pwc-archive/papers-with-abstracts huggingface dataset:
Iterative Low-Rank Approximation for CNN Compression
Authors: Maksym Kholiavchenko
Date: 2018-03-23 00:00:00
Tasks: General Classification, object-detection, Object Detection

Deep convolutional neural networks contain tens of millions of parameters, making them impossible to work efficiently on embedded devices. We propose iterative approach of applying low-rank approximation to compress deep convolutional neural networks. Since classification and object detection are the most favored tasks for embedded devices, we demonstrate the effectiveness of our approach by compressing AlexNet, VGG-16, YOLOv2 and Tiny YOLO networks. Our results show the superiority of the proposed method compared to non-repetitive ones. We demonstrate higher compression ratio providing less accuracy loss.
Deep Learning Phase Segregation
Authors: Amir Barati Farimani, Joseph Gomes, Rishi Sharma, Franklin L. Lee, Vijay S. Pande
Date: 2018-03-23 00:00:00
Tasks: Deep Learning

Phase segregation, the process by which the components of a binary mixture
spontaneously separate, is a key process in the evolution and design of many
chemical, mechanical, and biological systems. In this work, we present a
data-driven approach for the learning, modeling, and prediction of phase
segregation. A direct mapping between an initially dispersed, immiscible binary
fluid and the equilibrium concentration field is learned by conditional
generative convolutional neural networks. Concentration field predictions by
the deep learning model conserve phase fraction, correctly predict phase
transition, and reproduce area, perimeter, and total free energy distributions
up to 98% accuracy.
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Authors: Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus
Date: 2018-02-26 00:00:00
Proceeding: ICML 2018 7
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the multi-agent reinforcement learning setting with imperfect
information in which each agent is trying to maximize its own utility. The
reward function depends on the hidden state (or goal) of both agents, so the
agents must infer the other players' hidden goals from their observed behavior
in order to solve the tasks. We propose a new approach for learning in these
domains: Self Other-Modeling (SOM), in which an agent uses its own policy to
predict the other agent's actions and update its belief of their hidden state
in an online manner. We evaluate this approach on three different tasks and
show that the agents are able to learn better policies using their estimate of
the other players' hidden states, in both cooperative and adversarial settings.
Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals
Authors: Filip L. Iliev, Valentin G. Stanev, Velimir V. Vesselinov, Boian S. Alexandrov
Date: 2016-12-12 00:00:00
Tasks: blind source separation

Factor analysis is broadly used as a powerful unsupervised machine learning
tool for reconstruction of hidden features in recorded mixtures of signals. In
the case of a linear approximation, the mixtures can be decomposed by a variety
of model-free Blind Source Separation (BSS) algorithms. Most of the available
BSS algorithms consider an instantaneous mixing of signals, while the case when
the mixtures are linear combinations of signals with delays is less explored.
Especially difficult is the case when the number of sources of the signals with
delays is unknown and has to be determined from the data as well. To address
this problem, in this paper, we present a new method based on Nonnegative
Matrix Factorization (NMF) that is capable of identifying: (a) the unknown
number of the sources, (b) the delays and speed of propagation of the signals,
and (c) the locations of the sources. Our method can be used to decompose
records of mixtures of signals with delays emitted by an unknown number of
sources in a nondispersive medium, based only on recorded data. This is the
case, for example, when electromagnetic signals from multiple antennas are
received asynchronously; or mixtures of acoustic or seismic signals recorded by
sensors located at different positions; or when a shift in frequency is induced
by the Doppler effect. By applying our method to synthetic datasets, we
demonstrate its ability to identify the unknown number of sources as well as
the waveforms, the delays, and the strengths of the signals. Using Bayesian
analysis, we also evaluate estimation uncertainties and identify the region of
likelihood where the positions of the sources can be found.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Authors: Bokai Cao, Lei Zheng, Chenwei Zhang, Philip S. Yu, Andrea Piscitello, John Zulueta, Olu Ajilore, Kelly Ryan, Alex D. Leow
Date: 2018-03-23 00:00:00

The increasing use of electronic forms of communication presents new
opportunities in the study of mental health, including the ability to
investigate the manifestations of psychiatric diseases unobtrusively and in the
setting of patients' daily lives. A pilot study to explore the possible
connections between bipolar affective disorder and mobile phone usage was
conducted. In this study, participants were provided a mobile phone to use as
their primary phone. This phone was loaded with a custom keyboard that
collected metadata consisting of keypress entry time and accelerometer
movement. Individual character data with the exceptions of the backspace key
and space bar were not collected due to privacy concerns. We propose an
end-to-end deep architecture based on late fusion, named DeepMood, to model the
multi-view metadata for the prediction of mood scores. Experimental results
show that 90.31% prediction accuracy on the depression score can be achieved
based on session-level mobile phone typing dynamics which is typically less
than one minute. It demonstrates the feasibility of using mobile phone metadata
to infer mood disturbance and severity.
Automated Evaluation of Out-of-Context Errors
Authors: Patrick Huber, Jan Niehues, Alex Waibel
Date: 2018-03-23 00:00:00
Proceeding: LREC 2018 5
Tasks: Binary Classification, Language Modeling, Language Modelling, Sentence, Translation

We present a new approach to evaluate computational models for the task of
text understanding by the means of out-of-context error detection. Through the
novel design of our automated modification process, existing large-scale data
sources can be adopted for a vast number of text understanding tasks. The data
is thereby altered on a semantic level, allowing models to be tested against a
challenging set of modified text passages that require to comprise a broader
narrative discourse. Our newly introduced task targets actual real-world
problems of transcription and translation systems by inserting authentic
out-of-context errors. The automated modification process is applied to the
2016 TEDTalk corpus. Entirely automating the process allows the adoption of
complete datasets at low cost, facilitating supervised learning procedures and
deeper networks to be trained and tested. To evaluate the quality of the
modification algorithm a language model and a supervised binary classification
model are trained and tested on the altered dataset. A human baseline
evaluation is examined to compare the results with human performance. The
outcome of the evaluation task indicates the difficulty to detect semantic
errors for machine-learning algorithms and humans, showing that the errors
cannot be identified when limited to a single sentence.
Multi-View Factorization Machines
Authors: Bokai Cao, Hucheng Zhou, Guoqiang Li, Philip S. Yu
Date: 2015-06-03 00:00:00
Tasks: parameter estimation

For a learning task, data can usually be collected from different sources or
be represented from multiple views. For example, laboratory results from
different medical examinations are available for disease diagnosis, and each of
them can only reflect the health state of a person from a particular
aspect/view. Therefore, different views provide complementary information for
learning tasks. An effective integration of the multi-view information is
expected to facilitate the learning performance. In this paper, we propose a
general predictor, named multi-view machines (MVMs), that can effectively
include all the possible interactions between features from multiple views. A
joint factorization is embedded for the full-order interaction parameters which
allows parameter estimation under sparsity. Moreover, MVMs can work in
conjunction with different loss functions for a variety of machine learning
tasks. A stochastic gradient descent method is presented to learn the MVM
model. We further illustrate the advantages of MVMs through comparison with
other methods for multi-view classification, including support vector machines
(SVMs), support tensor machines (STMs) and factorization machines (FMs).
Broad Learning for Healthcare
Authors: Bokai Cao
Date: 2018-03-23 00:00:00
Tasks: feature selection, Network Embedding

A broad spectrum of data from different modalities are generated in the
healthcare domain every day, including scalar data (e.g., clinical measures
collected at hospitals), tensor data (e.g., neuroimages analyzed by research
institutes), graph data (e.g., brain connectivity networks), and sequence data
(e.g., digital footprints recorded on smart sensors). Capability for modeling
information from these heterogeneous data sources is potentially transformative
for investigating disease mechanisms and for informing therapeutic
interventions.
  Our works in this thesis attempt to facilitate healthcare applications in the
setting of broad learning which focuses on fusing heterogeneous data sources
for a variety of synergistic knowledge discovery and machine learning tasks. We
are generally interested in computer-aided diagnosis, precision medicine, and
mobile health by creating accurate user profiles which include important
biomarkers, brain connectivity patterns, and latent representations. In
particular, our works involve four different data mining problems with
application to the healthcare domain: multi-view feature selection, subgraph
pattern mining, brain network embedding, and multi-view sequence prediction.
Computational Power and the Social Impact of Artificial Intelligence
Authors: Tim Hwang
Date: 2018-03-23 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is a computational process. To that end, it is inextricably
tied to computational power - the tangible material of chips and semiconductors
that the algorithms of machine intelligence operate on. Most obviously,
computational power and computing architectures shape the speed of training and
inference in machine learning, and therefore influence the rate of progress in
the technology. But, these relationships are more nuanced than that: hardware
shapes the methods used by researchers and engineers in the design and
development of machine learning models. Characteristics such as the power
consumption of chips also define where and how machine learning can be used in
the real world.
  Despite this, many analyses of the social impact of the current wave of
progress in AI have not substantively brought the dimension of hardware into
their accounts. While a common trope in both the popular press and scholarly
literature is to highlight the massive increase in computational power that has
enabled the recent breakthroughs in machine learning, the analysis frequently
goes no further than this observation around magnitude. This paper aims to dig
more deeply into the relationship between computational power and the
development of machine learning. Specifically, it examines how changes in
computing architectures, machine learning methodologies, and supply chains
might influence the future of AI. In doing so, it seeks to trace a set of
specific relationships between this underlying hardware layer and the broader
social impacts and risks around AI.
Counterexamples for Robotic Planning Explained in Structured Language
Authors: Lu Feng, Mahsa Ghasemi, Kai-Wei Chang, Ufuk Topcu
Date: 2018-03-23 00:00:00

Automated techniques such as model checking have been used to verify models
of robotic mission plans based on Markov decision processes (MDPs) and generate
counterexamples that may help diagnose requirement violations. However, such
artifacts may be too complex for humans to understand, because existing
representations of counterexamples typically include a large number of paths or
a complex automaton. To help improve the interpretability of counterexamples,
we define a notion of explainable counterexample, which includes a set of
structured natural language sentences to describe the robotic behavior that
lead to a requirement violation in an MDP model of robotic mission plan. We
propose an approach based on mixed-integer linear programming for generating
explainable counterexamples that are minimal, sound and complete. We
demonstrate the usefulness of the proposed approach via a case study of
warehouse robots planning.
Generalized Byzantine-tolerant SGD
Authors: Cong Xie, Oluwasanmi Koyejo, Indranil Gupta
Date: 2018-02-27 00:00:00

We propose three new robust aggregation rules for distributed synchronous
Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The
attackers can arbitrarily manipulate the data transferred between the servers
and the workers in the parameter server~(PS) architecture. We prove the
Byzantine resilience properties of these aggregation rules. Empirical analysis
shows that the proposed techniques outperform current approaches for realistic
use cases and Byzantine attack scenarios.
Online Learning of a Memory for Learning Rates
Authors: Franziska Meier, Daniel Kappler, Stefan Schaal
Date: 2017-09-20 00:00:00
Tasks: Meta-Learning

The promise of learning to learn for robotics rests on the hope that by
extracting some information about the learning process itself we can speed up
subsequent similar learning tasks. Here, we introduce a computationally
efficient online meta-learning algorithm that builds and optimizes a memory
model of the optimal learning rate landscape from previously observed gradient
behaviors. While performing task specific optimization, this memory of learning
rates predicts how to scale currently observed gradients. After applying the
gradient scaling our meta-learner updates its internal memory based on the
observed effect its prediction had. Our meta-learner can be combined with any
gradient-based optimizer, learns on the fly and can be transferred to new
optimization tasks. In our evaluations we show that our meta-learning algorithm
speeds up learning of MNIST classification and a variety of learning control
tasks, either in batch or online learning settings.
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
Authors: Nathaniel Chodosh, Chaoyang Wang, Simon Lucey
Date: 2018-03-23 00:00:00
Tasks: compressed sensing, Depth Completion

In this paper we consider the problem of estimating a dense depth map from a
set of sparse LiDAR points. We use techniques from compressed sensing and the
recently developed Alternating Direction Neural Networks (ADNNs) to create a
deep recurrent auto-encoder for this task. Our architecture internally performs
an algorithm for extracting multi-level convolutional sparse codes from the
input which are then used to make a prediction. Our results demonstrate that
with only two layers and 1800 parameters we are able to out perform all
previously published results, including deep networks with orders of magnitude
more parameters.
Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images
Authors: Wei Zhu, Zuoqiang Shi, Stanley Osher
Date: 2016-05-18 00:00:00

We present a scalable low dimensional manifold model for the reconstruction
of noisy and incomplete hyperspectral images. The model is based on the
observation that the spatial-spectral blocks of a hyperspectral image typically
lie close to a collection of low dimensional manifolds. To emphasize this, the
dimension of the manifold is directly used as a regularizer in a variational
functional, which is solved efficiently by alternating direction of
minimization and weighted nonlocal Laplacian. Unlike general 3D images, the
same similarity matrix can be shared across all spectral bands for a
hyperspectral image, therefore the resulting algorithm is much more scalable
than that for general 3D data. Numerical experiments on the reconstruction of
hyperspectral images from sparse and noisy sampling demonstrate the superiority
of our proposed algorithm in terms of both speed and accuracy.
Top-Down Saliency Detection Driven by Visual Classification
Authors: Francesca Murabito, Concetto Spampinato, Simone Palazzo, Konstantin Pogorelov, Michael Riegler
Date: 2017-09-15 00:00:00
Tasks: Classification, General Classification, Saliency Detection

This paper presents an approach for top-down saliency detection guided by
visual classification tasks. We first learn how to compute visual saliency when
a specific visual task has to be accomplished, as opposed to most
state-of-the-art methods which assess saliency merely through bottom-up
principles. Afterwards, we investigate if and to what extent visual saliency
can support visual classification in nontrivial cases. To achieve this, we
propose SalClassNet, a CNN framework consisting of two networks jointly
trained: a) the first one computing top-down saliency maps from input images,
and b) the second one exploiting the computed saliency maps for visual
classification. To test our approach, we collected a dataset of eye-gaze maps,
using a Tobii T60 eye tracker, by asking several subjects to look at images
from the Stanford Dogs dataset, with the objective of distinguishing dog
breeds. Performance analysis on our dataset and other saliency bench-marking
datasets, such as POET, showed that SalClassNet out-performs state-of-the-art
saliency detectors, such as SalNet and SALICON. Finally, we analyzed the
performance of SalClassNet in a fine-grained recognition task and found out
that it generalizes better than existing visual classifiers. The achieved
results, thus, demonstrate that 1) conditioning saliency detectors with object
classes reaches state-of-the-art performance, and 2) providing explicitly
top-down saliency maps to visual classifiers enhances classification accuracy.
Chatter Classification in Turning Using Machine Learning and Topological Data Analysis
Authors: Firas A. Khasawneh, Elizabeth Munch, Jose A. Perea
Date: 2018-03-23 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Time Series Analysis, Topological Data Analysis

Chatter identification and detection in machining processes has been an
active area of research in the past two decades. Part of the challenge in
studying chatter is that machining equations that describe its occurrence are
often nonlinear delay differential equations. The majority of the available
tools for chatter identification rely on defining a metric that captures the
characteristics of chatter, and a threshold that signals its occurrence. The
difficulty in choosing these parameters can be somewhat alleviated by utilizing
machine learning techniques. However, even with a successful classification
algorithm, the transferability of typical machine learning methods from one
data set to another remains very limited. In this paper we combine supervised
machine learning with Topological Data Analysis (TDA) to obtain a descriptor of
the process which can detect chatter. The features we use are derived from the
persistence diagram of an attractor reconstructed from the time series via
Takens embedding. We test the approach using deterministic and stochastic
turning models, where the stochasticity is introduced via the cutting
coefficient term. Our results show a 97% successful classification rate on the
deterministic model labeled by the stability diagram obtained using the
spectral element method. The features gleaned from the deterministic model are
then utilized for characterization of chatter in a stochastic turning model
where there are very limited analysis methods.
Online Detection of Action Start in Untrimmed, Streaming Videos
Authors: Zheng Shou, Junting Pan, Jonathan Chan, Kazuyuki Miyazawa, Hassan Mansour, Anthony Vetro, Xavier Giro-i-Nieto, Shih-Fu Chang
Date: 2018-02-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Detection, Generative Adversarial Network

We aim to tackle a novel task in action detection - Online Detection of
Action Start (ODAS) in untrimmed, streaming videos. The goal of ODAS is to
detect the start of an action instance, with high categorization accuracy and
low detection latency. ODAS is important in many applications such as early
alert generation to allow timely security or emergency response. We propose
three novel methods to specifically address the challenges in training ODAS
models: (1) hard negative samples generation based on Generative Adversarial
Network (GAN) to distinguish ambiguous background, (2) explicitly modeling the
temporal consistency between data around action start and data succeeding
action start, and (3) adaptive sampling strategy to handle the scarcity of
training data. We conduct extensive experiments using THUMOS'14 and
ActivityNet. We show that our proposed methods lead to significant performance
gains and improve the state-of-the-art methods. An ablation study confirms the
effectiveness of each proposed method.
Byzantine Stochastic Gradient Descent
Authors: Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
Date: 2018-03-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Stochastic Optimization

This paper studies the problem of distributed stochastic optimization in an
adversarial setting where, out of the $m$ machines which allegedly compute
stochastic gradients every iteration, an $\alpha$-fraction are Byzantine, and
can behave arbitrarily and adversarially. Our main result is a variant of
stochastic gradient descent (SGD) which finds $\varepsilon$-approximate
minimizers of convex functions in $T = \tilde{O}\big( \frac{1}{\varepsilon^2 m}
+ \frac{\alpha^2}{\varepsilon^2} \big)$ iterations. In contrast, traditional
mini-batch SGD needs $T = O\big( \frac{1}{\varepsilon^2 m} \big)$ iterations,
but cannot tolerate Byzantine failures. Further, we provide a lower bound
showing that, up to logarithmic factors, our algorithm is
information-theoretically optimal both in terms of sampling complexity and time
complexity.
Stance Detection on Tweets: An SVM-based Approach
Authors: Dilek Küçük, Fazli Can
Date: 2018-03-23 00:00:00
Tasks: Sentiment Analysis, Stance Detection

Stance detection is a subproblem of sentiment analysis where the stance of
the author of a piece of natural language text for a particular target (either
explicitly stated in the text or not) is explored. The stance output is usually
given as Favor, Against, or Neither. In this paper, we target at stance
detection on sports-related tweets and present the performance results of our
SVM-based stance classifiers on such tweets. First, we describe three versions
of our proprietary tweet data set annotated with stance information, all of
which are made publicly available for research purposes. Next, we evaluate SVM
classifiers using different feature sets for stance detection on this data set.
The employed features are based on unigrams, bigrams, hashtags, external links,
emoticons, and lastly, named entities. The results indicate that joint use of
the features based on unigrams, hashtags, and named entities by SVM classifiers
is a plausible approach for stance detection problem on sports-related tweets.