pwc-archive/papers-with-abstracts huggingface dataset:
Computational Social Choice Meets Databases
Authors: Benny Kimelfeld, Phokion G. Kolaitis, Julia Stoyanovich
Date: 2018-05-10 00:00:00
Tasks: Management

We develop a novel framework that aims to create bridges between the
computational social choice and the database management communities. This
framework enriches the tasks currently supported in computational social choice
with relational database context, thus making it possible to formulate
sophisticated queries about voting rules, candidates, voters, issues, and
positions. At the conceptual level, we give rigorous semantics to queries in
this framework by introducing the notions of necessary answers and possible
answers to queries. At the technical level, we embark on an investigation of
the computational complexity of the necessary answers. We establish a number of
results about the complexity of the necessary answers of conjunctive queries
involving positional scoring rules that contrast sharply with earlier results
about the complexity of the necessary winners.
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control
Authors: Kenny Young, Baoxiang Wang, Matthew E. Taylor
Date: 2018-05-10 00:00:00
Tasks: Atari Games, Meta-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) has had many successes in both "deep" and "shallow" settings. In both cases, significant hyperparameter tuning is often required to achieve good performance. Furthermore, when nonlinear function approximation is used, non-stationarity in the state representation can lead to learning instability. A variety of techniques exist to combat this --- most notably large experience replay buffers or the use of multiple parallel actors. These techniques come at the cost of moving away from the online RL problem as it is traditionally formulated (i.e., a single agent learning online without maintaining a large database of training examples). Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem. This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces. Our novel technique, Metatrace, makes use of an eligibility trace analogous to methods like $TD(\lambda)$. We explore tuning both a single scalar step-size and a separate step-size for each learned parameter. We evaluate Metatrace first for control with linear function approximation in the classic mountain car problem and then in a noisy, non-stationary version. Finally, we apply Metatrace for control with nonlinear function approximation in 5 games in the Arcade Learning Environment where we explore how it impacts learning speed and robustness to initial step-size choice. Results show that the meta-step-size parameter of Metatrace is easy to set, Metatrace can speed learning, and Metatrace can allow an RL algorithm to deal with non-stationarity in the learning task.
Training Recurrent Neural Networks via Dynamical Trajectory-Based Optimization
Authors: Hamid Khodabandehlou, M. Sami Fadali
Date: 2018-05-10 00:00:00

This paper introduces a new method to train recurrent neural networks using
dynamical trajectory-based optimization. The optimization method utilizes a
projected gradient system (PGS) and a quotient gradient system (QGS) to
determine the feasible regions of an optimization problem and search the
feasible regions for local minima. By exploring the feasible regions, local
minima are identified and the local minimum with the lowest cost is chosen as
the global minimum of the optimization problem. Lyapunov theory is used to
prove the stability of the local minima and their stability in the presence of
measurement errors. Numerical examples show that the new approach provides
better results than genetic algorithm and error backpropagation (EBP) trained
networks.
Quantum Machine Learning
Authors: Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd
Date: 2016-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

Fuelled by increasing computer power and algorithmic advances, machine
learning techniques have become powerful tools for finding patterns in data.
Since quantum systems produce counter-intuitive patterns believed not to be
efficiently produced by classical systems, it is reasonable to postulate that
quantum computers may outperform classical computers on machine learning tasks.
The field of quantum machine learning explores how to devise and implement
concrete quantum software that offers such advantages. Recent work has made
clear that the hardware and software challenges are still considerable but has
also opened paths towards solutions.
Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing
Authors: Zhe Li, Ji Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Qinru Qiu, Bo Yuan, Yanzhi Wang
Date: 2018-05-10 00:00:00
Tasks: GPU

Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous
success in many machine learning applications. Nevertheless, the deep structure
has brought significant increases in computation complexity. Largescale deep
learning systems mainly operate in high-performance server clusters, thus
restricting the application extensions to personal or mobile devices. Previous
works on GPU and/or FPGA acceleration for DCNNs show increasing speedup, but
ignore other constraints, such as area, power, and energy. Stochastic Computing
(SC), as a unique data representation and processing technique, has the
potential to enable the design of fully parallel and scalable hardware
implementations of large-scale deep learning systems. This paper proposed an
automatic design allocation algorithm driven by budget requirement considering
overall accuracy performance. This systematic method enables the automatic
design of a DCNN where all design parameters are jointly optimized.
Experimental results demonstrate that proposed algorithm can achieve a joint
optimization of all design parameters given the comprehensive budget of a DCNN.
Semi-Supervised Domain Adaptation with Representation Learning for Semantic Segmentation across Time
Authors: Assia Benbihi, Matthieu Geist, Cédric Pradalier
Date: 2018-05-10 00:00:00
Tasks: Domain Adaptation, regression, Representation Learning, Semantic Segmentation, Semi-supervised Domain Adaptation, Transfer Learning

Deep learning generates state-of-the-art semantic segmentation provided that a large number of images together with pixel-wise annotations are available. To alleviate the expensive data collection process, we propose a semi-supervised domain adaptation method for the specific case of images with similar semantic content but different pixel distributions. A network trained with supervision on a past dataset is finetuned on the new dataset to conserve its features maps. The domain adaptation becomes a simple regression between feature maps and does not require annotations on the new dataset. This method reaches performances similar to classic transfer learning on the PASCAL VOC dataset with synthetic transformations.
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Authors: Kfir Aberman, Jing Liao, Mingyi Shi, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or
Date: 2018-05-10 00:00:00
Tasks: Image Morphing

Correspondence between images is a fundamental problem in computer vision,
with a variety of graphics applications. This paper presents a novel method for
sparse cross-domain correspondence. Our method is designed for pairs of images
where the main objects of interest may belong to different semantic categories
and differ drastically in shape and appearance, yet still contain semantically
related or geometrically similar parts. Our approach operates on hierarchies of
deep features, extracted from the input images by a pre-trained CNN.
Specifically, starting from the coarsest layer in both hierarchies, we search
for Neural Best Buddies (NBB): pairs of neurons that are mutual nearest
neighbors. The key idea is then to percolate NBBs through the hierarchy, while
narrowing down the search regions at each level and retaining only NBBs with
significant activations. Furthermore, in order to overcome differences in
appearance, each pair of search regions is transformed into a common
appearance. We evaluate our method via a user study, in addition to comparisons
with alternative correspondence approaches. The usefulness of our method is
demonstrated using a variety of graphics applications, including cross-domain
image alignment, creation of hybrid images, automatic image morphing, and more.
Unsupervised Deep Representations for Learning Audience Facial Behaviors
Authors: Suman Saha, Rajitha Navarathna, Leonhard Helminger, Romann Weber
Date: 2018-05-10 00:00:00
Tasks: Generative Adversarial Network

In this paper, we present an unsupervised learning approach for analyzing
facial behavior based on a deep generative model combined with a convolutional
neural network (CNN). We jointly train a variational auto-encoder (VAE) and a
generative adversarial network (GAN) to learn a powerful latent representation
from footage of audiences viewing feature-length movies. We show that the
learned latent representation successfully encodes meaningful signatures of
behaviors related to audience engagement (smiling & laughing) and disengagement
(yawning). Our results provide a proof of concept for a more general
methodology for annotating hard-to-label multimedia data featuring sparse
examples of signals of interest.
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric
Authors: Ruidi Chen, Ioannis Ch. Paschalidis
Date: 2017-06-07 00:00:00
Tasks: Outlier Detection, regression

We present a Distributionally Robust Optimization (DRO) approach to estimate
a robustified regression plane in a linear regression setting, when the
observed samples are potentially contaminated with adversarially corrupted
outliers. Our approach mitigates the impact of outliers through hedging against
a family of distributions on the observed data, some of which assign very low
probabilities to the outliers. The set of distributions under consideration are
close to the empirical distribution in the sense of the Wasserstein metric. We
show that this DRO formulation can be relaxed to a convex optimization problem
which encompasses a class of models. By selecting proper norm spaces for the
Wasserstein metric, we are able to recover several commonly used regularized
regression models. We provide new insights into the regularization term and
give guidance on the selection of the regularization coefficient from the
standpoint of a confidence region. We establish two types of performance
guarantees for the solution to our formulation under mild conditions. One is
related to its out-of-sample behavior (prediction bias), and the other concerns
the discrepancy between the estimated and true regression planes (estimation
bias). Extensive numerical results demonstrate the superiority of our approach
to a host of regression models, in terms of the prediction and estimation
accuracies. We also consider the application of our robust learning procedure
to outlier detection, and show that our approach achieves a much higher AUC
(Area Under the ROC Curve) than M-estimation.
Laconic Deep Learning Computing
Authors: Sayeh Sharify, Mostafa Mahmoud, Alberto Delmas Lascorz, Milos Nikolic, Andreas Moshovos
Date: 2018-05-10 00:00:00
Tasks: 2k, Deep Learning, image-classification, Image Classification

We motivate a method for transparently identifying ineffectual computations
in unmodified Deep Learning models and without affecting accuracy.
Specifically, we show that if we decompose multiplications down to the bit
level the amount of work performed during inference for image classification
models can be consistently reduced by two orders of magnitude. In the best case
studied of a sparse variant of AlexNet, this approach can ideally reduce
computation work by more than 500x. We present Laconic a hardware accelerator
that implements this approach to improve execution time, and energy efficiency
for inference with Deep Learning Networks. Laconic judiciously gives up some of
the work reduction potential to yield a low-cost, simple, and energy efficient
design that outperforms other state-of-the-art accelerators. For example, a
Laconic configuration that uses a weight memory interface with just 128 wires
outperforms a conventional accelerator with a 2K-wire weight memory interface
by 2.3x on average while being 2.13x more energy efficient on average. A
Laconic configuration that uses a 1K-wire weight memory interface, outperforms
the 2K-wire conventional accelerator by 15.4x and is 1.95x more energy
efficient. Laconic does not require but rewards advances in model design such
as a reduction in precision, the use of alternate numeric representations that
reduce the number of bits that are "1", or an increase in weight or activation
sparsity.
Ordinal Depth Supervision for 3D Human Pose Estimation
Authors: Georgios Pavlakos, Xiaowei Zhou, Kostas Daniilidis
Date: 2018-05-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation

Our ability to train end-to-end systems for 3D human pose estimation from
single images is currently constrained by the limited availability of 3D
annotations for natural images. Most datasets are captured using Motion Capture
(MoCap) systems in a studio setting and it is difficult to reach the
variability of 2D human pose datasets, like MPII or LSP. To alleviate the need
for accurate 3D ground truth, we propose to use a weaker supervision signal
provided by the ordinal depths of human joints. This information can be
acquired by human annotators for a wide range of images and poses. We showcase
the effectiveness and flexibility of training Convolutional Networks (ConvNets)
with these ordinal relations in different settings, always achieving
competitive performance with ConvNets trained with accurate 3D joint
coordinates. Additionally, to demonstrate the potential of the approach, we
augment the popular LSP and MPII datasets with ordinal depth annotations. This
extension allows us to present quantitative and qualitative evaluation in
non-studio conditions. Simultaneously, these ordinal annotations can be easily
incorporated in the training procedure of typical ConvNets for 3D human pose.
Through this inclusion we achieve new state-of-the-art performance for the
relevant benchmarks and validate the effectiveness of ordinal depth supervision
for 3D human pose.
Learning to Estimate 3D Human Pose and Shape from a Single Color Image
Authors: Georgios Pavlakos, Luyang Zhu, Xiaowei Zhou, Kostas Daniilidis
Date: 2018-05-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Human Pose Estimation

This work addresses the problem of estimating the full body 3D human pose and
shape from a single color image. This is a task where iterative
optimization-based solutions have typically prevailed, while Convolutional
Networks (ConvNets) have suffered because of the lack of training data and
their low resolution 3D predictions. Our work aims to bridge this gap and
proposes an efficient and effective direct prediction method based on ConvNets.
Central part to our approach is the incorporation of a parametric statistical
body shape model (SMPL) within our end-to-end framework. This allows us to get
very detailed 3D mesh results, while requiring estimation only of a small
number of parameters, making it friendly for direct network prediction.
Interestingly, we demonstrate that these parameters can be predicted reliably
only from 2D keypoints and masks. These are typical outputs of generic 2D human
analysis ConvNets, allowing us to relax the massive requirement that images
with 3D shape ground truth are available for training. Simultaneously, by
maintaining differentiability, at training time we generate the 3D mesh from
the estimated parameters and optimize explicitly for the surface using a 3D
per-vertex loss. Finally, a differentiable renderer is employed to project the
3D mesh to the image, which enables further refinement of the network, by
optimizing for the consistency of the projection with 2D annotations (i.e., 2D
keypoints or masks). The proposed approach outperforms previous baselines on
this task and offers an attractive solution for direct prediction of 3D shape
from a single color image.
Pragmatically Informative Image Captioning with Character-Level Inference
Authors: Reuben Cohn-Gordon, Noah Goodman, Christopher Potts
Date: 2018-04-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Image Captioning, Rolling Shutter Correction

We combine a neural image captioner with a Rational Speech Acts (RSA) model
to make a system that is pragmatically informative: its objective is to produce
captions that are not merely true but also distinguish their inputs from
similar images. Previous attempts to combine RSA with neural image captioning
require an inference which normalizes over the entire set of possible
utterances. This poses a serious problem of efficiency, previously solved by
sampling a small subset of possible utterances. We instead solve this problem
by implementing a version of RSA which operates at the level of characters
("a","b","c"...) during the unrolling of the caption. We find that the
utterance-level effect of referential captions can be obtained with only
character-level decisions. Finally, we introduce an automatic method for
testing the performance of pragmatic speaker models, and show that our model
outperforms a non-pragmatic baseline as well as a word-level RSA captioner.
Exploiting Unintended Feature Leakage in Collaborative Learning
Authors: Luca Melis, Congzheng Song, Emiliano De Cristofaro, Vitaly Shmatikov
Date: 2018-05-10 00:00:00
Tasks: Federated Learning

Collaborative machine learning and related techniques such as federated
learning allow multiple participants, each with his own training dataset, to
build a joint model by training locally and periodically exchanging model
updates. We demonstrate that these updates leak unintended information about
participants' training data and develop passive and active inference attacks to
exploit this leakage. First, we show that an adversarial participant can infer
the presence of exact data points -- for example, specific locations -- in
others' training data (i.e., membership inference). Then, we show how this
adversary can infer properties that hold only for a subset of the training data
and are independent of the properties that the joint model aims to capture. For
example, he can infer when a specific person first appears in the photos used
to train a binary gender classifier. We evaluate our attacks on a variety of
tasks, datasets, and learning configurations, analyze their limitations, and
discuss possible defenses.
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Authors: Yuning Mao, Xiang Ren, Jiaming Shen, Xiaotao Gu, Jiawei Han
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a novel end-to-end reinforcement learning approach to automatic
taxonomy induction from a set of terms. While prior methods treat the problem
as a two-phase task (i.e., detecting hypernymy pairs followed by organizing
these pairs into a tree-structured hierarchy), we argue that such two-phase
methods may suffer from error propagation, and cannot effectively optimize
metrics that capture the holistic structure of a taxonomy. In our approach, the
representations of term pairs are learned using multiple sources of information
and used to determine \textit{which} term to select and \textit{where} to place
it on the taxonomy via a policy network. All components are trained in an
end-to-end manner with cumulative rewards, measured by a holistic tree metric
over the training taxonomies. Experiments on two public datasets of different
domains show that our approach outperforms prior state-of-the-art taxonomy
induction methods up to 19.6\% on ancestor F1.
Scaling limit of the Stein variational gradient descent: the mean field regime
Authors: Jianfeng Lu, Yulong Lu, James Nolen
Date: 2018-05-10 00:00:00

We study an interacting particle system in $\mathbf{R}^d$ motivated by Stein
variational gradient descent [Q. Liu and D. Wang, NIPS 2016], a deterministic
algorithm for sampling from a given probability density with unknown
normalization. We prove that in the large particle limit the empirical measure
of the particle system converges to a solution of a non-local and nonlinear
PDE. We also prove global existence, uniqueness and regularity of the solution
to the limiting PDE. Finally, we prove that the solution to the PDE converges
to the unique invariant solution in long time limit.
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency
Authors: Bingzhen Wei, Xuancheng Ren, Xu sun, Yi Zhang, Xiaoyan Cai, Qi Su
Date: 2018-05-10 00:00:00
Tasks: Abstractive Text Summarization, Text Summarization

Abstractive text summarization is a highly difficult problem, and the
sequence-to-sequence model has shown success in improving the performance on
the task. However, the generated summaries are often inconsistent with the
source content in semantics. In such cases, when generating summaries, the
model selects semantically unrelated words with respect to the source content
as the most probable output. The problem can be attributed to heuristically
constructed training data, where summaries can be unrelated to the source
content, thus containing semantically unrelated words and spurious word
correspondence. In this paper, we propose a regularization approach for the
sequence-to-sequence model and make use of what the model has learned to
regularize the learning objective to alleviate the effect of the problem. In
addition, we propose a practical human evaluation method to address the problem
that the existing automatic evaluation method does not evaluate the semantic
consistency with the source content properly. Experimental results demonstrate
the effectiveness of the proposed approach, which outperforms almost all the
existing models. Especially, the proposed approach improves the semantic
consistency by 4\% in terms of human evaluation.
Towards an Unequivocal Representation of Actions
Authors: Michael Wray, Davide Moltisanti, Dima Damen
Date: 2018-05-10 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization

This work introduces verb-only representations for actions and interactions;
the problem of describing similar motions (e.g. 'open door', 'open cupboard'),
and distinguish differing ones (e.g. 'open door' vs 'open bottle') using
verb-only labels. Current approaches for action recognition neglect legitimate
semantic ambiguities and class overlaps between verbs (Fig. 1), relying on the
objects to disambiguate interactions. We deviate from single-verb labels and
introduce a mapping between observations and multiple verb labels - in order to
create an Unequivocal Representation of Actions. The new representation
benefits from increased vocabulary and a soft assignment to an enriched space
of verb labels. We learn these representations as multi-output regression,
using a two-stream fusion CNN. The proposed approach outperforms conventional
single-verb labels (also known as majority voting) on three egocentric datasets
for both recognition and retrieval.