pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization
Authors: Zhe Wang, Yi Zhou, Yingbin Liang, Guanghui Lan
Date: 2018-02-20 00:00:00

Cubic regularization (CR) is an optimization method with emerging popularity
due to its capability to escape saddle points and converge to second-order
stationary solutions for nonconvex optimization. However, CR encounters a high
sample complexity issue for finite-sum problems with a large data size.
%Various inexact variants of CR have been proposed to improve the sample
complexity. In this paper, we propose a stochastic variance-reduced
cubic-regularization (SVRC) method under random sampling, and study its
convergence guarantee as well as sample complexity. We show that the iteration
complexity of SVRC for achieving a second-order stationary solution within
$\epsilon$ accuracy is $O(\epsilon^{-3/2})$, which matches the state-of-art
result on CR types of methods. Moreover, our proposed variance reduction scheme
significantly reduces the per-iteration sample complexity. The resulting total
Hessian sample complexity of our SVRC is ${\Oc}(N^{2/3} \epsilon^{-3/2})$,
which outperforms the state-of-art result by a factor of $O(N^{2/15})$. We also
study our SVRC under random sampling without replacement scheme, which yields a
lower per-iteration sample complexity, and hence justifies its practical
applicability.
Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural Network
Authors: Siming Yan, Feng Shi, Yu-Hua Chen, Damini Dey, Sang-Eun Lee, Hyuk-Jae Chang, Debiao Li, Yibin Xie
Date: 2018-02-20 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Specificity

Coronary calcium causes beam hardening and blooming artifacts on cardiac
computed tomography angiography (CTA) images, which lead to overestimation of
lumen stenosis and reduction of diagnostic specificity. To properly remove
coronary calcification and restore arterial lumen precisely, we propose a
machine learning-based method with a multi-step inpainting process. We
developed a new network configuration, Dense-Unet, to achieve optimal
performance with low computational cost. Results after the calcium removal
process were validated by comparing with gold-standard X-ray angiography. Our
results demonstrated that removing coronary calcification from images with the
proposed approach was feasible, and may potentially improve the diagnostic
accuracy of CTA.
SufiSent - Universal Sentence Representations Using Suffix Encodings
Authors: Siddhartha Brahma
Date: 2018-02-20 00:00:00
Tasks: Natural Language Inference, Sentence

Computing universal distributed representations of sentences is a fundamental
task in natural language processing. We propose a method to learn such
representations by encoding the suffixes of word sequences in a sentence and
training on the Stanford Natural Language Inference (SNLI) dataset. We
demonstrate the effectiveness of our approach by evaluating it on the SentEval
benchmark, improving on existing approaches on several transfer tasks.
Dynamic Reconfiguration of Mission Parameters in Underwater Human-Robot Collaboration
Authors: Md Jahidul Islam, Marc Ho, Junaed Sattar
Date: 2017-09-26 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

This paper presents a real-time programming and parameter reconfiguration
method for autonomous underwater robots in human-robot collaborative tasks.
Using a set of intuitive and meaningful hand gestures, we develop a
syntactically simple framework that is computationally more efficient than a
complex, grammar-based approach. In the proposed framework, a convolutional
neural network is trained to provide accurate hand gesture recognition;
subsequently, a finite-state machine-based deterministic model performs
efficient gesture-to-instruction mapping, and further improves robustness of
the interaction scheme. The key aspect of this framework is that it can be
easily adopted by divers for communicating simple instructions to underwater
robots without using artificial tags such as fiducial markers, or requiring
them to memorize a potentially complex set of language rules. Extensive
experiments are performed both on field-trial data and through simulation,
which demonstrate the robustness, efficiency, and portability of this framework
in a number of different scenarios. Finally, a user interaction study is
presented that illustrates the gain in usability of our proposed interaction
framework compared to the existing methods for underwater domains.
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
Authors: Qiuyi Wu, Ernest Fokoue, Dhireesha Kudithipudi
Date: 2018-02-20 00:00:00

Echo state networks are powerful recurrent neural networks. However, they are
often unstable and shaky, making the process of finding an good ESN for a
specific dataset quite hard. Obtaining a superb accuracy by using the Echo
State Network is a challenging task. We create, develop and implement a family
of predictably optimal robust and stable ensemble of Echo State Networks via
regularizing the training and perturbing the input. Furthermore, several
distributions of weights have been tried based on the shape to see if the shape
of the distribution has the impact for reducing the error. We found ESN can
track in short term for most dataset, but it collapses in the long run.
Short-term tracking with large size reservoir enables ESN to perform strikingly
with superior prediction. Based on this scenario, we go a further step to
aggregate many of ESNs into an ensemble to lower the variance and stabilize the
system by stochastic replications and bootstrapping of input data.
Calculating the similarity between words and sentences using a lexical database and corpus statistics
Authors: Atish Pawar, Vijay Mago
Date: 2018-02-15 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity, Word Similarity

Calculating the semantic similarity between sentences is a long dealt problem
in the area of natural language processing. The semantic analysis field has a
crucial role to play in the research related to the text analytics. The
semantic similarity differs as the domain of operation differs. In this paper,
we present a methodology which deals with this issue by incorporating semantic
similarity and corpus statistics. To calculate the semantic similarity between
words and sentences, the proposed method follows an edge-based approach using a
lexical database. The methodology can be applied in a variety of domains. The
methodology has been tested on both benchmark standards and mean human
similarity dataset. When tested on these two datasets, it gives highest
correlation value for both word and sentence similarity outperforming other
similar models. For word similarity, we obtained Pearson correlation
coefficient of 0.8753 and for sentence similarity, the correlation obtained is
0.8794.
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
Authors: William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian Goodfellow
Date: 2017-10-23 00:00:00
Proceeding: ICLR 2018 1

Generative adversarial networks (GANs) are a family of generative models that
do not minimize a single training criterion. Unlike other generative models,
the data distribution is learned via a game between a generator (the generative
model) and a discriminator (a teacher providing training signal) that each
minimize their own cost. GANs are designed to reach a Nash equilibrium at which
each player cannot reduce their cost without changing the other players'
parameters. One useful approach for the theory of GANs is to show that a
divergence between the training distribution and the model distribution obtains
its minimum value at equilibrium. Several recent research directions have been
motivated by the idea that this divergence is the primary guide for the
learning process and that every step of learning should decrease the
divergence. We show that this view is overly restrictive. During GAN training,
the discriminator provides learning signal in situations where the gradients of
the divergences between distributions would not be useful. We provide empirical
counterexamples to the view of GAN training as divergence minimization.
Specifically, we demonstrate that GANs are able to learn distributions in
situations where the divergence minimization point of view predicts they would
fail. We also show that gradient penalties motivated from the divergence
minimization perspective are equally helpful when applied in other contexts in
which the divergence minimization perspective does not predict they would be
helpful. This contributes to a growing body of evidence that GAN training may
be more usefully viewed as approaching Nash equilibria via trajectories that do
not necessarily minimize a specific divergence at each step.
Devon: Deformable Volume Network for Learning Optical Flow
Authors: Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr
Date: 2018-02-20 00:00:00
Tasks: Optical Flow Estimation

State-of-the-art neural network models estimate large displacement optical
flow in multi-resolution and use warping to propagate the estimation between
two resolutions. Despite their impressive results, it is known that there are
two problems with the approach. First, the multi-resolution estimation of
optical flow fails in situations where small objects move fast. Second, warping
creates artifacts when occlusion or dis-occlusion happens. In this paper, we
propose a new neural network module, Deformable Cost Volume, which alleviates
the two problems. Based on this module, we designed the Deformable Volume
Network (Devon) which can estimate multi-scale optical flow in a single high
resolution. Experiments show Devon is more suitable in handling small objects
moving fast and achieves comparable results to the state-of-the-art methods in
public benchmarks.
Chipmunk: A Systolically Scalable 0.9 mm${}^2$, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference
Authors: Francesco Conti, Lukas Cavigelli, Gianna Paulin, Igor Susmelj, Luca Benini
Date: 2017-11-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Recurrent neural networks (RNNs) are state-of-the-art in voice
awareness/understanding and speech recognition. On-device computation of RNNs
on low-power mobile and wearable devices would be key to applications such as
zero-latency voice-based human-machine interfaces. Here we present Chipmunk, a
small (<1 mm${}^2$) hardware accelerator for Long-Short Term Memory RNNs in UMC
65 nm technology capable to operate at a measured peak efficiency up to 3.08
Gop/s/mW at 1.24 mW peak power. To implement big RNN models without incurring
in huge memory transfer overhead, multiple Chipmunk engines can cooperate to
form a single systolic array. In this way, the Chipmunk architecture in a 75
tiles configuration can achieve real-time phoneme extraction on a demanding RNN
topology proposed by Graves et al., consuming less than 13 mW of average power.
Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition
Authors: Mohammad Rostami, Soheil Kolouri, Kyungnam Kim, Eric Eaton
Date: 2017-09-15 00:00:00
Tasks: Lifelong learning, Multi-Task Learning

Lifelong machine learning methods acquire knowledge over a series of
consecutive tasks, continually building upon their experience. Current lifelong
learning algorithms rely upon a single learning agent that has centralized
access to all data. In this paper, we extend the idea of lifelong learning from
a single agent to a network of multiple agents that collectively learn a series
of tasks. Each agent faces some (potentially unique) set of tasks; the key idea
is that knowledge learned from these tasks may benefit other agents trying to
learn different (but related) tasks. Our Collective Lifelong Learning Algorithm
(CoLLA) provides an efficient way for a network of agents to share their
learned knowledge in a distributed and decentralized manner, while preserving
the privacy of the locally observed data. Note that a decentralized scheme is a
subclass of distributed algorithms where a central server does not exist and in
addition to data, computations are also distributed among the agents. We
provide theoretical guarantees for robust performance of the algorithm and
empirically demonstrate that CoLLA outperforms existing approaches for
distributed multi-task learning on a variety of data sets.
A folded model for compositional data analysis
Authors: Michail Tsagris, Connie Stewart
Date: 2018-02-20 00:00:00
Tasks: model, parameter estimation

A folded type model is developed for analyzing compositional data. The
proposed model involves an extension of the $\alpha$-transformation for
compositional data and provides a new and flexible class of distributions for
modeling data defined on the simplex sample space. Despite its rather seemingly
complex structure, employment of the EM algorithm guarantees efficient
parameter estimation. The model is validated through simulation studies and
examples which illustrate that the proposed model performs better in terms of
capturing the data structure, when compared to the popular logistic normal
distribution, and can be advantageous over a similar model without folding.
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning
Authors: Junhong Xu, Shangyue Zhu, Hanqing Guo, Shaoen Wu
Date: 2017-09-22 00:00:00
Tasks: Autonomous Navigation, Imitation Learning, Sensor Fusion

Imitation learning holds the promise to address challenging robotic tasks
such as autonomous navigation. It however requires a human supervisor to
oversee the training process and send correct control commands to robots
without feedback, which is always prone to error and expensive. To minimize
human involvement and avoid manual labeling of data in the robotic autonomous
navigation with imitation learning, this paper proposes a novel semi-supervised
imitation learning solution based on a multi-sensory design. This solution
includes a suboptimal sensor policy based on sensor fusion to automatically
label states encountered by a robot to avoid human supervision during training.
In addition, a recording policy is developed to throttle the adversarial affect
of learning too much from the suboptimal sensor policy. This solution allows
the robot to learn a navigation policy in a self-supervised manner. With
extensive experiments in indoor environments, this solution can achieve near
human performance in most of the tasks and even surpasses human performance in
case of unexpected events such as hardware failures or human operation errors.
To best of our knowledge, this is the first work that synthesizes sensor fusion
and imitation learning to enable robotic autonomous navigation in the real
world without human supervision.
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity
Authors: Vishesh Jain, Frederic Koehler, Elchanan Mossel
Date: 2018-02-16 00:00:00

The mean field approximation to the Ising model is a canonical variational
tool that is used for analysis and inference in Ising models. We provide a
simple and optimal bound for the KL error of the mean field approximation for
Ising models on general graphs, and extend it to higher order Markov random
fields. Our bound improves on previous bounds obtained in work in the graph
limit literature by Borgs, Chayes, Lov\'asz, S\'os, and Vesztergombi and
another recent work by Basak and Mukherjee. Our bound is tight up to lower
order terms. Building on the methods used to prove the bound, along with
techniques from combinatorics and optimization, we study the algorithmic
problem of estimating the (variational) free energy for Ising models and
general Markov random fields. For a graph $G$ on $n$ vertices and interaction
matrix $J$ with Frobenius norm $\| J \|_F$, we provide algorithms that
approximate the free energy within an additive error of $\epsilon n \|J\|_F$ in
time $\exp(poly(1/\epsilon))$. We also show that approximation within $(n
\|J\|_F)^{1-\delta}$ is NP-hard for every $\delta > 0$. Finally, we provide
more efficient approximation algorithms, which find the optimal mean field
approximation, for ferromagnetic Ising models and for Ising models satisfying
Dobrushin's condition.
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Authors: Valentin Stanev, Velimir V. Vesselinov, A. Gilad Kusne, Graham Antoszewski, Ichiro Takeuchi, Boian S. Alexandrov
Date: 2018-02-20 00:00:00
Tasks: Clustering, X-Ray Diffraction (XRD)

Analyzing large X-ray diffraction (XRD) datasets is a key step in
high-throughput mapping of the compositional phase diagrams of combinatorial
materials libraries. Optimizing and automating this task can help accelerate
the process of discovery of materials with novel and desirable properties.
Here, we report a new method for pattern analysis and phase extraction of XRD
datasets. The method expands the Nonnegative Matrix Factorization method, which
has been used previously to analyze such datasets, by combining it with custom
clustering and cross-correlation algorithms. This new method is capable of
robust determination of the number of basis patterns present in the data which,
in turn, enables straightforward identification of any possible peak-shifted
patterns. Peak-shifting arises due to continuous change in the lattice
constants as a function of composition, and is ubiquitous in XRD datasets from
composition spread libraries. Successful identification of the peak-shifted
patterns allows proper quantification and classification of the basis XRD
patterns, which is necessary in order to decipher the contribution of each
unique single-phase structure to the multi-phase regions. The process can be
utilized to determine accurately the compositional phase diagram of a system
under study. The presented method is applied to one synthetic and one
experimental dataset, and demonstrates robust accuracy and identification
abilities.
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning
Authors: Christopher Frederickson, Michael Moore, Glenn Dawson, Robi Polikar
Date: 2018-02-20 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, Outlier Detection

As the prevalence and everyday use of machine learning algorithms, along with
our reliance on these algorithms grow dramatically, so do the efforts to attack
and undermine these algorithms with malicious intent, resulting in a growing
interest in adversarial machine learning. A number of approaches have been
developed that can render a machine learning algorithm ineffective through
poisoning or other types of attacks. Most attack algorithms typically use
sophisticated optimization approaches, whose objective function is designed to
cause maximum damage with respect to accuracy and performance of the algorithm
with respect to some task. In this effort, we show that while such an objective
function is indeed brutally effective in causing maximum damage on an embedded
feature selection task, it often results in an attack mechanism that can be
easily detected with an embarrassingly simple novelty or outlier detection
algorithm. We then propose an equally simple yet elegant solution by adding a
regularization term to the attacker's objective function that penalizes
outlying attack points.
Logic Programming Applications: What Are the Abstractions and Implementations?
Authors: Yanhong A. Liu
Date: 2018-02-20 00:00:00

This article presents an overview of applications of logic programming,
classifying them based on the abstractions and implementations of logic
languages that support the applications. The three key abstractions are join,
recursion, and constraint. Their essential implementations are for-loops, fixed
points, and backtracking, respectively. The corresponding kinds of applications
are database queries, inductive analysis, and combinatorial search,
respectively. We also discuss language extensions and programming paradigms,
summarize example application problems by application areas, and touch on
example systems that support variants of the abstractions with different
implementations.
Meta-Reinforcement Learning of Structured Exploration Strategies
Authors: Abhishek Gupta, Russell Mendonca, Yuxuan Liu, Pieter Abbeel, Sergey Levine
Date: 2018-02-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration is a fundamental challenge in reinforcement learning (RL). Many
of the current exploration methods for deep RL use task-agnostic objectives,
such as information gain or bonuses based on state visitation. However, many
practical applications of RL involve learning more than a single task, and
prior tasks can be used to inform how exploration should be performed in new
tasks. In this work, we explore how prior tasks can inform an agent about how
to explore effectively in new situations. We introduce a novel gradient-based
fast adaptation algorithm -- model agnostic exploration with structured noise
(MAESN) -- to learn exploration strategies from prior experience. The prior
experience is used both to initialize a policy and to acquire a latent
exploration space that can inject structured stochasticity into a policy,
producing exploration strategies that are informed by prior knowledge and are
more effective than random action-space noise. We show that MAESN is more
effective at learning exploration strategies when compared to prior meta-RL
methods, RL without learned exploration strategies, and task-agnostic
exploration methods. We evaluate our method on a variety of simulated tasks:
locomotion with a wheeled robot, locomotion with a quadrupedal walker, and
object manipulation.
Continual Reinforcement Learning with Complex Synapses
Authors: Christos Kaplanis, Murray Shanahan, Claudia Clopath
Date: 2018-02-20 00:00:00
Proceeding: ICML 2018 7
Tasks: Continual Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Unlike humans, who are capable of continual learning over their lifetimes,
artificial neural networks have long been known to suffer from a phenomenon
known as catastrophic forgetting, whereby new learning can lead to abrupt
erasure of previously acquired knowledge. Whereas in a neural network the
parameters are typically modelled as scalar values, an individual synapse in
the brain comprises a complex network of interacting biochemical components
that evolve at different timescales. In this paper, we show that by equipping
tabular and deep reinforcement learning agents with a synaptic model that
incorporates this biological complexity (Benna & Fusi, 2016), catastrophic
forgetting can be mitigated at multiple timescales. In particular, we find that
as well as enabling continual learning across sequential training of two simple
tasks, it can also be used to overcome within-task forgetting by reducing the
need for an experience replay database.