pwc-archive/papers-with-abstracts huggingface dataset:
Random Shuffling Beats SGD after Finite Epochs
Authors: Jeff Z. HaoChen, Suvrit Sra
Date: 2018-06-26 00:00:00

A long-standing problem in the theory of stochastic gradient descent (SGD) is to prove that its without-replacement version RandomShuffle converges faster than the usual with-replacement version. We present the first (to our knowledge) non-asymptotic solution to this problem, which shows that after a "reasonable" number of epochs RandomShuffle indeed converges faster than SGD. Specifically, we prove that under strong convexity and second-order smoothness, the sequence generated by RandomShuffle converges to the optimal solution at the rate O(1/T^2 + n^3/T^3), where n is the number of components in the objective, and T is the total number of iterations. This result shows that after a reasonable number of epochs RandomShuffle is strictly better than SGD (which converges as O(1/T)). The key step toward showing this better dependence on T is the introduction of n into the bound; and as our analysis will show, in general a dependence on n is unavoidable without further changes to the algorithm. We show that for sparse data RandomShuffle has the rate O(1/T^2), again strictly better than SGD. Furthermore, we discuss extensions to nonconvex gradient dominated functions, as well as non-strongly convex settings.
Learning Existing Social Conventions via Observationally Augmented Self-Play
Authors: Adam Lerer, Alexander Peysakhovich
Date: 2018-06-26 00:00:00
Tasks: Imitation Learning, Multi-agent Reinforcement Learning, Navigate, Reinforcement Learning

In order for artificial agents to coordinate effectively with people, they
must act consistently with existing conventions (e.g. how to navigate in
traffic, which language to speak, or how to coordinate with teammates). A
group's conventions can be viewed as a choice of equilibrium in a coordination
game. We consider the problem of an agent learning a policy for a coordination
game in a simulated environment and then using this policy when it enters an
existing group. When there are multiple possible conventions we show that
learning a policy via multi-agent reinforcement learning (MARL) is likely to
find policies which achieve high payoffs at training time but fail to
coordinate with the real group into which the agent enters. We assume access to
a small number of samples of behavior from the true convention and show that we
can augment the MARL objective to help it find policies consistent with the
real group's convention. In three environments from the literature - traffic,
communication, and team coordination - we observe that augmenting MARL with a
small amount of imitation learning greatly increases the probability that the
strategy found by MARL fits well with the existing social convention. We show
that this works even in an environment where standard training methods very
rarely find the true convention of the agent's partners.
Deep $k$-Means: Jointly clustering with $k$-Means and learning representations
Authors: Maziar Moradi Fard, Thibaut Thonet, Eric Gaussier
Date: 2018-06-26 00:00:00
Tasks: Clustering

We study in this paper the problem of jointly clustering and learning
representations. As several previous studies have shown, learning
representations that are both faithful to the data to be clustered and adapted
to the clustering algorithm can lead to better clustering performance, all the
more so that the two tasks are performed jointly. We propose here such an
approach for $k$-Means clustering based on a continuous reparametrization of
the objective function that leads to a truly joint solution. The behavior of
our approach is illustrated on various datasets showing its efficacy in
learning representations for objects while clustering them.
Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks
Authors: Leon René Sütfeld, Flemming Brieger, Holger Finger, Sonja Füllhase, Gordon Pipa
Date: 2018-06-26 00:00:00

The most widely used activation functions in current deep feed-forward neural
networks are rectified linear units (ReLU), and many alternatives have been
successfully applied, as well. However, none of the alternatives have managed
to consistently outperform the rest and there is no unified theory connecting
properties of the task and network with properties of activation functions for
most efficient training. A possible solution is to have the network learn its
preferred activation functions. In this work, we introduce Adaptive Blending
Units (ABUs), a trainable linear combination of a set of activation functions.
Since ABUs learn the shape, as well as the overall scaling of the activation
function, we also analyze the effects of adaptive scaling in common activation
functions. We experimentally demonstrate advantages of both adaptive scaling
and ABUs over common activation functions across a set of systematically varied
network specifications. We further show that adaptive scaling works by
mitigating covariate shifts during training, and that the observed advantages
in performance of ABUs likewise rely largely on the activation function's
ability to adapt over the course of training.
Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
Authors: Xiaoming Yu, Zhenqiang Ying, Thomas Li, Shan Liu, Ge Li
Date: 2018-06-26 00:00:00
Tasks: Diversity, Image-to-Image Translation, Translation

Recent advances in image-to-image translation have seen a rise in approaches generating diverse images through a single network. To indicate the target domain for a one-to-many mapping, the latent code is injected into the generator network. However, we found that the injection method leads to mode collapse because of normalization strategies. Existing normalization strategies might either cause the inconsistency of feature distribution or eliminate the effect of the latent code. To solve these problems, we propose the consistency within diversity criteria for designing the multi-mapping model. Based on the criteria, we propose central biasing normalization to inject the latent code information. Experiments show that our method can improve the quality and diversity of existing image-to-image translation models, such as StarGAN, BicycleGAN, and pix2pix.
Crowd Counting with Density Adaption Networks
Authors: Li Wang, Weiyuan Shao, Yao Lu, Hao Ye, Jian Pu, Yingbin Zheng
Date: 2018-06-26 00:00:00
Tasks: Crowd Counting, GPU

Crowd counting is one of the core tasks in various surveillance applications.
A practical system involves estimating accurate head counts in dynamic
scenarios under different lightning, camera perspective and occlusion states.
Previous approaches estimate head counts despite that they can vary
dramatically in different density settings; the crowd is often unevenly
distributed and the results are therefore unsatisfactory. In this paper, we
propose a lightweight deep learning framework that can automatically estimate
the crowd density level and adaptively choose between different counter
networks that are explicitly trained for different density domains. Experiments
on two recent crowd counting datasets, UCF_CC_50 and ShanghaiTech, show that
the proposed mechanism achieves promising improvements over state-of-the-art
methods. Moreover, runtime speed is 20 FPS on a single GPU.
Adversarial Active Exploration for Inverse Dynamics Model Learning
Authors: Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee
Date: 2018-06-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Imitation Learning, model, Reinforcement Learning

We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other. The former collects training samples for the latter, with an objective to maximize the error of the latter. The latter is trained with samples collected by the former, and generates rewards for the former when it fails to predict the actual action taken by the former. In such a competitive setting, the DRL agent learns to generate samples that the inverse dynamics model fails to predict correctly, while the inverse dynamics model learns to adapt to the challenging samples. We further propose a reward structure that ensures the DRL agent to collect only moderately hard samples but not overly hard ones that prevent the inverse model from predicting effectively. We evaluate the effectiveness of our method on several robotic arm and hand manipulation tasks against multiple baseline models. Experimental results show that our method is comparable to those directly trained with expert demonstrations, and superior to the other baselines even without any human priors.
Complexity Results for Preference Aggregation over (m)CP-nets: Pareto and Majority Voting
Authors: Thomas Lukasiewicz, Enrico Malizia
Date: 2018-06-26 00:00:00

Combinatorial preference aggregation has many applications in AI. Given the
exponential nature of these preferences, compact representations are needed and
($m$)CP-nets are among the most studied ones. Sequential and global voting are
two ways to aggregate preferences over CP-nets. In the former, preferences are
aggregated feature-by-feature. Hence, when preferences have specific feature
dependencies, sequential voting may exhibit voting paradoxes, i.e., it might
select sub-optimal outcomes. To avoid paradoxes in sequential voting, one has
often assumed the $\mathcal{O}$-legality restriction, which imposes a shared
topological order among all the CP-nets. On the contrary, in global voting,
CP-nets are considered as a whole during preference aggregation. For this
reason, global voting is immune from paradoxes, and there is no need to impose
restrictions over the CP-nets' topological structure. Sequential voting over
$\mathcal{O}$-legal CP-nets has extensively been investigated. On the other
hand, global voting over non-$\mathcal{O}$-legal CP-nets has not carefully been
analyzed, despite it was stated in the literature that a theoretical comparison
between global and sequential voting was promising and a precise complexity
analysis for global voting has been asked for multiple times. In quite few
works, very partial results on the complexity of global voting over CP-nets
have been given. We start to fill this gap by carrying out a thorough
complexity analysis of Pareto and majority global voting over not necessarily
$\mathcal{O}$-legal acyclic binary polynomially connected (m)CP-nets. We settle
these problems in the polynomial hierarchy, and some of them in PTIME or
LOGSPACE, whereas EXPTIME was the previously known upper bound for most of
them. We show various tight lower bounds and matching upper bounds for problems
that up to date did not have any explicit non-obvious lower bound.
The decoupled extended Kalman filter for dynamic exponential-family factorization models
Authors: Carlos Alberto Gomez-Uribe, Brian Karrer
Date: 2018-06-26 00:00:00
Tasks: Recommendation Systems

Motivated by the needs of online large-scale recommender systems, we specialize the decoupled extended Kalman filter (DEKF) to factorization models, including factorization machines, matrix and tensor factorization, and illustrate the effectiveness of the approach through numerical experiments on synthetic and on real-world data. Online learning of model parameters through the DEKF makes factorization models more broadly useful by (i) allowing for more flexible observations through the entire exponential family, (ii) modeling parameter drift, and (iii) producing parameter uncertainty estimates that can enable explore/exploit and other applications. We use a different parameter dynamics than the standard DEKF, allowing parameter drift while encouraging reasonable values. We also present an alternate derivation of the extended Kalman filter and DEKF that highlights the role of the Fisher information matrix in the EKF.
Independence of Sources in Social Networks
Authors: Manel Chehibi, Mouna Chebbah, Arnaud Martin
Date: 2018-06-26 00:00:00

Online social networks are more and more studied. The links between users of
a social network are important and have to be well qualified in order to detect
communities and find influencers for example. In this paper, we present an
approach based on the theory of belief functions to estimate the degrees of
cognitive independence between users in a social network. We experiment the
proposed method on a large amount of data gathered from the Twitter social
network.
A Constraint-based Encoding for Domain-Independent Temporal Planning
Authors: Arthur Bit-Monnot
Date: 2018-06-26 00:00:00
Tasks: Scheduling, Task Planning

We present a general constraint-based encoding for domain-independent task
planning. Task planning is characterized by causal relationships expressed as
conditions and effects of optional actions. Possible actions are typically
represented by templates, where each template can be instantiated into a number
of primitive actions. While most previous work for domain-independent task
planning has focused on primitive actions in a state-oriented view, our
encoding uses a fully lifted representation at the level of action templates.
It follows a time-oriented view in the spirit of previous work in
constraint-based scheduling. As a result, the proposed encoding is simple and
compact as it grows with the number of actions in a solution plan rather than
the number of possible primitive actions. When solved with an SMT solver, we
show that the proposed encoding is slightly more efficient than
state-of-the-art methods on temporally constrained planning benchmarks while
clearly outperforming other fully constraint-based approaches.
Open the Black Box Data-Driven Explanation of Black Box Decision Systems
Authors: Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Luca Pappalardo, Salvatore Ruggieri, Franco Turini
Date: 2018-06-26 00:00:00
Tasks: Decision Making

Black box systems for automated decision making, often based on machine
learning over (big) data, map a user's features into a class or a score without
exposing the reasons why. This is problematic not only for lack of
transparency, but also for possible biases hidden in the algorithms, due to
human prejudices and collection artifacts hidden in the training data, which
may lead to unfair or wrong decisions. We introduce the local-to-global
framework for black box explanation, a novel approach with promising early
results, which paves the road for a wide spectrum of future developments along
three dimensions: (i) the language for expressing explanations in terms of
highly expressive logic-based rules, with a statistical and causal
interpretation; (ii) the inference of local explanations aimed at revealing the
logic of the decision adopted for a specific instance by querying and auditing
the black box in the vicinity of the target instance; (iii), the bottom-up
generalization of the many local explanations into simple global ones, with
algorithms that optimize the quality and comprehensibility of explanations.
Text-Independent Speaker Verification Based on Deep Neural Networks and Segmental Dynamic Time Warping
Authors: Mohamed Adel, Mohamed Afify, Akram Gaballah
Date: 2018-06-26 00:00:00
Tasks: Dynamic Time Warping, Speaker Verification, Text-Independent Speaker Verification

In this paper we present a new method for text-independent speaker
verification that combines segmental dynamic time warping (SDTW) and the
d-vector approach. The d-vectors, generated from a feed forward deep neural
network trained to distinguish between speakers, are used as features to
perform alignment and hence calculate the overall distance between the
enrolment and test utterances.We present results on the NIST 2008 data set for
speaker verification where the proposed method outperforms the conventional
i-vector baseline with PLDA scores and outperforms d-vector approach with local
distances based on cosine and PLDA scores. Also score combination with the
i-vector/PLDA baseline leads to significant gains over both methods.
Coupled Dictionary Learning for Multi-contrast MRI Reconstruction
Authors: Pingfan Song, Lior Weizman, Joao F. C. Mota, Yonina C. Eldar, Miguel R. D. Rodrigues
Date: 2018-06-26 00:00:00
Tasks: Anatomy, Denoising, Dictionary Learning, MRI Reconstruction

Medical imaging tasks often involve multiple contrasts, such as T1- and
T2-weighted magnetic resonance imaging (MRI) data. These contrasts capture
information associated with the same underlying anatomy and thus exhibit
similarities. In this paper, we propose a Coupled Dictionary Learning based
multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available
guidance contrast to restore the target contrast. Our approach consists of
three stages: coupled dictionary learning, coupled sparse denoising, and
$k$-space consistency enforcing. The first stage learns a group of dictionaries
that capture correlations among multiple contrasts. By capitalizing on the
learned adaptive dictionaries, the second stage performs joint sparse coding to
denoise the corrupted target image with the aid of a guidance contrast. The
third stage enforces consistency between the denoised image and the
measurements in the $k$-space domain. Numerical experiments on the
retrospective under-sampling of clinical MR images demonstrate that
incorporating additional guidance contrast via our design improves MRI
reconstruction, compared to state-of-the-art approaches.
Tangent-Space Regularization for Neural-Network Models of Dynamical Systems
Authors: Fredrik Bagge Carlson, Rolf Johansson, Anders Robertsson
Date: 2018-06-26 00:00:00

This work introduces the concept of tangent space regularization for
neural-network models of dynamical systems. The tangent space to the dynamics
function of many physical systems of interest in control applications exhibits
useful properties, e.g., smoothness, motivating regularization of the model
Jacobian along system trajectories using assumptions on the tangent space of
the dynamics. Without assumptions, large amounts of training data are required
for a neural network to learn the full non-linear dynamics without overfitting.
We compare different network architectures on one-step prediction and
simulation performance and investigate the propensity of different
architectures to learn models with correct input-output Jacobian. Furthermore,
the influence of $L_2$ weight regularization on the learned Jacobian eigenvalue
spectrum, and hence system stability, is investigated.
Hierarchical VampPrior Variational Fair Auto-Encoder
Authors: Philip Botros, Jakub M. Tomczak
Date: 2018-06-26 00:00:00
Tasks: Decision Making, Fairness

Decision making is a process that is extremely prone to different biases. In
this paper we consider learning fair representations that aim at removing
nuisance (sensitive) information from the decision process. For this purpose,
we propose to use deep generative modeling and adapt a hierarchical Variational
Auto-Encoder to learn these fair representations. Moreover, we utilize the
mutual information as a useful regularizer for enforcing fairness of a
representation. In experiments on two benchmark datasets and two scenarios
where the sensitive variables are fully and partially observable, we show that
the proposed approach either outperforms or performs on par with the current
best model.
Autoencoders for Multi-Label Prostate MR Segmentation
Authors: Ard de Gelder, Henkjan Huisman
Date: 2018-06-09 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation

Organ image segmentation can be improved by implementing prior knowledge
about the anatomy. One way of doing this is by training an autoencoder to learn
a lowdimensional representation of the segmentation. In this paper, this is
applied in multi-label prostate MR segmentation, with some positive results.
Manifold Structured Prediction
Authors: Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi, Lorenzo Rosasco
Date: 2018-06-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, regression, Structured Prediction

Structured prediction provides a general framework to deal with supervised
problems where the outputs have semantically rich structure. While classical
approaches consider finite, albeit potentially huge, output spaces, in this
paper we discuss how structured prediction can be extended to a continuous
scenario. Specifically, we study a structured prediction approach to manifold
valued regression. We characterize a class of problems for which the considered
approach is statistically consistent and study how geometric optimization can
be used to compute the corresponding estimator. Promising experimental results
on both simulated and real data complete our study.
AirLab: Autograd Image Registration Laboratory
Authors: Robin Sandkühler, Christoph Jud, Simon Andermatt, Philippe C. Cattin
Date: 2018-06-26 00:00:00
Tasks: CPU, GPU, Image Registration, Medical Image Analysis, Medical Image Registration

Medical image registration is an active research topic and forms a basis for many medical image analysis tasks. Although image registration is a rather general concept specialized methods are usually required to target a specific registration problem. The development and implementation of such methods has been tough so far as the gradient of the objective has to be computed. Also, its evaluation has to be performed preferably on a GPU for larger images and for more complex transformation models and regularization terms. This hinders researchers from rapid prototyping and poses hurdles to reproduce research results. There is a clear need for an environment which hides this complexity to put the modeling and the experimental exploration of registration methods into the foreground. With the "Autograd Image Registration Laboratory" (AIRLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent. It is meant as a laboratory for researchers and developers enabling them to rapidly try out new ideas for registering images and to reproduce registration results which have already been published. AIRLab is implemented in Python using PyTorch as tensor and optimization library and SimpleITK for basic image IO. Therefore, it profits from recent advances made by the machine learning community concerning optimization and deep neural network models. The presented draft of this paper outlines AIRLab with first code snippets and performance analyses. A more exhaustive introduction will follow as a final version soon.
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music
Authors: Rachel Manzelli, Vijay Thakkar, Ali Siahkamari, Brian Kulis
Date: 2018-06-26 00:00:00
Tasks: Music Generation

Existing automatic music generation approaches that feature deep learning can
be broadly classified into two types: raw audio models and symbolic models.
Symbolic models, which train and generate at the note level, are currently the
more prevalent approach; these models can capture long-range dependencies of
melodic structure, but fail to grasp the nuances and richness of raw audio
generations. Raw audio models, such as DeepMind's WaveNet, train directly on
sampled audio waveforms, allowing them to produce realistic-sounding, albeit
unstructured music. In this paper, we propose an automatic music generation
methodology combining both of these approaches to create structured,
realistic-sounding compositions. We consider a Long Short Term Memory network
to learn the melodic structure of different styles of music, and then use the
unique symbolic generations from this model as a conditioning input to a
WaveNet-based raw audio generator, creating a model for automatic, novel music.
We then evaluate this approach by showcasing results of this work.