pwc-archive/papers-with-abstracts huggingface dataset:
Asynchronous adaptive networks
Authors: Ali H. Sayed, Xiaochuan Zhao
Date: 2015-11-30 00:00:00

In a recent article [1] we surveyed advances related to adaptation, learning,
and optimization over synchronous networks. Various distributed strategies were
discussed that enable a collection of networked agents to interact locally in
response to streaming data and to continually learn and adapt to track drifts
in the data and models. Under reasonable technical conditions on the data, the
adaptive networks were shown to be mean-square stable in the slow adaptation
regime, and their mean-square-error performance and convergence rate were
characterized in terms of the network topology and data statistical moments
[2]. Classical results for single-agent adaptation and learning were recovered
as special cases. Following the works [3]-[5], this chapter complements the
exposition from [1] and extends the results to asynchronous networks. The
operation of this class of networks can be subject to various sources of
uncertainties that influence their dynamic behavior, including randomly
changing topologies, random link failures, random data arrival times, and
agents turning on and off randomly. In an asynchronous environment, agents may
stop updating their solutions or may stop sending or receiving information in a
random manner and without coordination with other agents. The presentation will
reveal that the mean-square-error performance of asynchronous networks remains
largely unaltered compared to synchronous networks. The results justify the
remarkable resilience of cooperative networks in the face of random events.
Parallel Mapper
Authors: Mustafa Hajij, Basem Assiri, Paul Rosen
Date: 2017-12-11 00:00:00
Tasks: Topological Data Analysis

The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees. In this paper, we study the parallel analysis of the construction of Mapper. We give a provably correct parallel algorithm to execute Mapper on multiple processors and discuss the performance results that compare our approach to a reference sequential Mapper implementation. We report the performance experiments that demonstrate the efficiency of our method.
Deep Reinforcement Learning Boosted by External Knowledge
Authors: Nicolas Bougie, Ryutaro Ichise
Date: 2017-12-12 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent improvements in deep reinforcement learning have allowed to solve
problems in many 2D domains such as Atari games. However, in complex 3D
environments, numerous learning episodes are required which may be too time
consuming or even impossible especially in real-world scenarios. We present a
new architecture to combine external knowledge and deep reinforcement learning
using only visual input. A key concept of our system is augmenting image input
by adding environment feature information and combining two sources of
decision. We evaluate the performances of our method in a 3D
partially-observable environment from the Microsoft Malmo platform.
Experimental evaluation exhibits higher performance and faster learning
compared to a single reinforcement learning model.
Continual Learning with Deep Generative Replay
Authors: Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim
Date: 2017-05-24 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: class-incremental learning, Class Incremental Learning, Continual Learning, General Classification, Hippocampus, image-classification, Image Classification

Attempts to train a comprehensive artificial intelligence capable of solving
multiple tasks have been impeded by a chronic problem called catastrophic
forgetting. Although simply replaying all previous data alleviates the problem,
it requires large memory and even worse, often infeasible in real world
applications where the access to past data is limited. Inspired by the
generative nature of hippocampus as a short-term memory system in primate
brain, we propose the Deep Generative Replay, a novel framework with a
cooperative dual model architecture consisting of a deep generative model
("generator") and a task solving model ("solver"). With only these two models,
training data for previous tasks can easily be sampled and interleaved with
those for a new task. We test our methods in several sequential learning
settings involving image classification tasks.
Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning
Authors: Andrew Gordon Wilson, Jason Yosinski, Patrice Simard, Rich Caruana, William Herlands
Date: 2017-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine
Learning, held in Long Beach, California, USA on December 7, 2017
Context Augmentation for Convolutional Neural Networks
Authors: Aysegul Dundar, Ignacio Garcia-Dorado
Date: 2017-11-22 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object Recognition

Recent enhancements of deep convolutional neural networks (ConvNets)
empowered by enormous amounts of labeled data have closed the gap with human
performance for many object recognition tasks. These impressive results have
generated interest in understanding and visualization of ConvNets. In this
work, we study the effect of background in the task of image classification.
Our results show that changing the backgrounds of the training datasets can
have drastic effects on testing accuracies. Furthermore, we enhance existing
augmentation techniques with the foreground segmented objects. The findings of
this work are important in increasing the accuracies when only a small dataset
is available, in creating datasets, and creating synthetic images.
Learning Compressible 360° Video Isomers
Authors: Yu-Chuan Su, Kristen Grauman
Date: 2017-12-12 00:00:00

Standard video encoders developed for conventional narrow field-of-view video
are widely applied to 360{\deg} video as well, with reasonable results.
However, while this approach commits arbitrarily to a projection of the
spherical frames, we observe that some orientations of a 360{\deg} video, once
projected, are more compressible than others. We introduce an approach to
predict the sphere rotation that will yield the maximal compression rate. Given
video clips in their original encoding, a convolutional neural network learns
the association between a clip's visual content and its compressibility at
different rotations of a cubemap projection. Given a novel video, our
learning-based approach efficiently infers the most compressible direction in
one shot, without repeated rendering and compression of the source video. We
validate our idea on thousands of video clips and multiple popular video
codecs. The results show that this untapped dimension of 360{\deg} compression
has substantial potential--"good" rotations are typically 8-10% more
compressible than bad ones, and our learning approach can predict them reliably
82% of the time.
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Authors: Alexej Gossmann, Pascal Zille, Vince Calhoun, Yu-Ping Wang
Date: 2017-05-11 00:00:00

Reducing the number of false discoveries is presently one of the most
pressing issues in the life sciences. It is of especially great importance for
many applications in neuroimaging and genomics, where datasets are typically
high-dimensional, which means that the number of explanatory variables exceeds
the sample size. The false discovery rate (FDR) is a criterion that can be
employed to address that issue. Thus it has gained great popularity as a tool
for testing multiple hypotheses. Canonical correlation analysis (CCA) is a
statistical technique that is used to make sense of the cross-correlation of
two sets of measurements collected on the same set of samples (e.g., brain
imaging and genomic data for the same mental illness patients), and sparse CCA
extends the classical method to high-dimensional settings. Here we propose a
way of applying the FDR concept to sparse CCA, and a method to control the FDR.
The proposed FDR correction directly influences the sparsity of the solution,
adapting it to the unknown true sparsity level. Theoretical derivation as well
as simulation studies show that our procedure indeed keeps the FDR of the
canonical vectors below a user-specified target level. We apply the proposed
method to an imaging genomics dataset from the Philadelphia Neurodevelopmental
Cohort. Our results link the brain connectivity profiles derived from brain
activity during an emotion identification task, as measured by functional
magnetic resonance imaging (fMRI), to the corresponding subjects' genomic data.
In a Nutshell -- The Sequential Parameter Optimization Toolbox
Authors: Thomas Bartz-Beielstein, Martin Zaefferer, Frederik Rehbach
Date: 2017-12-12 00:00:00

The performance of optimization algorithms relies crucially on their parameterizations. Finding good parameter settings is called algorithm tuning. The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. SPOT includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. Using a simple simulated annealing algorithm, we will demonstrate how optimization algorithms can be tuned using SPOT. The underling concepts of the SPOT approach are explained. This includes key techniques such as exploratory fitness landscape analysis and sensititvity analysis. Many examples illustrate how SPOT can be used for understanding the performance of algorithms and gaining insight into algorithm's behavior. Furthermore, we demonstrate how SPOT can be used as an optimizer and how a sophisticated ensemble approach is able to combine several meta models via stacking. This article exemplifies how SPOT can be used for automatic and interactive tuning.
ImageNet Training in Minutes
Authors: Yang You, Zhao Zhang, Cho-Jui Hsieh, James Demmel, Kurt Keutzer
Date: 2017-09-14 00:00:00
Tasks: 16k, GPU, Playing the Game of 2048

Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU
takes 14 days. This training requires 10^18 single precision operations in
total. On the other hand, the world's current fastest supercomputer can finish
2 * 10^17 single precision operations per second (Dongarra et al 2017,
https://www.top500.org/lists/2017/06/). If we can make full use of the
supercomputer for DNN training, we should be able to finish the 90-epoch
ResNet-50 training in one minute. However, the current bottleneck for fast DNN
training is in the algorithm level. Specifically, the current batch size (e.g.
512) is too small to make efficient use of many processors. For large-scale DNN
training, we focus on using large-batch data-parallelism synchronous SGD
without losing accuracy in the fixed epochs. The LARS algorithm (You, Gitman,
Ginsburg, 2017, arXiv:1708.03888) enables us to scale the batch size to
extremely large case (e.g. 32K). We finish the 100-epoch ImageNet training with
AlexNet in 11 minutes on 1024 CPUs. About three times faster than Facebook's
result (Goyal et al 2017, arXiv:1706.02677), we finish the 90-epoch ImageNet
training with ResNet-50 in 20 minutes on 2048 KNLs without losing accuracy.
State-of-the-art ImageNet training speed with ResNet-50 is 74.9% top-1 test
accuracy in 15 minutes. We got 74.9% top-1 test accuracy in 64 epochs, which
only needs 14 minutes. Furthermore, when we increase the batch size to above
16K, our accuracy is much higher than Facebook's on corresponding batch sizes.
Our source code is available upon request.
Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources
Authors: John P. Dickerson, Karthik A. Sankararaman, Aravind Srinivasan, Pan Xu
Date: 2017-11-22 00:00:00

Bipartite matching markets pair agents on one side of a market with agents,
items, or contracts on the opposing side. Prior work addresses online bipartite
matching markets, where agents arrive over time and are dynamically matched to
a known set of disposable resources. In this paper, we propose a new model,
Online Matching with (offline) Reusable Resources under Known Adversarial
Distributions (OM-RR-KAD), in which resources on the offline side are reusable
instead of disposable; that is, once matched, resources become available again
at some point in the future. We show that our model is tractable by presenting
an LP-based adaptive algorithm that achieves an online competitive ratio of 1/2
- eps for any given eps greater than 0. We also show that no non-adaptive
algorithm can achieve a ratio of 1/2 + o(1) based on the same benchmark LP.
Through a data-driven analysis on a massive openly-available dataset, we show
our model is robust enough to capture the application of taxi dispatching
services and ride-sharing systems. We also present heuristics that perform well
in practice.
Human-like Clustering with Deep Convolutional Neural Networks
Authors: Ali Borji, Aysegul Dundar
Date: 2017-06-15 00:00:00
Tasks: Clustering, Object Recognition, Semantic Segmentation

Classification and clustering have been studied separately in machine
learning and computer vision. Inspired by the recent success of deep learning
models in solving various vision problems (e.g., object recognition, semantic
segmentation) and the fact that humans serve as the gold standard in assessing
clustering algorithms, here, we advocate for a unified treatment of the two
problems and suggest that hierarchical frameworks that progressively build
complex patterns on top of the simpler ones (e.g., convolutional neural
networks) offer a promising solution. We do not dwell much on the learning
mechanisms in these frameworks as they are still a matter of debate, with
respect to biological constraints. Instead, we emphasize on the
compositionality of the real world structures and objects. In particular, we
show that CNNs, trained end to end using back propagation with noisy labels,
are able to cluster data points belonging to several overlapping shapes, and do
so much better than the state of the art algorithms. The main takeaway lesson
from our study is that mechanisms of human vision, particularly the hierarchal
organization of the visual ventral stream should be taken into account in
clustering algorithms (e.g., for learning representations in an unsupervised
manner or with minimum supervision) to reach human level clustering
performance. This, by no means, suggests that other methods do not hold merits.
For example, methods relying on pairwise affinities (e.g., spectral clustering)
have been very successful in many scenarios but still fail in some cases (e.g.,
overlapping clusters).
The Eigenoption-Critic Framework
Authors: Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell
Date: 2017-12-11 00:00:00
Tasks: Efficient Exploration, Hierarchical Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Eigenoptions (EOs) have been recently introduced as a promising idea for
generating a diverse set of options through the graph Laplacian, having been
shown to allow efficient exploration. Despite its initial promising results, a
couple of issues in current algorithms limit its application, namely: (1) EO
methods require two separate steps (eigenoption discovery and reward
maximization) to learn a control policy, which can incur a significant amount
of storage and computation; (2) EOs are only defined for problems with discrete
state-spaces and; (3) it is not easy to take the environment's reward function
into consideration when discovering EOs. To addresses these issues, we
introduce an algorithm termed eigenoption-critic (EOC) based on the
Option-critic (OC) framework [Bacon17], a general hierarchical reinforcement
learning (RL) algorithm that allows learning the intra-option policies
simultaneously with the policy over options. We also propose a generalization
of EOC to problems with continuous state-spaces through the Nystr\"om
approximation. EOC can also be seen as extending OC to nonstationary settings,
where the discovered options are not tailored for a single task.
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
Authors: Hao Zhang, Shizhen Xu, Graham Neubig, Wei Dai, Qirong Ho, Guangwen Yang, Eric P. Xing
Date: 2017-12-11 00:00:00
Tasks: Dynamic neural networks, graph construction, Management, Scheduling

Recent deep learning (DL) models have moved beyond static network
architectures to dynamic ones, handling data where the network structure
changes every example, such as sequences of variable lengths, trees, and
graphs. Existing dataflow-based programming models for DL---both static and
dynamic declaration---either cannot readily express these dynamic models, or
are inefficient due to repeated dataflow graph construction and processing, and
difficulties in batched execution. We present Cavs, a vertex-centric
programming interface and optimized system implementation for dynamic DL
models. Cavs represents dynamic network structure as a static vertex function
$\mathcal{F}$ and a dynamic instance-specific graph $\mathcal{G}$, and performs
backpropagation by scheduling the execution of $\mathcal{F}$ following the
dependencies in $\mathcal{G}$. Cavs bypasses expensive graph construction and
preprocessing overhead, allows for the use of static graph optimization
techniques on pre-defined operations in $\mathcal{F}$, and naturally exposes
batched execution opportunities over different graphs. Experiments comparing
Cavs to two state-of-the-art frameworks for dynamic NNs (TensorFlow Fold and
DyNet) demonstrate the efficacy of this approach: Cavs achieves a near one
order of magnitude speedup on training of various dynamic NN architectures, and
ablations demonstrate the contribution of our proposed batching and memory
management strategies.
Character-Based Handwritten Text Transcription with Attention Networks
Authors: Jason Poulos, Rafael Valle
Date: 2017-12-11 00:00:00
Tasks: Decoder, Handwritten Text Recognition, HTR

The paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment on lines of text from popular handwriting datasets and compare different activation functions for the attention mechanism used for aligning image pixels and target characters. We find that softmax attention focuses heavily on individual characters, while sigmoid attention focuses on multiple characters at each step of the decoding. When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise. When a linear function is used to obtain attention weights, the model predicts a character by looking at the entire sequence of characters and performs poorly because it lacks a precise alignment between the source and target. Future research may explore HTR in natural scene images, since the model is capable of transcribing handwritten text without the need for producing segmentations or bounding boxes of text in images.
Learning Robust Dialog Policies in Noisy Environments
Authors: Maryam Fazel-Zarandi, Shang-Wen Li, Jin Cao, Jared Casale, Peter Henderson, David Whitney, Alborz Geramifard
Date: 2017-12-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Reinforcement Learning, Natural Language Understanding, Reinforcement Learning, speech-recognition, Speech Recognition

Modern virtual personal assistants provide a convenient interface for
completing daily tasks via voice commands. An important consideration for these
assistants is the ability to recover from automatic speech recognition (ASR)
and natural language understanding (NLU) errors. In this paper, we focus on
learning robust dialog policies to recover from these errors. To this end, we
develop a user simulator which interacts with the assistant through voice
commands in realistic scenarios with noisy audio, and use it to learn dialog
policies through deep reinforcement learning. We show that dialogs generated by
our simulator are indistinguishable from human generated dialogs, as determined
by human evaluators. Furthermore, preliminary experimental results show that
the learned policies in noisy environments achieve the same execution success
rate with fewer dialog turns compared to fixed rule-based policies.
Variational approach for learning Markov processes from time series data
Authors: Hao Wu, Frank Noé
Date: 2017-07-14 00:00:00
Tasks: Management, Model Selection, Time Series, Time Series Analysis

Inference, prediction and control of complex dynamical systems from time series is important in many areas, including financial markets, power grid management, climate and weather modeling, or molecular dynamics. The analysis of such highly nonlinear dynamical systems is facilitated by the fact that we can often find a (generally nonlinear) transformation of the system coordinates to features in which the dynamics can be excellently approximated by a linear Markovian model. Moreover, the large number of system variables often change collectively on large time- and length-scales, facilitating a low-dimensional analysis in feature space. In this paper, we introduce a variational approach for Markov processes (VAMP) that allows us to find optimal feature mappings and optimal Markovian models of the dynamics from given time series data. The key insight is that the best linear model can be obtained from the top singular components of the Koopman operator. This leads to the definition of a family of score functions called VAMP-r which can be calculated from data, and can be employed to optimize a Markovian model. In addition, based on the relationship between the variational scores and approximation errors of Koopman operators, we propose a new VAMP-E score, which can be applied to cross-validation for hyper-parameter optimization and model selection in VAMP. VAMP is valid for both reversible and nonreversible processes and for stationary and non-stationary processes or realizations.
Deep metric learning for multi-labelled radiographs
Authors: Mauro Annarumma, Giovanni Montana
Date: 2017-12-11 00:00:00
Tasks: Clustering, Image Retrieval, Metric Learning, Retrieval

Many radiological studies can reveal the presence of several co-existing
abnormalities, each one represented by a distinct visual pattern. In this
article we address the problem of learning a distance metric for plain
radiographs that captures a notion of "radiological similarity": two chest
radiographs are considered to be similar if they share similar abnormalities.
Deep convolutional neural networks (DCNs) are used to learn a low-dimensional
embedding for the radiographs that is equipped with the desired metric. Two
loss functions are proposed to deal with multi-labelled images and potentially
noisy labels. We report on a large-scale study involving over 745,000 chest
radiographs whose labels were automatically extracted from free-text
radiological reports through a natural language processing system. Using 4,500
validated exams, we demonstrate that the methodology performs satisfactorily on
clustering and image retrieval tasks. Remarkably, the learned metric separates
normal exams from those having radiological abnormalities.