pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Represent Mechanics via Long-term Extrapolation and Interpolation
Authors: Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra
Date: 2017-06-06 00:00:00

While the basic laws of Newtonian mechanics are well understood, explaining a
physical scenario still requires manually modeling the problem with suitable
equations and associated parameters. In order to adopt such models for
artificial intelligence, researchers have handcrafted the relevant states, and
then used neural networks to learn the state transitions using simulation runs
as training data. Unfortunately, such approaches can be unsuitable for modeling
complex real-world scenarios, where manually authoring relevant state spaces
tend to be challenging. In this work, we investigate if neural networks can
implicitly learn physical states of real-world mechanical processes only based
on visual data, and thus enable long-term physical extrapolation. We develop a
recurrent neural network architecture for this task and also characterize
resultant uncertainties in the form of evolving variance estimates. We evaluate
our setup to extrapolate motion of a rolling ball on bowl of varying shape and
orientation using only images as input, and report competitive results with
approaches that assume access to internal physics models and parameters.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
Authors: Eirikur Agustsson, Fabian Mentzer, Michael Tschannen, Lukas Cavigelli, Radu Timofte, Luca Benini, Luc van Gool
Date: 2017-04-03 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Image Compression, Neural Network Compression, Quantization

We present a new approach to learn compressible representations in deep
architectures with an end-to-end training strategy. Our method is based on a
soft (continuous) relaxation of quantization and entropy, which we anneal to
their discrete counterparts throughout training. We showcase this method for
two challenging applications: Image compression and neural network compression.
While these tasks have typically been approached with different methods, our
soft-to-hard quantization approach gives results competitive with the
state-of-the-art for both.
Multi-Task Learning with Labeled and Unlabeled Tasks
Authors: Anastasia Pentina, Christoph H. Lampert
Date: 2016-02-21 00:00:00
Proceeding: ICML 2017 8
Tasks: Multi-Task Learning

In multi-task learning, a learner is given a collection of prediction tasks
and needs to solve all of them. In contrast to previous work, which required
that annotated training data is available for all tasks, we consider a new
setting, in which for some tasks, potentially most of them, only unlabeled
training data is provided. Consequently, to solve all tasks, information must
be transferred between tasks with labels and tasks without labels. Focusing on
an instance-based transfer method we analyze two variants of this setting: when
the set of labeled tasks is fixed, and when it can be actively selected by the
learner. We state and prove a generalization bound that covers both scenarios
and derive from it an algorithm for making the choice of labeled tasks (in the
active case) and for transferring information between the tasks in a principled
way. We also illustrate the effectiveness of the algorithm by experiments on
synthetic and real data.
Forward Thinking: Building and Training Neural Networks One Layer at a Time
Authors: Chris Hettinger, Tanner Christensen, Ben Ehlert, Jeffrey Humpherys, Tyler Jarvis, Sean Wade
Date: 2017-06-08 00:00:00

We present a general framework for training deep neural networks without
backpropagation. This substantially decreases training time and also allows for
construction of deep networks with many sorts of learners, including networks
whose layers are defined by functions that are not easily differentiated, like
decision trees. The main idea is that layers can be trained one at a time, and
once they are trained, the input data are mapped forward through the layer to
create a new learning problem. The process is repeated, transforming the data
through multiple layers, one at a time, rendering a new data set, which is
expected to be better behaved, and on which a final output layer can achieve
good performance. We call this forward thinking and demonstrate a proof of
concept by achieving state-of-the-art accuracy on the MNIST dataset for
convolutional neural networks. We also provide a general mathematical
formulation of forward thinking that allows for other types of deep learning
problems to be considered.
Distribution-Free One-Pass Learning
Authors: Peng Zhao, Zhi-Hua Zhou
Date: 2017-06-08 00:00:00

In many large-scale machine learning applications, data are accumulated with
time, and thus, an appropriate model should be able to update in an online
paradigm. Moreover, as the whole data volume is unknown when constructing the
model, it is desired to scan each data item only once with a storage
independent with the data volume. It is also noteworthy that the distribution
underlying may change during the data accumulation procedure. To handle such
tasks, in this paper we propose DFOP, a distribution-free one-pass learning
approach. This approach works well when distribution change occurs during data
accumulation, without requiring prior knowledge about the change. Every data
item can be discarded once it has been scanned. Besides, theoretical guarantee
shows that the estimate error, under a mild assumption, decreases until
convergence with high probability. The performance of DFOP for both regression
and classification are validated in experiments.
Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation
Authors: Raphael Shu, Hideki Nakayama
Date: 2017-04-11 00:00:00
Tasks: GPU, Machine Translation, Reranking, Translation

For extended periods of time, sequence generation models rely on beam search
algorithm to generate output sequence. However, the correctness of beam search
degrades when the a model is over-confident about a suboptimal prediction. In
this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some
extra steps at a later stage. In order to speed up MBR decoding, we compute the
Bayes risks on GPU in batch mode. In our experiments, we found that MBR
reranking works with a large beam size. Later-stage MBR decoding is shown to
outperform simple MBR reranking in machine translation tasks.
An Empirical Study of Adequate Vision Span for Attention-Based Neural Machine Translation
Authors: Raphael Shu, Hideki Nakayama
Date: 2016-12-19 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, Translation

Recently, the attention mechanism plays a key role to achieve high
performance for Neural Machine Translation models. However, as it computes a
score function for the encoder states in all positions at each decoding step,
the attention model greatly increases the computational complexity. In this
paper, we investigate the adequate vision span of attention models in the
context of machine translation, by proposing a novel attention framework that
is capable of reducing redundant score computation dynamically. The term
"vision span" means a window of the encoder states considered by the attention
model in one step. In our experiments, we found that the average window size of
vision span can be reduced by over 50% with modest loss in accuracy on
English-Japanese and German-English translation tasks.% This results indicate
that the conventional attention mechanism performs a significant amount of
redundant computation.
Regular Boardgames
Authors: Jakub Kowalski, Maksymilian Mika, Jakub Sutowicz, Marek Szykuła
Date: 2017-06-08 00:00:00
Tasks: Board Games

We propose a new General Game Playing (GGP) language called Regular
Boardgames (RBG), which is based on the theory of regular languages. The
objective of RBG is to join key properties as expressiveness, efficiency, and
naturalness of the description in one GGP formalism, compensating certain
drawbacks of the existing languages. This often makes RBG more suitable for
various research and practical developments in GGP. While dedicated mostly for
describing board games, RBG is universal for the class of all finite
deterministic turn-based games with perfect information. We establish
foundations of RBG, and analyze it theoretically and experimentally, focusing
on the efficiency of reasoning. Regular Boardgames is the first GGP language
that allows efficient encoding and playing games with complex rules and with
large branching factor (e.g.\ amazons, arimaa, large chess variants, go,
international checkers, paper soccer).
CAD2RL: Real Single-Image Flight without a Single Real Image
Authors: Fereshteh Sadeghi, Sergey Levine
Date: 2016-11-13 00:00:00
Tasks: 3D geometry, Collision Avoidance, Deep Reinforcement Learning, Depth Estimation, Depth Prediction, Motion Planning, Reinforcement Learning

Deep reinforcement learning has emerged as a promising and powerful technique
for automatically acquiring control policies that can process raw sensory
inputs, such as images, and perform complex behaviors. However, extending deep
RL to real-world robotic tasks has proven challenging, particularly in
safety-critical domains such as autonomous flight, where a trial-and-error
learning process is often impractical. In this paper, we explore the following
question: can we train vision-based navigation policies entirely in simulation,
and then transfer them into the real world to achieve real-world flight without
a single real training image? We propose a learning method that we call
CAD$^2$RL, which can be used to perform collision-free indoor flight in the
real world while being trained entirely on 3D CAD models. Our method uses
single RGB images from a monocular camera, without needing to explicitly
reconstruct the 3D geometry of the environment or perform explicit motion
planning. Our learned collision avoidance policy is represented by a deep
convolutional neural network that directly processes raw monocular images and
outputs velocity commands. This policy is trained entirely on simulated images,
with a Monte Carlo policy evaluation algorithm that directly optimizes the
network's ability to produce collision-free flight. By highly randomizing the
rendering settings for our simulated training set, we show that we can train a
policy that generalizes to the real world, without requiring the simulator to
be particularly realistic or high-fidelity. We evaluate our method by flying a
real quadrotor through indoor environments, and further evaluate the design
choices in our simulator through a series of ablation studies on depth
prediction. For supplementary video see: https://youtu.be/nXBWmzFrj5s
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
Authors: Shuming Ma, Xu sun, Jingjing Xu, Houfeng Wang, Wenjie Li, Qi Su
Date: 2017-06-08 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Semantic Similarity, Semantic Textual Similarity, Text Summarization

Current Chinese social media text summarization models are based on an
encoder-decoder framework. Although its generated summaries are similar to
source texts literally, they have low semantic relevance. In this work, our
goal is to improve semantic relevance between source texts and summaries for
Chinese social media summarization. We introduce a Semantic Relevance Based
neural model to encourage high semantic similarity between texts and summaries.
In our model, the source text is represented by a gated attention encoder,
while the summary representation is produced by a decoder. Besides, the
similarity score between the representations is maximized during training. Our
experiments show that the proposed model outperforms baseline systems on a
social media corpus.
Graph Fourier Transform with Negative Edges for Depth Image Coding
Authors: Weng-Tai Su, Gene Cheung, Chia-Wen Lin
Date: 2017-02-10 00:00:00

Recent advent in graph signal processing (GSP) has led to the development of
new graph-based transforms and wavelets for image / video coding, where the
underlying graph describes inter-pixel correlations. In this paper, we develop
a new transform called signed graph Fourier transform (SGFT), where the
underlying graph G contains negative edges that describe anti-correlations
between pixel pairs. Specifically, we first construct a one-state Markov
process that models both inter-pixel correlations and anti-correlations. We
then derive the corresponding precision matrix, and show that the loopy graph
Laplacian matrix Q of a graph G with a negative edge and two self-loops at its
end nodes is approximately equivalent. This proves that the eigenvectors of Q -
called SGFT - approximates the optimal Karhunen-Lo`eve Transform (KLT). We show
the importance of the self-loops in G to ensure Q is positive semi-definite. We
prove that the first eigenvector of Q is piecewise constant (PWC), and thus can
well approximate a piecewise smooth (PWS) signal like a depth image.
Experimental results show that a block-based coding scheme based on SGFT
outperforms a previous scheme using graph transforms with only positive edges
for several depth images.
Luck is Hard to Beat: The Difficulty of Sports Prediction
Authors: Raquel YS Aoki, Renato M Assuncao, Pedro OS Vaz de Melo
Date: 2017-06-08 00:00:00

Predicting the outcome of sports events is a hard task. We quantify this
difficulty with a coefficient that measures the distance between the observed
final results of sports leagues and idealized perfectly balanced competitions
in terms of skill. This indicates the relative presence of luck and skill. We
collected and analyzed all games from 198 sports leagues comprising 1503
seasons from 84 countries of 4 different sports: basketball, soccer, volleyball
and handball. We measured the competitiveness by countries and sports. We also
identify in each season which teams, if removed from its league, result in a
completely random tournament. Surprisingly, not many of them are needed. As
another contribution of this paper, we propose a probabilistic graphical model
to learn about the teams' skills and to decompose the relative weights of luck
and skill in each game. We break down the skill component into factors
associated with the teams' characteristics. The model also allows to estimate
as 0.36 the probability that an underdog team wins in the NBA league, with a
home advantage adding 0.09 to this probability. As shown in the first part of
the paper, luck is substantially present even in the most competitive
championships, which partially explains why sophisticated and complex
feature-based models hardly beat simple models in the task of forecasting
sports' outcomes.
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
Authors: Jungsik Hwang, Jinhyung Kim, Ahmadreza Ahmadi, Minkyu Choi, Jun Tani
Date: 2017-06-08 00:00:00
Tasks: Action Generation

This study presents a dynamic neural network model based on the predictive
coding framework for perceiving and predicting the dynamic visuo-proprioceptive
patterns. In our previous study [1], we have shown that the deep dynamic neural
network model was able to coordinate visual perception and action generation in
a seamless manner. In the current study, we extended the previous model under
the predictive coding framework to endow the model with a capability of
perceiving and predicting dynamic visuo-proprioceptive patterns as well as a
capability of inferring intention behind the perceived visuomotor information
through minimizing prediction error. A set of synthetic experiments were
conducted in which a robot learned to imitate the gestures of another robot in
a simulation environment. The experimental results showed that with given
intention states, the model was able to mentally simulate the possible incoming
dynamic visuo-proprioceptive patterns in a top-down process without the inputs
from the external environment. Moreover, the results highlighted the role of
minimizing prediction error in inferring underlying intention of the perceived
visuo-proprioceptive patterns, supporting the predictive coding account of the
mirror neuron systems. The results also revealed that minimizing prediction
error in one modality induced the recall of the corresponding representation of
another modality acquired during the consolidative learning of raw-level
visuo-proprioceptive patterns.
Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings
Authors: Vindula Jayawardana, Dimuthu Lakmal, Nisansa de Silva, Amal Shehan Perera, Keet Sugathadasa, Buddhi Ayesha
Date: 2017-06-08 00:00:00

Selecting a representative vector for a set of vectors is a very common
requirement in many algorithmic tasks. Traditionally, the mean or median vector
is selected. Ontology classes are sets of homogeneous instance objects that can
be converted to a vector space by word vector embeddings. This study proposes a
methodology to derive a representative vector for ontology classes whose
instances were converted to the vector space. We start by deriving five
candidate vectors which are then used to train a machine learning model that
would calculate a representative vector for the class. We show that our
methodology out-performs the traditional mean and median vector
representations.
Automatic tracking of vessel-like structures from a single starting point
Authors: Dario Augusto Borges Oliveira, Laura Leal-Taixe, Raul Queiroz Feitosa, Bodo Rosenhahn
Date: 2017-06-08 00:00:00
Tasks: Medical Image Analysis

The identification of vascular networks is an important topic in the medical
image analysis community. While most methods focus on single vessel tracking,
the few solutions that exist for tracking complete vascular networks are
usually computationally intensive and require a lot of user interaction. In
this paper we present a method to track full vascular networks iteratively
using a single starting point. Our approach is based on a cloud of sampling
points distributed over concentric spherical layers. We also proposed a vessel
model and a metric of how well a sample point fits this model. Then, we
implement the network tracking as a min-cost flow problem, and propose a novel
optimization scheme to iteratively track the vessel structure by inherently
handling bifurcations and paths. The method was tested using both synthetic and
real images. On the 9 different data-sets of synthetic blood vessels, we
achieved maximum accuracies of more than 98\%. We further use the synthetic
data-set to analyse the sensibility of our method to parameter setting, showing
the robustness of the proposed algorithm. For real images, we used coronary,
carotid and pulmonary data to segment vascular structures and present the
visual results. Still for real images, we present numerical and visual results
for networks of nerve fibers in the olfactory system. Further visual results
also show the potential of our approach for identifying vascular networks
topologies. The presented method delivers good results for the several
different datasets tested and have potential for segmenting vessel-like
structures. Also, the topology information, inherently extracted, can be used
for further analysis to computed aided diagnosis and surgical planning.
Finally, the method's modular aspect holds potential for problem-oriented
adjustments and improvements.
Image Captioning with Object Detection and Localization
Authors: Zhongliang Yang, Yu-Jin Zhang, Sadaqat ur Rehman, Yongfeng Huang
Date: 2017-06-08 00:00:00
Tasks: Image Captioning, Object, object-detection, Object Detection

Automatically generating a natural language description of an image is a task
close to the heart of image understanding. In this paper, we present a
multi-model neural network method closely related to the human visual system
that automatically learns to describe the content of images. Our model consists
of two sub-models: an object detection and localization model, which extract
the information of objects and their spatial relationship in images
respectively; Besides, a deep recurrent neural network (RNN) based on long
short-term memory (LSTM) units with attention mechanism for sentences
generation. Each word of the description will be automatically aligned to
different objects of the input image when it is generated. This is similar to
the attention mechanism of the human visual system. Experimental results on the
COCO dataset showcase the merit of the proposed method, which outperforms
previous benchmark models.
Content-Based Table Retrieval for Web Queries
Authors: Zhao Yan, Duyu Tang, Nan Duan, Junwei Bao, Yuanhua Lv, Ming Zhou, Zhoujun Li
Date: 2017-06-08 00:00:00
Tasks: Retrieval, Table Retrieval

Understanding the connections between unstructured text and semi-structured
table is an important yet neglected problem in natural language processing. In
this work, we focus on content-based table retrieval. Given a query, the task
is to find the most relevant table from a collection of tables. Further
progress towards improving this area requires powerful models of semantic
matching and richer training and evaluation resources. To remedy this, we
present a ranking based approach, and implement both carefully designed
features and neural network architectures to measure the relevance between a
query and the content of a table. Furthermore, we release an open-domain
dataset that includes 21,113 web queries for 273,816 tables. We conduct
comprehensive experiments on both real world and synthetic datasets. Results
verify the effectiveness of our approach and present the challenges for this
task.
C-arm Tomographic Imaging Technique for Nephrolithiasis and Detection of Kidney Stones
Authors: Nuhad A. Malalla, Ying Chen
Date: 2017-06-08 00:00:00

In this paper, we investigated a C-arm tomographic technique as a new three
dimensional (3D) kidney imaging method for nephrolithiasis and kidney stone
detection over view angle less than 180o. Our C-arm tomographic technique
provides a series of two dimensional (2D) images with a single scan over 40o
view angle. Experimental studies were performed with a kidney phantom that was
formed from a pig kidney with two embedded kidney stones. Different
reconstruction methods were developed for C-arm tomographic technique to
generate 3D kidney information including: point by point back projection (BP),
filtered back projection (FBP), simultaneous algebraic reconstruction technique
(SART) and maximum likelihood expectation maximization (MLEM). Computer
simulation study was also done with simulated 3D spherical object to evaluate
the reconstruction results. Preliminary results demonstrated the capability of
our C-arm tomographic technique to generate 3D kidney information for kidney
stone detection with low exposure of radiation. The kidney stones are visible
on reconstructed planes with identifiable shapes and sizes.