pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Dynamics of Learning Restricted Boltzmann Machines
Authors: Aurélien Decelle, Giancarlo Fissore, Cyril Furtlehner
Date: 2017-08-09 00:00:00

The Restricted Boltzmann Machine (RBM), an important tool used in machine
learning in particular for unsupervized learning tasks, is investigated from
the perspective of its spectral properties. Starting from empirical
observations, we propose a generic statistical ensemble for the weight matrix
of the RBM and characterize its mean evolution. This let us show how in the
linear regime, in which the RBM is found to operate at the beginning of the
training, the statistical properties of the data drive the selection of the
unstable modes of the weight matrix. A set of equations characterizing the
non-linear regime is then derived, unveiling in some way how the selected modes
interact in later stages of the learning procedure and defining a deterministic
learning curve for the RBM.
Who wins the Miss Contest for Imputation Methods? Our Vote for Miss BooPF
Authors: Burim Ramosaj, Markus Pauly
Date: 2017-11-30 00:00:00
Tasks: Imputation

Missing data is an expected issue when large amounts of data is collected,
and several imputation techniques have been proposed to tackle this problem.
Beneath classical approaches such as MICE, the application of Machine Learning
techniques is tempting. Here, the recently proposed missForest imputation
method has shown high imputation accuracy under the Missing (Completely) at
Random scheme with various missing rates. In its core, it is based on a random
forest for classification and regression, respectively. In this paper we study
whether this approach can even be enhanced by other methods such as the
stochastic gradient tree boosting method, the C5.0 algorithm or modified random
forest procedures. In particular, other resampling strategies within the random
forest protocol are suggested. In an extensive simulation study, we analyze
their performances for continuous, categorical as well as mixed-type data.
Therein, MissBooPF, a combination of the stochastic gradient tree boosting
method together with the parametrically bootstrapped random forest method,
appeared to be promising. Finally, an empirical analysis focusing on credit
information and Facebook data is conducted.
Learning to Learn from Weak Supervision by Full Supervision
Authors: Mostafa Dehghani, Aliaksei Severyn, Sascha Rothe, Jaap Kamps
Date: 2017-11-30 00:00:00

In this paper, we propose a method for training neural networks when we have
a large set of data with weak labels and a small amount of data with true
labels. In our proposed model, we train two neural networks: a target network,
the learner and a confidence network, the meta-learner. The target network is
optimized to perform a given task and is trained using a large set of unlabeled
data that are weakly annotated. We propose to control the magnitude of the
gradient updates to the target network using the scores provided by the second
confidence network, which is trained on a small amount of supervised data. Thus
we avoid that the weight updates computed from noisy labels harm the quality of
the target network model.
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm
Authors: Tongyi Cao, Akshay Krishnamurthy
Date: 2017-11-21 00:00:00

We design new algorithms for the combinatorial pure exploration problem in the multi-arm bandit framework. In this problem, we are given $K$ distributions and a collection of subsets $\mathcal{V} \subset 2^{[K]}$ of these distributions, and we would like to find the subset $v \in \mathcal{V}$ that has largest mean, while collecting, in a sequential fashion, as few samples from the distributions as possible. In both the fixed budget and fixed confidence settings, our algorithms achieve new sample-complexity bounds that provide polynomial improvements on previous results in some settings. Via an information-theoretic lower bound, we show that no approach based on uniform sampling can improve on ours in any regime, yielding the first interactive algorithms for this problem with this basic property. Computationally, we show how to efficiently implement our fixed confidence algorithm whenever $\mathcal{V}$ supports efficient linear optimization. Our results involve precise concentration-of-measure arguments and a new algorithm for linear programming with exponentially many constraints.
High Dynamic Range Imaging Technology
Authors: Alessandro Artusi, Thomas Richter, Touradj Ebrahimi, Rafal K. Mantiuk
Date: 2017-11-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this lecture note, we describe high dynamic range (HDR) imaging systems;
such systems are able to represent luminances of much larger brightness and,
typically, also a larger range of colors than conventional standard dynamic
range (SDR) imaging systems. The larger luminance range greatly improve the
overall quality of visual content, making it appears much more realistic and
appealing to observers. HDR is one of the key technologies of the future
imaging pipeline, which will change the way the digital visual content is
represented and manipulated today.
Dense scale selection over space, time and space-time
Authors: Tony Lindeberg
Date: 2017-09-25 00:00:00

Scale selection methods based on local extrema over scale of scale-normalized
derivatives have been primarily developed to be applied sparsely --- at image
points where the magnitude of a scale-normalized differential expression
additionally assumes local extrema over the domain where the data are defined.
This paper presents a methodology for performing dense scale selection, so that
hypotheses about local characteristic scales in images, temporal signals and
video can be computed at every image point and every time moment.
  A critical problem when designing mechanisms for dense scale selection is
that the scale at which scale-normalized differential entities assume local
extrema over scale can be strongly dependent on the local order of the locally
dominant differential structure. To address this problem, we propose a
methodology where local extrema over scale are detected of a quasi quadrature
measure involving scale-space derivatives up to order two and propose two
independent mechanisms to reduce the phase dependency of the local scale
estimates by: (i) introducing a second layer of post-smoothing prior to the
detection of local extrema over scale and (ii) performing local phase
compensation based on a model of the phase dependency of the local scale
estimates depending on the relative strengths between first- vs. second-order
differential structure.
  This general methodology is applied over three types of domains: (i) spatial
images, (ii) temporal signals and (iii) spatio-temporal video. Experiments show
that the proposed methodology leads to intuitively reasonable results with
local scale estimates that reflect variations in the characteristic scales of
locally dominant structures over space and time.
Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding
Authors: Bing Liu, Ian Lane
Date: 2017-11-30 00:00:00
Tasks: slot-filling, Slot Filling, Spoken Language Understanding

The goal of this paper is to learn cross-domain representations for slot
filling task in spoken language understanding (SLU). Most of the recently
published SLU models are domain-specific ones that work on individual task
domains. Annotating data for each individual task domain is both financially
costly and non-scalable. In this work, we propose an adversarial training
method in learning common features and representations that can be shared
across multiple domains. Model that produces such shared representations can be
combined with models trained on individual domain SLU data to reduce the amount
of training samples required for developing a new domain. In our experiments
using data sets from multiple domains, we show that adversarial training helps
in learning better domain-general SLU models, leading to improved slot filling
F1 scores. We further show that applying adversarial learning on domain-general
model also helps in achieving higher slot filling performance when the model is
jointly optimized with domain-specific models.
Non-Asymptotic Analysis of Robust Control from Coarse-Grained Identification
Authors: Stephen Tu, Ross Boczar, Andrew Packard, Benjamin Recht
Date: 2017-07-15 00:00:00

This work explores the trade-off between the number of samples required to
accurately build models of dynamical systems and the degradation of performance
in various control objectives due to a coarse approximation. In particular, we
show that simple models can be easily fit from input/output data and are
sufficient for achieving various control objectives. We derive bounds on the
number of noisy input/output samples from a stable linear time-invariant system
that are sufficient to guarantee that the corresponding finite impulse response
approximation is close to the true system in the $\mathcal{H}_\infty$-norm. We
demonstrate that these demands are lower than those derived in prior art which
aimed to accurately identify dynamical models. We also explore how different
physical input constraints, such as power constraints, affect the sample
complexity. Finally, we show how our analysis fits within the established
framework of robust control, by demonstrating how a controller designed for an
approximate system provably meets performance objectives on the true system.
Towards Accurate Binary Convolutional Neural Network
Authors: Xiaofan Lin, Cong Zhao, Wei Pan
Date: 2017-11-30 00:00:00
Proceeding: NeurIPS 2017 12

We introduce a novel scheme to train binary convolutional neural networks
(CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
It has been known that using binary weights and activations drastically reduce
memory size and accesses, and can replace arithmetic operations with more
efficient bitwise operations, leading to much faster test-time inference and
lower power consumption. However, previous works on binarizing CNNs usually
result in severe prediction accuracy degradation. In this paper, we address
this issue with two major innovations: (1) approximating full-precision weights
with the linear combination of multiple binary weight bases; (2) employing
multiple binary activations to alleviate information loss. The implementation
of the resulting binary CNN, denoted as ABC-Net, is shown to achieve much
closer performance to its full-precision counterpart, and even reach the
comparable prediction accuracy on ImageNet and forest trail datasets, given
adequate binary weight bases and activations.
Learning to Compose Skills
Authors: Himanshu Sahni, Saurabh Kumar, Farhan Tejani, Charles Isbell
Date: 2017-11-30 00:00:00

We present a differentiable framework capable of learning a wide variety of
compositions of simple policies that we call skills. By recursively composing
skills with themselves, we can create hierarchies that display complex
behavior. Skill networks are trained to generate skill-state embeddings that
are provided as inputs to a trainable composition function, which in turn
outputs a policy for the overall task. Our experiments on an environment
consisting of multiple collect and evade tasks show that this architecture is
able to quickly build complex skills from simpler ones. Furthermore, the
learned composition function displays some transfer to unseen combinations of
skills, allowing for zero-shot generalizations.
Analysis of Italian Word Embeddings
Authors: Rocco Tripodi, Stefano Li Pira
Date: 2017-07-27 00:00:00
Tasks: Word Embeddings

In this work we analyze the performances of two of the most used word
embeddings algorithms, skip-gram and continuous bag of words on Italian
language. These algorithms have many hyper-parameter that have to be carefully
tuned in order to obtain accurate word representation in vectorial space. We
provide an accurate analysis and an evaluation, showing what are the best
configuration of parameters for specific tasks.
Hierarchical Policy Search via Return-Weighted Density Estimation
Authors: Takayuki Osa, Masashi Sugiyama
Date: 2017-11-28 00:00:00
Tasks: Density Estimation, Motion Planning, Reinforcement Learning, Reinforcement Learning (RL)

Learning an optimal policy from a multi-modal reward function is a
challenging problem in reinforcement learning (RL). Hierarchical RL (HRL)
tackles this problem by learning a hierarchical policy, where multiple option
policies are in charge of different strategies corresponding to modes of a
reward function and a gating policy selects the best option for a given
context. Although HRL has been demonstrated to be promising, current
state-of-the-art methods cannot still perform well in complex real-world
problems due to the difficulty of identifying modes of the reward function. In
this paper, we propose a novel method called hierarchical policy search via
return-weighted density estimation (HPSDE), which can efficiently identify the
modes through density estimation with return-weighted importance sampling. Our
proposed method finds option policies corresponding to the modes of the return
function and automatically determines the number and the location of option
policies, which significantly reduces the burden of hyper-parameters tuning.
Through experiments, we demonstrate that the proposed HPSDE successfully learns
option policies corresponding to modes of the return function and that it can
be successfully applied to a challenging motion planning problem of a redundant
robotic manipulator.
A novel graph structure for salient object detection based on divergence background and compact foreground
Authors: Chenxing Xia, Hanling Zhang, Keqin Li
Date: 2017-11-30 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

In this paper, we propose an efficient and discriminative model for salient
object detection. Our method is carried out in a stepwise mechanism based on
both divergence background and compact foreground cues. In order to effectively
enhance the distinction between nodes along object boundaries and the
similarity among object regions, a graph is constructed by introducing the
concept of virtual node. To remove incorrect outputs, a scheme for selecting
background seeds and a method for generating compactness foreground regions are
introduced, respectively. Different from prior methods, we calculate the
saliency value of each node based on the relationship between the corresponding
node and the virtual node. In order to achieve significant performance
improvement consistently, we propose an Extended Manifold Ranking (EMR)
algorithm, which subtly combines suppressed / active nodes and mid-level
information. Extensive experimental results demonstrate that the proposed
algorithm performs favorably against the state-of-art saliency detection
methods in terms of different evaluation metrics on several benchmark datasets.
Enabling Embodied Analogies in Intelligent Music Systems
Authors: Fabio Paolizzo
Date: 2017-11-30 00:00:00
Tasks: Audio Signal Processing, BIG-bench Machine Learning

The present methodology is aimed at cross-modal machine learning and uses
multidisciplinary tools and methods drawn from a broad range of areas and
disciplines, including music, systematic musicology, dance, motion capture,
human-computer interaction, computational linguistics and audio signal
processing. Main tasks include: (1) adapting wisdom-of-the-crowd approaches to
embodiment in music and dance performance to create a dataset of music and
music lyrics that covers a variety of emotions, (2) applying
audio/language-informed machine learning techniques to that dataset to identify
automatically the emotional content of the music and the lyrics, and (3)
integrating motion capture data from a Vicon system and dancers performing on
that music.
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections
Authors: David Isele, Akansel Cosgun
Date: 2017-11-30 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

We view intersection handling on autonomous vehicles as a reinforcement
learning problem, and study its behavior in a transfer learning setting. We
show that a network trained on one type of intersection generally is not able
to generalize to other intersections. However, a network that is pre-trained on
one intersection and fine-tuned on another performs better on the new task
compared to training in isolation. This network also retains knowledge of the
prior task, even though some forgetting occurs. Finally, we show that the
benefits of fine-tuning hold when transferring simulated intersection handling
knowledge to a real autonomous vehicle.
Improving Latent User Models in Online Social Media
Authors: Adit Krishnan, ASHISH SHARMA, Hari Sundaram
Date: 2017-11-30 00:00:00
Tasks: Avg

Modern social platforms are characterized by the presence of rich
user-behavior data associated with the publication, sharing and consumption of
textual content. Users interact with content and with each other in a complex
and dynamic social environment while simultaneously evolving over time. In
order to effectively characterize users and predict their future behavior in
such a setting, it is necessary to overcome several challenges. Content
heterogeneity and temporal inconsistency of behavior data result in severe
sparsity at the user level. In this paper, we propose a novel
mutual-enhancement framework to simultaneously partition and learn latent
activity profiles of users. We propose a flexible user partitioning approach to
effectively discover rare behaviors and tackle user-level sparsity. We
extensively evaluate the proposed framework on massive datasets from real-world
platforms including Q&A networks and interactive online courses (MOOCs). Our
results indicate significant gains over state-of-the-art behavior models ( 15%
avg ) in a varied range of tasks and our gains are further magnified for users
with limited interaction data. The proposed algorithms are amenable to
parallelization, scale linearly in the size of datasets, and provide
flexibility to model diverse facets of user behavior.
ArbiText: Arbitrary-Oriented Text Detection in Unconstrained Scene
Authors: Daitao Xing, Zichen Li, Xin Chen, Yi Fang
Date: 2017-11-30 00:00:00
Tasks: Text Detection

Arbitrary-oriented text detection in the wild is a very challenging task, due
to the aspect ratio, scale, orientation, and illumination variations. In this
paper, we propose a novel method, namely Arbitrary-oriented Text (or ArbText
for short) detector, for efficient text detection in unconstrained natural
scene images. Specifically, we first adopt the circle anchors rather than the
rectangular ones to represent bounding boxes, which is more robust to
orientation variations. Subsequently, we incorporate a pyramid pooling module
into the Single Shot MultiBox Detector framework, in order to simultaneously
explore the local and global visual information, which can, therefore, generate
more confidential detection results. Experiments on established scene-text
datasets, such as the ICDAR 2015 and MSRA-TD500 datasets, have demonstrated the
supe rior performance of the proposed method, compared to the state-of-the-art
approaches.
Quantum Neuron: an elementary building block for machine learning on quantum computers
Authors: Yudong Cao, Gian Giacomo Guerreschi, Alán Aspuru-Guzik
Date: 2017-11-30 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

Even the most sophisticated artificial neural networks are built by
aggregating substantially identical units called neurons. A neuron receives
multiple signals, internally combines them, and applies a non-linear function
to the resulting weighted sum. Several attempts to generalize neurons to the
quantum regime have been proposed, but all proposals collided with the
difficulty of implementing non-linear activation functions, which is essential
for classical neurons, due to the linear nature of quantum mechanics. Here we
propose a solution to this roadblock in the form of a small quantum circuit
that naturally simulates neurons with threshold activation. Our quantum circuit
defines a building block, the "quantum neuron", that can reproduce a variety of
classical neural network constructions while maintaining the ability to process
superpositions of inputs and preserve quantum coherence and entanglement. In
the construction of feedforward networks of quantum neurons, we provide
numerical evidence that the network not only can learn a function when trained
with superposition of inputs and the corresponding output, but that this
training suffices to learn the function on all individual inputs separately.
When arranged to mimic Hopfield networks, quantum neural networks exhibit
properties of associative memory. Patterns are encoded using the simple Hebbian
rule for the weights and we demonstrate attractor dynamics from corrupted
inputs. Finally, the fact that our quantum model closely captures (traditional)
neural network dynamics implies that the vast body of literature and results on
neural networks becomes directly relevant in the context of quantum machine
learning.
Knowledge Graph Embedding with Iterative Guidance from Soft Rules
Authors: Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo
Date: 2017-11-30 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of
current research. Combining such an embedding model with logic rules has
recently attracted increasing attention. Most previous attempts made a one-time
injection of logic rules, ignoring the interactive nature between embedding
learning and logical inference. And they focused only on hard rules, which
always hold with no exception and usually require extensive manual effort to
create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a
novel paradigm of KG embedding with iterative guidance from soft rules. RUGE
enables an embedding model to learn simultaneously from 1) labeled triples that
have been directly observed in a given KG, 2) unlabeled triples whose labels
are going to be predicted iteratively, and 3) soft rules with various
confidence levels extracted automatically from the KG. In the learning process,
RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and
integrates such newly labeled triples to update the embedding model. Through
this iterative procedure, knowledge embodied in logic rules may be better
transferred into the learned embeddings. We evaluate RUGE in link prediction on
Freebase and YAGO. Experimental results show that: 1) with rule knowledge
injected iteratively, RUGE achieves significant and consistent improvements
over state-of-the-art baselines; and 2) despite their uncertainties,
automatically extracted soft rules are highly beneficial to KG embedding, even
those with moderate confidence levels. The code and data used for this paper
can be obtained from https://github.com/iieir-km/RUGE.
Variational Deep Q Network
Authors: Yunhao Tang, Alp Kucukelbir
Date: 2017-11-30 00:00:00
Tasks: Efficient Exploration, Variational Inference

We propose a framework that directly tackles the probability distribution of
the value function parameters in Deep Q Network (DQN), with powerful
variational inference subroutines to approximate the posterior of the
parameters. We will establish the equivalence between our proposed surrogate
objective and variational inference loss. Our new algorithm achieves efficient
exploration and performs well on large scale chain Markov Decision Process
(MDP).