with
the best filter size obtained by exhaustive search. The OFS-CNN also beats the
CNN using multiple filter sizes and more importantly, is much more efficient
during testing with the proposed forward-backward propagation algorithm.
MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples
Authors: Nicholas Carlini, David Wagner
Date: 2017-11-22 00:00:00

MagNet and "Efficient Defenses..." were recently proposed as a defense to
adversarial examples. We find that we can construct adversarial examples that
defeat these defenses with only a slight increase in distortion.
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Authors: Ethan R. Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi
Date: 2017-03-08 00:00:00
Proceeding: NeurIPS 2017 12

In many machine learning applications, it is important to explain the
predictions of a black-box classifier. For example, why does a deep neural
network assign an image to a particular class? We cast interpretability of
black-box classifiers as a combinatorial maximization problem and propose an
efficient streaming algorithm to solve it subject to cardinality constraints.
By extending ideas from Badanidiyuru et al. [2014], we provide a constant
factor approximation guarantee for our algorithm in the case of random stream
order and a weakly submodular objective function. This is the first such
theoretical guarantee for this general class of functions, and we also show
that no such algorithm exists for a worst case stream order. Our algorithm
obtains similar explanations of Inception V3 predictions $10$ times faster than
the state-of-the-art LIME framework of Ribeiro et al. [2016].
Causal nearest neighbor rules for optimal treatment regimes
Authors: Xin Zhou, Michael R. Kosorok
Date: 2017-11-22 00:00:00
Tasks: Causal Inference, Variable Selection

The estimation of optimal treatment regimes is of considerable interest to
precision medicine. In this work, we propose a causal $k$-nearest neighbor
method to estimate the optimal treatment regime. The method roots in the
framework of causal inference, and estimates the causal treatment effects
within the nearest neighborhood. Although the method is simple, it possesses
nice theoretical properties. We show that the causal $k$-nearest neighbor
regime is universally consistent. That is, the causal $k$-nearest neighbor
regime will eventually learn the optimal treatment regime as the sample size
increases. We also establish its convergence rate. However, the causal
$k$-nearest neighbor regime may suffer from the curse of dimensionality, i.e.
performance deteriorates as dimensionality increases. To alleviate this
problem, we develop an adaptive causal $k$-nearest neighbor method to perform
metric selection and variable selection simultaneously. The performance of the
proposed methods is illustrated in simulation studies and in an analysis of a
chronic depression clinical trial.
From Monte Carlo to Las Vegas: Improving Restricted Boltzmann Machine Training Through Stopping Sets
Authors: Pedro H. P. Savarese, Mayank Kakodkar, Bruno Ribeiro
Date: 2017-11-22 00:00:00

We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC)
estimators of Restricted Boltzmann Machines (RBMs). We denote our approach
Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange
for random running times. MCLV uses a stopping set built from the training data
and has maximum number of Markov chain steps K (referred as MCLV-K). We present
a MCLV-K gradient estimator (LVS-K) for RBMs and explore the correspondence and
differences between LVS-K and Contrastive Divergence (CD-K), with LVS-K
significantly outperforming CD-K training RBMs over the MNIST dataset,
indicating MCLV to be a promising direction in learning generative models.
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
Authors: Kaiyu Zheng, Andrzej Pronobis, Rajesh P. N. Rao
Date: 2017-09-24 00:00:00
Tasks: Structured Prediction

We introduce Graph-Structured Sum-Product Networks (GraphSPNs), a
probabilistic approach to structured prediction for problems where dependencies
between latent variables are expressed in terms of arbitrary, dynamic graphs.
While many approaches to structured prediction place strict constraints on the
interactions between inferred variables, many real-world problems can be only
characterized using complex graph structures of varying size, often
contaminated with noise when obtained from real data. Here, we focus on one
such problem in the domain of robotics. We demonstrate how GraphSPNs can be
used to bolster inference about semantic, conceptual place descriptions using
noisy topological relations discovered by a robot exploring large-scale office
spaces. Through experiments, we show that GraphSPNs consistently outperform the
traditional approach based on undirected graphical models, successfully
disambiguating information in global semantic maps built from uncertain, noisy
local evidence. We further exploit the probabilistic nature of the model to
infer marginal distributions over semantic descriptions of as yet unexplored
places and detect spatial environment configurations that are novel and
incongruent with the known evidence.
Leverage Score Sampling for Faster Accelerated Regression and ERM
Authors: Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, Praneeth Netrapalli, Aaron Sidford
Date: 2017-11-22 00:00:00
Tasks: regression

Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b
\in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution
to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x
- b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot
s\cdot\log\epsilon^{-1}) $ where
$\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$
and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$. Our
algorithm improves upon the previous best running time of $ \tilde{O}
((n+\sqrt{n \cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1})$.
  We achieve our result through a careful combination of leverage score
sampling techniques, proximal point methods, and accelerated coordinate
descent. Our method not only matches the performance of previous methods, but
further improves whenever leverage scores of rows are small (up to
polylogarithmic factors). We also provide a non-linear generalization of these
results that improves the running time for solving a broader class of ERM
problems.
Exploring the Bounds of the Utility of Context for Object Detection
Authors: Ehud Barnea, Ohad Ben-Shahar
Date: 2017-11-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection

The recurring context in which objects appear holds valuable information that
can be employed to predict their existence. This intuitive observation indeed
led many researchers to endow appearance-based detectors with explicit
reasoning about context. The underlying thesis suggests that stronger
contextual relations would facilitate greater improvements in detection
capacity. In practice, however, the observed improvement in many cases is
modest at best, and often only marginal. In this work we seek to improve our
understanding of this phenomenon, in part by pursuing an opposite approach.
Instead of attempting to improve detection scores by employing context, we
treat the utility of context as an optimization problem: to what extent can
detection scores be improved by considering context or any other kind of
additional information? With this approach we explore the bounds on improvement
by using contextual relations between objects and provide a tool for
identifying the most helpful ones. We show that simple co-occurrence relations
can often provide large gains, while in other cases a significant improvement
is simply impossible or impractical with either co-occurrence or more precise
spatial relations. To better understand these results we then analyze the
ability of context to handle different types of false detections, revealing
that tested contextual information cannot ameliorate localization errors,
severely limiting its gains. These and additional insights further our
understanding on where and why utilization of context for object detection
succeeds and fails.
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes
Authors: Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou
Date: 2017-11-22 00:00:00
Tasks: Word Embeddings

Word embeddings use vectors to represent words such that the geometry between
vectors captures semantic relationship between the words. In this paper, we
develop a framework to demonstrate how the temporal dynamics of the embedding
can be leveraged to quantify changes in stereotypes and attitudes toward women
and ethnic minorities in the 20th and 21st centuries in the United States. We
integrate word embeddings trained on 100 years of text data with the U.S.
Census to show that changes in the embedding track closely with demographic and
occupation shifts over time. The embedding captures global social shifts --
e.g., the women's movement in the 1960s and Asian immigration into the U.S --
and also illuminates how specific adjectives and occupations became more
closely associated with certain populations over time. Our framework for
temporal analysis of word embedding opens up a powerful new intersection
between machine learning and quantitative social science.
Rotation Adaptive Visual Object Tracking with Motion Consistency
Authors: Litu Rout, Sidhartha, Gorthi R. K. S. S. Manyam, Deepak Mishra
Date: 2017-09-18 00:00:00
Tasks: feature selection, Object, Object Tracking, Visual Object Tracking

Visual Object tracking research has undergone significant improvement in the
past few years. The emergence of tracking by detection approach in tracking
paradigm has been quite successful in many ways. Recently, deep convolutional
neural networks have been extensively used in most successful trackers. Yet,
the standard approach has been based on correlation or feature selection with
minimal consideration given to motion consistency. Thus, there is still a need
to capture various physical constraints through motion consistency which will
improve accuracy, robustness and more importantly rotation adaptiveness.
Therefore, one of the major aspects of this paper is to investigate the outcome
of rotation adaptiveness in visual object tracking. Among other key
contributions, the paper also includes various consistencies that turn out to
be extremely effective in numerous challenging sequences than the current
state-of-the-art.
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
Authors: Alex Tank, Emily B. Fox, Ali Shojaie
Date: 2017-11-22 00:00:00
Tasks: Time Series, Time Series Analysis

We present an efficient alternating direction method of multipliers (ADMM)
algorithm for segmenting a multivariate non-stationary time series with
structural breaks into stationary regions. We draw from recent work where the
series is assumed to follow a vector autoregressive model within segments and a
convex estimation procedure may be formulated using group fused lasso
penalties. Our ADMM approach first splits the convex problem into a global
quadratic program and a simple group lasso proximal update. We show that the
global problem may be parallelized over rows of the time dependent transition
matrices and furthermore that each subproblem may be rewritten in a form
identical to the log-likelihood of a Gaussian state space model. Consequently,
we develop a Kalman smoothing algorithm to solve the global update in time
linear in the length of the series.