pwc-archive/papers-with-abstracts huggingface dataset:
Towards a more flexible Language of Thought: Bayesian grammar updates after each concept exposure
Authors: Pablo Tano, Sergio Romano, Mariano Sigman, Alejo Salles, Santiago Figueira
Date: 2018-05-17 00:00:00
Tasks: Descriptive

Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples. The aim of most of these studies is to reveal the underlying language structuring these representations and providing a general substrate for thought. However, describing a model of thought that is fixed once trained is against the extensive literature that shows how experience shapes concept learning. Here, we ask about the plasticity of these symbolic descriptive languages. We perform a concept learning experiment that demonstrates that humans can change very rapidly the repertoire of symbols they use to identify concepts, by compiling expressions which are frequently used into new symbols of the language. The pattern of concept learning times is accurately described by a Bayesian agent that rationally updates the probability of compiling a new expression according to how useful it has been to compress concepts so far. By portraying the Language of Thought as a flexible system of rules, we also highlight the difficulties to pin it down empirically.
A Brief Introduction to Machine Learning for Engineers
Authors: Osvaldo Simeone
Date: 2017-09-08 00:00:00
Tasks: BIG-bench Machine Learning

This monograph aims at providing an introduction to key concepts, algorithms,
and theoretical results in machine learning. The treatment concentrates on
probabilistic models for supervised and unsupervised learning problems. It
introduces fundamental concepts and algorithms by building on first principles,
while also exposing the reader to more advanced topics with extensive pointers
to the literature, within a unified notation and mathematical framework. The
material is organized according to clearly defined categories, such as
discriminative and generative models, frequentist and Bayesian approaches,
exact and approximate inference, as well as directed and undirected models.
This monograph is meant as an entry point for researchers with a background in
probability and linear algebra.
A Note on Coding and Standardization of Categorical Variables in (Sparse) Group Lasso Regression
Authors: Felicitas J. Detmer, Martin Slawski
Date: 2018-05-17 00:00:00
Tasks: regression, Variable Selection

Categorical regressor variables are usually handled by introducing a set of
indicator variables, and imposing a linear constraint to ensure identifiability
in the presence of an intercept, or equivalently, using one of various coding
schemes. As proposed in Yuan and Lin [J. R. Statist. Soc. B, 68 (2006), 49-67],
the group lasso is a natural and computationally convenient approach to perform
variable selection in settings with categorical covariates. As pointed out by
Simon and Tibshirani [Stat. Sin., 22 (2011), 983-1001], "standardization" by
means of block-wise orthonormalization of column submatrices each corresponding
to one group of variables can substantially boost performance. In this note, we
study the aspect of standardization for the special case of categorical
predictors in detail. The main result is that orthonormalization is not
required; column-wise scaling of the design matrix followed by re-scaling and
centering of the coefficients is shown to have exactly the same effect. Similar
reductions can be achieved in the case of interactions. The extension to the
so-called sparse group lasso, which additionally promotes within-group
sparsity, is considered as well. The importance of proper standardization is
illustrated via extensive simulations.
Fully Convolutional Model for Variable Bit Length and Lossy High Density Compression of Mammograms
Authors: Aupendu Kar, Sri Phani Krishna Karri, Nirmalya Ghosh, Ramanathan Sethuraman, Debdoot Sheet
Date: 2018-05-17 00:00:00
Tasks: Image Compression, SSIM

Early works on medical image compression date to the 1980's with the impetus
on deployment of teleradiology systems for high-resolution digital X-ray
detectors. Commercially deployed systems during the period could compress 4,096
x 4,096 sized images at 12 bpp to 2 bpp using lossless arithmetic coding, and
over the years JPEG and JPEG2000 were imbibed reaching upto 0.1 bpp. Inspired
by the reprise of deep learning based compression for natural images over the
last two years, we propose a fully convolutional autoencoder for diagnostically
relevant feature preserving lossy compression. This is followed by leveraging
arithmetic coding for encapsulating high redundancy of features for further
high-density code packing leading to variable bit length. We demonstrate
performance on two different publicly available digital mammography datasets
using peak signal-to-noise ratio (pSNR), structural similarity (SSIM) index and
domain adaptability tests between datasets. At high density compression factors
of >300x (~0.04 bpp), our approach rivals JPEG and JPEG2000 as evaluated
through a Radiologist's visual Turing test.
Changing Observations in Epistemic Temporal Logic
Authors: Aurèle Barrière, Bastien Maubert, Aniello Murano, Sasha Rubin
Date: 2018-05-17 00:00:00

We study dynamic changes of agents' observational power in logics of
knowledge and time. We consider CTL*K, the extension of CTL* with knowledge
operators, and enrich it with a new operator that models a change in an agent's
way of observing the system. We extend the classic semantics of knowledge for
perfect-recall agents to account for changes of observation, and we show that
this new operator strictly increases the expressivity of CTL*K. We reduce the
model-checking problem for our logic to that for CTL*K, which is known to be
decidable. This provides a solution to the model-checking problem for our
logic, but its complexity is not optimal. Indeed we provide a direct decision
procedure with better complexity.
Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates
Authors: Leslie N. Smith, Nicholay Topin
Date: 2017-08-23 00:00:00

In this paper, we describe a phenomenon, which we named "super-convergence",
where neural networks can be trained an order of magnitude faster than with
standard training methods. The existence of super-convergence is relevant to
understanding why deep networks generalize well. One of the key elements of
super-convergence is training with one learning rate cycle and a large maximum
learning rate. A primary insight that allows super-convergence training is that
large learning rates regularize the training, hence requiring a reduction of
all other forms of regularization in order to preserve an optimal
regularization balance. We also derive a simplification of the Hessian Free
optimization method to compute an estimate of the optimal learning rate.
Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
datasets, and resnet, wide-resnet, densenet, and inception architectures. In
addition, we show that super-convergence provides a greater boost in
performance relative to standard training when the amount of labeled training
data is limited. The architectures and code to replicate the figures in this
paper are available at github.com/lnsmith54/super-convergence. See
http://www.fast.ai/2018/04/30/dawnbench-fastai/ for an application of
super-convergence to win the DAWNBench challenge (see
https://dawn.cs.stanford.edu/benchmark/).
It's all Relative: Monocular 3D Human Pose Estimation from Weakly Supervised Data
Authors: Matteo Ruggero Ronchi, Oisin Mac Aodha, Robert Eng, Pietro Perona
Date: 2018-05-17 00:00:00
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, 3D Pose Estimation, All, Monocular 3D Human Pose Estimation, Pose Estimation

We address the problem of 3D human pose estimation from 2D input images using
only weakly supervised training data. Despite showing considerable success for
2D pose estimation, the application of supervised machine learning to 3D pose
estimation in real world images is currently hampered by the lack of varied
training images with corresponding 3D poses. Most existing 3D pose estimation
algorithms train on data that has either been collected in carefully controlled
studio settings or has been generated synthetically. Instead, we take a
different approach, and propose a 3D human pose estimation algorithm that only
requires relative estimates of depth at training time. Such training signal,
although noisy, can be easily collected from crowd annotators, and is of
sufficient quality for enabling successful training and evaluation of 3D pose
algorithms. Our results are competitive with fully supervised regression based
approaches on the Human3.6M dataset, despite using significantly weaker
training data. Our proposed algorithm opens the door to using existing
widespread 2D datasets for 3D pose estimation by allowing fine-tuning with
noisy relative constraints, resulting in more accurate 3D poses.
Neural language representations predict outcomes of scientific research
Authors: James P. Bagrow, Daniel Berenberg, Joshua Bongard
Date: 2018-05-17 00:00:00

Many research fields codify their findings in standard formats, often by
reporting correlations between quantities of interest. But the space of all
testable correlates is far larger than scientific resources can currently
address, so the ability to accurately predict correlations would be useful to
plan research and allocate resources. Using a dataset of approximately 170,000
correlational findings extracted from leading social science journals, we show
that a trained neural network can accurately predict the reported correlations
using only the text descriptions of the correlates. Accurate predictive models
such as these can guide scientists towards promising untested correlates,
better quantify the information gained from new findings, and has implications
for moving artificial intelligence systems from predicting structures to
predicting relationships in the real world.
NeuralNetwork-Viterbi: A Framework for Weakly Supervised Video Learning
Authors: Alexander Richard, Hilde Kuehne, Ahsan Iqbal, Juergen Gall
Date: 2018-05-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Segmentation, Incremental Learning, Segmentation, Video Segmentation, Video Semantic Segmentation, Weakly Supervised Action Segmentation (Transcript)

Video learning is an important task in computer vision and has experienced
increasing interest over the recent years. Since even a small amount of videos
easily comprises several million frames, methods that do not rely on a
frame-level annotation are of special importance. In this work, we propose a
novel learning algorithm with a Viterbi-based loss that allows for online and
incremental learning of weakly annotated video data. We moreover show that
explicit context and length modeling leads to huge improvements in video
segmentation and labeling tasks andinclude these models into our framework. On
several action segmentation benchmarks, we obtain an improvement of up to 10%
compared to current state-of-the-art methods.
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
Authors: Alexander Richard, Hilde Kuehne, Juergen Gall
Date: 2017-06-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Detection, Action Segmentation

Action detection and temporal segmentation of actions in videos are topics of
increasing interest. While fully supervised systems have gained much attention
lately, full annotation of each action within the video is costly and
impractical for large amounts of video data. Thus, weakly supervised action
detection and temporal segmentation methods are of great importance. While most
works in this area assume an ordered sequence of occurring actions to be given,
our approach only uses a set of actions. Such action sets provide much less
supervision since neither action ordering nor the number of action occurrences
are known. In exchange, they can be easily obtained, for instance, from
meta-tags, while ordered sequences still require human annotation. We introduce
a system that automatically learns to temporally segment and label actions in a
video, where the only supervision that is used are action sets. An evaluation
on three datasets shows that our method still achieves good results although
the amount of supervision is significantly smaller than for other related
methods.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Authors: Mikel Artetxe, Gorka Labaka, Eneko Agirre
Date: 2018-05-16 00:00:00
Proceeding: ACL 2018 7
Tasks: Cross-Lingual Word Embeddings, Self-Learning, Word Embeddings

Recent work has managed to learn cross-lingual word embeddings without
parallel data by mapping monolingual embeddings to a shared space through
adversarial training. However, their evaluation has focused on favorable
conditions, using comparable corpora or closely-related languages, and we show
that they often fail in more realistic scenarios. This work proposes an
alternative approach based on a fully unsupervised initialization that
explicitly exploits the structural similarity of the embeddings, and a robust
self-learning algorithm that iteratively improves this solution. Our method
succeeds in all tested scenarios and obtains the best published results in
standard datasets, even surpassing previous supervised systems. Our
implementation is released as an open source project at
https://github.com/artetxem/vecmap
Resource allocation under uncertainty: an algebraic and qualitative treatment
Authors: Franklin Camacho, Gerardo Chacón, Ramón Pino Peréz
Date: 2018-05-17 00:00:00
Tasks: Relation

We use an algebraic viewpoint, namely a matrix framework to deal with the
problem of resource allocation under uncertainty in the context of a
qualitative approach. Our basic qualitative data are a plausibility relation
over the resources, a hierarchical relation over the agents and of course the
preference that the agents have over the resources. With this data we propose a
qualitative binary relation $\unrhd$ between allocations such that
$\mathcal{F}\unrhd \mathcal{G}$ has the following intended meaning: the
allocation $\mathcal{F}$ produces more or equal social welfare than the
allocation $\mathcal{G}$. We prove that there is a family of allocations which
are maximal with respect to $\unrhd$. We prove also that there is a notion of
simple deal such that optimal allocations can be reached by sequences of simple
deals. Finally, we introduce some mechanism for discriminating {optimal}
allocations.
Design Identification of Curve Patterns on Cultural Heritage Objects: Combining Template Matching and CNN-based Re-Ranking
Authors: Jun Zhou, Yuhang Lu, Kang Zheng, Karen Smith, Colin Wilder, Song Wang
Date: 2018-05-17 00:00:00
Tasks: Re-Ranking, Template Matching

The surfaces of many cultural heritage objects were embellished with various
patterns, especially curve patterns. In practice, most of the unearthed
cultural heritage objects are highly fragmented, e.g., sherds of potteries or
vessels, and each of them only shows a very small portion of the underlying
full design, with noise and deformations. The goal of this paper is to address
the challenging problem of automatically identifying the underlying full design
of curve patterns from such a sherd. Specifically, we formulate this problem as
template matching: curve structure segmented from the sherd is matched to each
location with each possible orientation of each known full design. In this
paper, we propose a new two-stage matching algorithm, with a different matching
cost in each stage. In Stage 1, we use a traditional template matching, which
is highly computationally efficient, over the whole search space and identify a
small set of candidate matchings. In Stage 2, we derive a new matching cost by
training a dual-source Convolutional Neural Network (CNN) and apply it to
re-rank the candidate matchings identified in Stage 1. We collect 600 pottery
sherds with 98 full designs from the Woodland Period in Southeastern North
America for experiments and the performance of the proposed algorithm is very
competitive.
Answer Set Programming Modulo `Space-Time'
Authors: Carl Schultz, Mehul Bhatt, Jakob Suchan, Przemysław Wałęga
Date: 2018-05-17 00:00:00

We present ASP Modulo `Space-Time', a declarative representational and
computational framework to perform commonsense reasoning about regions with
both spatial and temporal components. Supported are capabilities for mixed
qualitative-quantitative reasoning, consistency checking, and inferring
compositions of space-time relations; these capabilities combine and synergise
for applications in a range of AI application areas where the processing and
interpretation of spatio-temporal data is crucial. The framework and resulting
system is the only general KR-based method for declaratively reasoning about
the dynamics of `space-time' regions as first-class objects. We present an
empirical evaluation (with scalability and robustness results), and include
diverse application examples involving interpretation and control tasks.
Complexity Reduction in the Negotiation of New Lexical Conventions
Authors: William Schueller, Vittorio Loreto, Pierre-Yves Oudeyer
Date: 2018-05-15 00:00:00

In the process of collectively inventing new words for new concepts in a
population, conflicts can quickly become numerous, in the form of synonymy and
homonymy. Remembering all of them could cost too much memory, and remembering
too few may slow down the overall process. Is there an efficient behavior that
could help balance the two? The Naming Game is a multi-agent computational
model for the emergence of language, focusing on the negotiation of new lexical
conventions, where a common lexicon self-organizes but going through a phase of
high complexity. Previous work has been done on the control of complexity
growth in this particular model, by allowing agents to actively choose what
they talk about. However, those strategies were relying on ad hoc heuristics
highly dependent on fine-tuning of parameters. We define here a new principled
measure and a new strategy, based on the beliefs of each agent on the global
state of the population. The measure does not rely on heavy computation, and is
cognitively plausible. The new strategy yields an efficient control of
complexity growth, along with a faster agreement process. Also, we show that
short-term memory is enough to build relevant beliefs about the global lexicon.
RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
Authors: Xiuyuan Cheng, Qiang Qiu, Robert Calderbank, Guillermo Sapiro
Date: 2018-05-17 00:00:00
Proceeding: ICLR 2019 5

Explicit encoding of group actions in deep features makes it possible for
convolutional neural networks (CNNs) to handle global deformations of images,
which is critical to success in many vision tasks. This paper proposes to
decompose the convolutional filters over joint steerable bases across the space
and the group geometry simultaneously, namely a rotation-equivariant CNN with
decomposed convolutional filters (RotDCF). This decomposition facilitates
computing the joint convolution, which is proved to be necessary for the group
equivariance. It significantly reduces the model size and computational
complexity while preserving performance, and truncation of the bases expansion
serves implicitly to regularize the filters. On datasets involving in-plane and
out-of-plane object rotations, RotDCF deep features demonstrate greater
robustness and interpretability than regular CNNs. The stability of the
equivariant representation to input variations is also proved theoretically
under generic assumptions on the filters in the decomposed form. The RotDCF
framework can be extended to groups other than rotations, providing a general
approach which achieves both group equivariance and representation stability at
a reduced model size.
ScaffoldNet: Detecting and Classifying Biomedical Polymer-Based Scaffolds via a Convolutional Neural Network
Authors: Darlington Ahiale Akogo, Xavier-Lewis Palmer
Date: 2018-05-17 00:00:00
Tasks: BIG-bench Machine Learning

We developed a Convolutional Neural Network model to identify and classify
Airbrushed (alternatively known as Blow-spun), Electrospun and Steel Wire
scaffolds. Our model ScaffoldNet is a 6-layer Convolutional Neural Network
trained and tested on 3,043 images of Airbrushed, Electrospun and Steel Wire
scaffolds. The model takes in as input an imaged scaffold and then outputs the
scaffold type (Airbrushed, Electrospun or Steel Wire) as predicted
probabilities for the 3 classes. Our model scored a 99.44% Accuracy,
demonstrating potential for adaptation to investigating and solving complex
machine learning problems aimed at abstract spatial contexts, or in screening
complex, biological, fibrous structures seen in cortical bone and fibrous
shells.
Supervised classification of Dermatological diseases by Deep learning
Authors: Sourav Mishra, Toshihiko Yamasaki, Hideaki Imaizumi
Date: 2018-02-11 00:00:00
Tasks: Classification, Deep Learning, General Classification

This paper introduces a deep-learning based efficient classifier for common
dermatological conditions, aimed at people without easy access to skin
specialists. We report approximately 80% accuracy, in a situation where primary
care doctors have attained 57% success rate, according to recent literature.
The rationale of its design is centered on deploying and updating it on
handheld devices in near future. Dermatological diseases are common in every
population and have a wide spectrum in severity. With a shortage of
dermatological expertise being observed in several countries, machine learning
solutions can augment medical services and advise regarding existence of common
diseases. The paper implements supervised classification of nine distinct
conditions which have high occurrence in East Asian countries. Our current
attempt establishes that deep learning based techniques are viable avenues for
preliminary information to aid patients.
Auxiliary Tasks in Multi-task Learning
Authors: Lukas Liebel, Marco Körner
Date: 2018-05-16 00:00:00
Tasks: Depth Estimation, Multi-Task Learning, road scene understanding, Scene Understanding, Semantic Segmentation

Multi-task convolutional neural networks (CNNs) have shown impressive results
for certain combinations of tasks, such as single-image depth estimation (SIDE)
and semantic segmentation. This is achieved by pushing the network towards
learning a robust representation that generalizes well to different atomic
tasks. We extend this concept by adding auxiliary tasks, which are of minor
relevance for the application, to the set of learned tasks. As a kind of
additional regularization, they are expected to boost the performance of the
ultimately desired main tasks. To study the proposed approach, we picked
vision-based road scene understanding (RSU) as an exemplary application. Since
multi-task learning requires specialized datasets, particularly when using
extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU,
called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21
different labels, were acquired from the video game Grand Theft Auto V (GTA V).
Our proposed deep multi-task CNN architecture was trained on various
combination of tasks using synMT. The experiments confirmed that auxiliary
tasks can indeed boost network performance, both in terms of final results and
training time.