pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Align Images using Weak Geometric Supervision
Authors: Jing Dong, Byron Boots, Frank Dellaert, Ranveer Chandra, Sudipta N. Sinha
Date: 2018-08-04 00:00:00
Tasks: Video Alignment

Image alignment tasks require accurate pixel correspondences, which are
usually recovered by matching local feature descriptors. Such descriptors are
often derived using supervised learning on existing datasets with ground truth
correspondences. However, the cost of creating such datasets is usually
prohibitive. In this paper, we propose a new approach to align two images
related by an unknown 2D homography where the local descriptor is learned from
scratch from the images and the homography is estimated simultaneously. Our key
insight is that a siamese convolutional neural network can be trained jointly
while iteratively updating the homography parameters by optimizing a single
loss function. Our method is currently weakly supervised because the input
images need to be roughly aligned.
  We have used this method to align images of different modalities such as RGB
and near-infra-red (NIR) without using any prior labeled data. Images
automatically aligned by our method were then used to train descriptors that
generalize to new images. We also evaluated our method on RGB images. On the
HPatches benchmark, our method achieves comparable accuracy to deep local
descriptors that were trained offline in a supervised setting.
Language Model Supervision for Handwriting Recognition Model Adaptation
Authors: Chris Tensmeyer, Curtis Wigington, Brian Davis, Seth Stewart, Tony Martinez, William Barrett
Date: 2018-08-04 00:00:00
Tasks: Handwriting Recognition, Language Modeling, Language Modelling, model, Transfer Learning

Training state-of-the-art offline handwriting recognition (HWR) models
requires large labeled datasets, but unfortunately such datasets are not
available in all languages and domains due to the high cost of manual
labeling.We address this problem by showing how high resource languages can be
leveraged to help train models for low resource languages.We propose a transfer
learning methodology where we adapt HWR models trained on a source language to
a target language that uses the same writing script.This methodology only
requires labeled data in the source language, unlabeled data in the target
language, and a language model of the target language. The language model is
used in a bootstrapping fashion to refine predictions in the target language
for use as ground truth in training the model.Using this approach we
demonstrate improved transferability among French, English, and Spanish
languages using both historical and modern handwriting datasets. In the best
case, transferring with the proposed methodology results in character error
rates nearly as good as full supervised training.
On Lipschitz Bounds of General Convolutional Neural Networks
Authors: Dongmian Zou, Radu Balan, Maneesh Singh
Date: 2018-08-04 00:00:00

Many convolutional neural networks (CNNs) have a feed-forward structure. In
this paper, a linear program that estimates the Lipschitz bound of such CNNs is
proposed. Several CNNs, including the scattering networks, the AlexNet and the
GoogleNet, are studied numerically and compared to the theoretical bounds.
Next, concentration inequalities of the output distribution to a stationary
random input signal expressed in terms of the Lipschitz bound are established.
The Lipschitz bound is further used to establish a nonlinear discriminant
analysis designed to measure the separation between features of different
classes.
Active Learning for Wireless IoT Intrusion Detection
Authors: Kai Yang, Jie Ren, Yanqiao Zhu, Weiyi Zhang
Date: 2018-08-04 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Intrusion Detection

Internet of Things (IoT) is becoming truly ubiquitous in our everyday life,
but it also faces unique security challenges. Intrusion detection is critical
for the security and safety of a wireless IoT network. This paper discusses the
human-in-the-loop active learning approach for wireless intrusion detection. We
first present the fundamental challenges against the design of a successful
Intrusion Detection System (IDS) for wireless IoT network. We then briefly
review the rudimentary concepts of active learning and propose its employment
in the diverse applications of wireless intrusion detection. Experimental
example is also presented to show the significant performance improvement of
the active learning method over traditional supervised learning approach. While
machine learning techniques have been widely employed for intrusion detection,
the application of human-in-the-loop machine learning that leverages both
machine and human intelligence to intrusion detection of IoT is still in its
infancy. We hope this article can assist the readers in understanding the key
concepts of active learning and spur further research in this area.
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis
Authors: Daisy Stanton, Yuxuan Wang, RJ Skerry-Ryan
Date: 2018-08-04 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Global Style Tokens (GSTs) are a recently-proposed method to learn latent
disentangled representations of high-dimensional data. GSTs can be used within
Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to
uncover expressive factors of variation in speaking style. In this work, we
introduce the Text-Predicted Global Style Token (TP-GST) architecture, which
treats GST combination weights or style embeddings as "virtual" speaking style
labels within Tacotron. TP-GST learns to predict stylistic renderings from text
alone, requiring neither explicit labels during training nor auxiliary inputs
for inference. We show that, when trained on a dataset of expressive speech,
our system generates audio with more pitch and energy variation than two
state-of-the-art baseline models. We further demonstrate that TP-GSTs can
synthesize speech with background noise removed, and corroborate these analyses
with positive results on human-rated listener preference audiobook tasks.
Finally, we demonstrate that multi-speaker TP-GST models successfully factorize
speaker identity and speaking style. We provide a website with audio samples
for each of our findings.
Teacher Guided Architecture Search
Authors: Pouya Bashivan, Mark Tensen, James J. DiCarlo
Date: 2018-08-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Computational Efficiency, Neural Architecture Search

Much of the recent improvement in neural networks for computer vision has resulted from discovery of new networks architectures. Most prior work has used the performance of candidate models following limited training to automatically guide the search in a feasible way. Could further gains in computational efficiency be achieved by guiding the search via measurements of a high performing network with unknown detailed architecture (e.g. the primate visual system)? As one step toward this goal, we use representational similarity analysis to evaluate the similarity of internal activations of candidate networks with those of a (fixed, high performing) teacher network. We show that adopting this evaluation metric could produce up to an order of magnitude in search efficiency over performance-guided methods. Our approach finds a convolutional cell structure with similar performance as was previously found using other methods but at a total computational cost that is two orders of magnitude lower than Neural Architecture Search (NAS) and more than four times lower than progressive neural architecture search (PNAS). We further show that measurements from only ~300 neurons from primate visual system provides enough signal to find a network with an Imagenet top-1 error that is significantly lower than that achieved by performance-guided architecture search alone. These results suggest that representational matching can be used to accelerate network architecture search in cases where one has access to some or all of the internal representations of a teacher network of interest, such as the brain's sensory processing networks.
code2seq: Generating Sequences from Structured Representations of Code
Authors: Uri Alon, Shaked Brody, Omer Levy, Eran Yahav
Date: 2018-08-04 00:00:00
Proceeding: ICLR 2019 5
Tasks: Code Summarization, NMT, Retrieval, Source Code Summarization, Translation

The ability to generate natural language sequences from source code snippets
has a variety of applications such as code summarization, documentation, and
retrieval. Sequence-to-sequence (seq2seq) models, adopted from neural machine
translation (NMT), have achieved state-of-the-art performance on these tasks by
treating source code as a sequence of tokens. We present ${\rm {\scriptsize
CODE2SEQ}}$: an alternative approach that leverages the syntactic structure of
programming languages to better encode source code. Our model represents a code
snippet as the set of compositional paths in its abstract syntax tree (AST) and
uses attention to select the relevant paths while decoding. We demonstrate the
effectiveness of our approach for two tasks, two programming languages, and
four datasets of up to $16$M examples. Our model significantly outperforms
previous models that were specifically designed for programming languages, as
well as state-of-the-art NMT models. An interactive online demo of our model is
available at http://code2seq.org. Our code, data and trained models are
available at http://github.com/tech-srl/code2seq.
Large Scale Language Modeling: Converging on 40GB of Text in Four Hours
Authors: Raul Puri, Robert Kirby, Nikolai Yakovenko, Bryan Catanzaro
Date: 2018-08-03 00:00:00
Tasks: Language Modeling, Language Modelling

Recent work has shown how to train Convolutional Neural Networks (CNNs)
rapidly on large image datasets, then transfer the knowledge gained from these
models to a variety of tasks. Following [Radford 2017], in this work, we
demonstrate similar scalability and transfer for Recurrent Neural Networks
(RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and
a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to
train a character-level 4096-dimension multiplicative LSTM (mLSTM) for
unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews
dataset in four hours. This runtime compares favorably with previous work
taking one month to train the same size and configuration for one epoch over
the same dataset. Converging large batch RNN models can be challenging. Recent
work has suggested scaling the learning rate as a function of batch size, but
we find that simply scaling the learning rate as a function of batch size leads
either to significantly worse convergence or immediate divergence for this
problem. We provide a learning rate schedule that allows our model to converge
with a 32k batch size. Since our model converges over the Amazon Reviews
dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while
substantial, is commercially available, this work opens up large scale
unsupervised NLP training to most commercial applications and deep learning
researchers. A model can be trained over most public or private text datasets
overnight.
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware
Authors: Ananda Samajdar, Parth Mannan, Kartikay Garg, Tushar Krishna
Date: 2018-08-03 00:00:00
Tasks: CPU, GPU, image-classification, Image Classification, OpenAI Gym, Reinforcement Learning, speech-recognition, Speech Recognition

Modern deep learning systems rely on (a) a hand-tuned neural network
topology, (b) massive amounts of labeled training data, and (c) extensive
training over large-scale compute resources to build a system that can perform
efficient image classification or speech recognition. Unfortunately, we are
still far away from implementing adaptive general purpose intelligent systems
which would need to learn autonomously in unknown environments and may not have
access to some or any of these three components. Reinforcement learning and
evolutionary algorithm (EA) based methods circumvent this problem by
continuously interacting with the environment and updating the models based on
obtained rewards. However, deploying these algorithms on ubiquitous autonomous
agents at the edge (robots/drones) demands extremely high energy-efficiency due
to (i) tight power and energy budgets, (ii) continuous/lifelong interaction
with the environment, (iii) intermittent or no connectivity to the cloud to run
heavy-weight processing. To address this need, we present GENESYS, an HW-SW
prototype of an EA-based learning system, that comprises a closed loop learning
engine called EvE and an inference engine called ADAM. EvE can evolve the
topology and weights of neural networks completely in hardware for the task at
hand, without requiring hand-optimization or backpropagation training. ADAM
continuously interacts with the environment and is optimized for efficiently
running the irregular neural networks generated by EvE. GENESYS identifies and
leverages multiple unique avenues of parallelism unique to EAs that we term
'gene'- level parallelism, and 'population'-level parallelism. We ran GENESYS
with a suite of environments from OpenAI gym and observed 2-5 orders of
magnitude higher energy-efficiency over state-of-the-art embedded and desktop
CPU and GPU systems.
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
Authors: Francisco J. Gonzalez, Maciej Balajewicz
Date: 2018-08-03 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Model reduction of high-dimensional dynamical systems alleviates
computational burdens faced in various tasks from design optimization to model
predictive control. One popular model reduction approach is based on projecting
the governing equations onto a subspace spanned by basis functions obtained
from the compression of a dataset of solution snapshots. However, this method
is intrusive since the projection requires access to the system operators.
Further, some systems may require special treatment of nonlinearities to ensure
computational efficiency or additional modeling to preserve stability. In this
work we propose a deep learning-based strategy for nonlinear model reduction
that is inspired by projection-based model reduction where the idea is to
identify some optimal low-dimensional representation and evolve it in time. Our
approach constructs a modular model consisting of a deep convolutional
autoencoder and a modified LSTM network. The deep convolutional autoencoder
returns a low-dimensional representation in terms of coordinates on some
expressive nonlinear data-supporting manifold. The dynamics on this manifold
are then modeled by the modified LSTM network in a computationally efficient
manner. An offline unsupervised training strategy that exploits the model
modularity is also developed. We demonstrate our model on three illustrative
examples each highlighting the model's performance in prediction tasks for
fluid systems with large parameter-variations and its stability in long-term
prediction.
Investigating the performance of multi-objective optimization when learning Bayesian Networks
Authors: Paolo Cazzaniga, Marco S. Nobile, Daniele Ramazzotti
Date: 2018-08-03 00:00:00

Bayesian Networks have been widely used in the last decades in many fields, to describe statistical dependencies among random variables. In general, learning the structure of such models is a problem with considerable theoretical interest that poses many challenges. On the one hand, it is a well-known NP-complete problem, practically hardened by the huge search space of possible solutions. On the other hand, the phenomenon of I-equivalence, i.e., different graphical structures underpinning the same set of statistical dependencies, may lead to multimodal fitness landscapes further hindering maximum likelihood approaches to solve the task. In particular, we exploit the NSGA-II multi-objective optimization procedure in order to explicitly account for both the likelihood of a solution and the number of selected arcs, by setting these as the two objective functions of the method. The aim of this work is to investigate the behavior of NSGA-II and analyse the quality of its solutions. We thus thoroughly examined the optimization results obtained on a wide set of simulated data, by considering both the goodness of the inferred solutions in terms of the objective functions values achieved, and by comparing the retrieved structures with the ground truth, i.e., the networks used to generate the target data. Our results show that NSGA-II can converge to solutions characterized by better likelihood and less arcs than classic approaches, although paradoxically characterized in many cases by a lower similarity with the target network.
Purely Geometric Scene Association and Retrieval - A Case for Macro Scale 3D Geometry
Authors: Rahul Sawhney, Fuxin Li, Henrik I. Christensen, Charles L. Isbell
Date: 2018-08-03 00:00:00
Tasks: 3D geometry, Retrieval

We address the problems of measuring geometric similarity between 3D scenes,
represented through point clouds or range data frames, and associating them.
Our approach leverages macro-scale 3D structural geometry - the relative
configuration of arbitrary surfaces and relationships among structures that are
potentially far apart. We express such discriminative information in a
viewpoint-invariant feature space. These are subsequently encoded in a
frame-level signature that can be utilized to measure geometric similarity.
Such a characterization is robust to noise, incomplete and partially
overlapping data besides viewpoint changes. We show how it can be employed to
select a diverse set of data frames which have structurally similar content,
and how to validate whether views with similar geometric content are from the
same scene. The problem is formulated as one of general purpose retrieval from
an unannotated, spatio-temporally unordered database. Empirical analysis
indicates that the presented approach thoroughly outperforms baselines on depth
/ range data. Its depth-only performance is competitive with state-of-the-art
approaches with RGB or RGB-D inputs, including ones based on deep learning.
Experiments show retrieval performance to hold up well with much sparser
databases, which is indicative of the approach's robustness. The approach
generalized well - it did not require dataset specific training, and scaled up
in our experiments. Finally, we also demonstrate how geometrically diverse
selection of views can result in richer 3D reconstructions.
A Cooperative Group Optimization System
Authors: Xiao-Feng Xie, Jiming Liu, Zun-Jing Wang
Date: 2018-08-03 00:00:00

A cooperative group optimization (CGO) system is presented to implement CGO
cases by integrating the advantages of the cooperative group and low-level
algorithm portfolio design. Following the nature-inspired paradigm of a
cooperative group, the agents not only explore in a parallel way with their
individual memory, but also cooperate with their peers through the group
memory. Each agent holds a portfolio of (heterogeneous) embedded search
heuristics (ESHs), in which each ESH can drive the group into a stand-alone CGO
case, and hybrid CGO cases in an algorithmic space can be defined by low-level
cooperative search among a portfolio of ESHs through customized memory sharing.
The optimization process might also be facilitated by a passive group leader
through encoding knowledge in the search landscape. Based on a concrete
framework, CGO cases are defined by a script assembling over instances of
algorithmic components in a toolbox. A multilayer design of the script, with
the support of the inherent updatable graph in the memory protocol, enables a
simple way to address the challenge of accumulating heterogeneous ESHs and
defining customized portfolios without any additional code. The CGO system is
implemented for solving the constrained optimization problem with some generic
components and only a few domain-specific components. Guided by the insights
from algorithm portfolio design, customized CGO cases based on basic search
operators can achieve competitive performance over existing algorithms as
compared on a set of commonly-used benchmark instances. This work might provide
a basic step toward a user-oriented development framework, since the
algorithmic space might be easily evolved by accumulating competent ESHs.
A Short Note about Kinetics-600
Authors: Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, Andrew Zisserman
Date: 2018-08-03 00:00:00
Tasks: Action Classification

We describe an extension of the DeepMind Kinetics human action dataset from
400 classes, each with at least 400 video clips, to 600 classes, each with at
least 600 video clips. In order to scale up the dataset we changed the data
collection process so it uses multiple queries per class, with some of them in
a language other than english -- portuguese. This paper details the changes
between the two versions of the dataset and includes a comprehensive set of
statistics of the new version as well as baseline results using the I3D neural
network architecture. The paper is a companion to the release of the ground
truth labels for the public test set.
Detailed Human Avatars from Monocular Video
Authors: Thiemo Alldieck, Marcus Magnor, Weipeng Xu, Christian Theobalt, Gerard Pons-Moll
Date: 2018-08-03 00:00:00

We present a novel method for high detail-preserving human avatar creation
from monocular video. A parameterized body model is refined and optimized to
maximally resemble subjects from a video showing them from all sides. Our
avatars feature a natural face, hairstyle, clothes with garment wrinkles, and
high-resolution texture. Our paper contributes facial landmark and
shading-based human body shape refinement, a semantic texture prior, and a
novel texture stitching strategy, resulting in the most sophisticated-looking
human avatars obtained from a single video to date. Numerous results show the
robustness and versatility of our method. A user study illustrates its
superiority over the state-of-the-art in terms of identity preservation, level
of detail, realism, and overall user preference.
Parsing Geometry Using Structure-Aware Shape Templates
Authors: Vignesh Ganapathi-Subramanian, Olga Diamanti, Soeren Pirk, Chengcheng Tang, Matthias Niessner, Leonidas J. Guibas
Date: 2018-08-03 00:00:00
Proceeding: 3D Vision 2018 2018 9
Tasks: Object, Object Recognition, Scene Understanding

Real-life man-made objects often exhibit strong and easily-identifiable
structure, as a direct result of their design or their intended functionality.
Structure typically appears in the form of individual parts and their
arrangement. Knowing about object structure can be an important cue for object
recognition and scene understanding - a key goal for various AR and robotics
applications. However, commodity RGB-D sensors used in these scenarios only
produce raw, unorganized point clouds, without structural information about the
captured scene. Moreover, the generated data is commonly partial and
susceptible to artifacts and noise, which makes inferring the structure of
scanned objects challenging. In this paper, we organize large shape collections
into parameterized shape templates to capture the underlying structure of the
objects. The templates allow us to transfer the structural information onto new
objects and incomplete scans. We employ a deep neural network that matches the
partial scan with one of the shape templates, then match and fit it to complete
and detailed models from the collection. This allows us to faithfully label its
parts and to guide the reconstruction of the scanned object. We showcase the
effectiveness of our method by comparing it to other state-of-the-art
approaches.