pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Image Manipulation with Natural Language Instruction Commands
Authors: Seitaro Shinagawa, Koichiro Yoshino, Sakriani Sakti, Yu Suzuki, Satoshi Nakamura
Date: 2018-02-23 00:00:00
Tasks: Image Generation, Image Manipulation

We propose an interactive image-manipulation system with natural language
instruction, which can generate a target image from a source image and an
instruction that describes the difference between the source and the target
image. The system makes it possible to modify a generated image interactively
and make natural language conditioned image generation more controllable. We
construct a neural network that handles image vectors in latent space to
transform the source vector to the target vector by using the vector of
instruction. The experimental results indicate that the proposed framework
successfully generates the target image by using a source image and an
instruction on manipulation in our dataset.
High-Dimensional Vector Semantics
Authors: M. Andrecut
Date: 2018-02-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper we explore the "vector semantics" problem from the perspective
of "almost orthogonal" property of high-dimensional random vectors. We show
that this intriguing property can be used to "memorize" random vectors by
simply adding them, and we provide an efficient probabilistic solution to the
set membership problem. Also, we discuss several applications to word context
vector embeddings, document sentences similarity, and spam filtering.
Empirical Risk Minimization under Fairness Constraints
Authors: Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
Date: 2018-02-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Fairness

We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our approach. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.
CrowdMI: Multiple Imputation via Crowdsourcing
Authors: Lovedeep Gondara
Date: 2016-12-08 00:00:00
Tasks: Imputation, valid

Can humans impute missing data with similar proficiency as machines? This is
the question we aim to answer in this paper. We present a novel idea of
converting observations with missing data in to a survey questionnaire, which
is presented to crowdworkers for completion. We replicate a multiple imputation
framework by having multiple unique crowdworkers complete our questionnaire.
Experimental results demonstrate that using our method, it is possible to
generate valid imputations for qualitative and quantitative missing data, with
results comparable to imputations generated by complex statistical models.
Visualizing the Flow of Discourse with a Concept Ontology
Authors: Baoxu Shi, Tim Weninger
Date: 2018-02-23 00:00:00
Tasks: Argument Mining

Understanding and visualizing human discourse has long being a challenging
task. Although recent work on argument mining have shown success in classifying
the role of various sentences, the task of recognizing concepts and
understanding the ways in which they are discussed remains challenging. Given
an email thread or a transcript of a group discussion, our task is to extract
the relevant concepts and understand how they are referenced and re-referenced
throughout the discussion. In the present work, we present a preliminary
approach for extracting and visualizing group discourse by adapting Wikipedia's
category hierarchy to be an external concept ontology. From a user study, we
found that our method achieved better results than 4 strong alternative
approaches, and we illustrate our visualization method based on the extracted
discourse flows.
Adversarial vulnerability for any classifier
Authors: Alhussein Fawzi, Hamza Fawzi, Omar Fawzi
Date: 2018-02-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: General Classification

Despite achieving impressive performance, state-of-the-art classifiers remain
highly vulnerable to small, imperceptible, adversarial perturbations. This
vulnerability has proven empirically to be very intricate to address. In this
paper, we study the phenomenon of adversarial perturbations under the
assumption that the data is generated with a smooth generative model. We derive
fundamental upper bounds on the robustness to perturbations of any
classification function, and prove the existence of adversarial perturbations
that transfer well across different classifiers with small risk. Our analysis
of the robustness also provides insights onto key properties of generative
models, such as their smoothness and dimensionality of latent space. We
conclude with numerical experimental results showing that our bounds provide
informative baselines to the maximal achievable robustness on several datasets.
Kernel Implicit Variational Inference
Authors: Jiaxin Shi, Shengyang Sun, Jun Zhu
Date: 2017-05-29 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, regression, Variational Inference

Recent progress in variational inference has paid much attention to the
flexibility of variational posteriors. One promising direction is to use
implicit distributions, i.e., distributions without tractable densities as the
variational posterior. However, existing methods on implicit posteriors still
face challenges of noisy estimation and computational infeasibility when
applied to models with high-dimensional latent variables. In this paper, we
present a new approach named Kernel Implicit Variational Inference that
addresses these challenges. As far as we know, for the first time implicit
variational inference is successfully applied to Bayesian neural networks,
which shows promising results on both regression and classification tasks.
Convergent Tree Backup and Retrace with Function Approximation
Authors: Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent
Date: 2017-05-25 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning

Off-policy learning is key to scaling up reinforcement learning as it allows
to learn about a target policy from the experience generated by a different
behavior policy. Unfortunately, it has been challenging to combine off-policy
learning with function approximation and multi-step bootstrapping in a way that
leads to both stable and efficient algorithms. In this work, we show that the
\textsc{Tree Backup} and \textsc{Retrace} algorithms are unstable with linear
function approximation, both in theory and in practice with specific examples.
Based on our analysis, we then derive stable and efficient gradient-based
algorithms using a quadratic convex-concave saddle-point formulation. By
exploiting the problem structure proper to these algorithms, we are able to
provide convergence guarantees and finite-sample bounds. The applicability of
our new analysis also goes beyond \textsc{Tree Backup} and \textsc{Retrace} and
allows us to provide new convergence rates for the GTD and GTD2 algorithms
without having recourse to projections or Polyak averaging.
Reservoir computing with simple oscillators: Virtual and real networks
Authors: André Röhm, Kathy Lüdge
Date: 2018-02-23 00:00:00

The reservoir computing scheme is a machine learning mechanism which utilizes
the naturally occuring computational capabilities of dynamical systems. One
important subset of systems that has proven powerful both in experiments and
theory are delay-systems. In this work, we investigate the reservoir computing
performance of hybrid network-delay systems systematically by evaluating the
NARMA10 and the Sante Fe task.. We construct 'multiplexed networks' that can be
seen as intermediate steps on the scale from classical networks to the 'virtual
networks' of delay systems. We find that the delay approach can be extended to
the network case without loss of computational power, enabling the construction
of faster reservoir computing substrates.
Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network
Authors: Ayan Kumar Bhunia, Subham Mukherjee, Aneeshan Sain, Ankan Kumar Bhunia, Partha Pratim Roy, Umapada Pal
Date: 2018-02-23 00:00:00

In this paper, we propose a novel approach of word-level Indic script identification using only character-level data in training stage. The advantages of using character level data for training have been outlined in section I. Our method uses a multimodal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification task. We take handwritten data in either modality as input and the opposite modality is generated through intermodality conversion. Thereafter, we feed this offline-online modality pair to our network. Hence, along with the advantage of utilizing information from both the modalities, it can work as a single framework for both offline and online script identification simultaneously which alleviates the need for designing two separate script identification modules for individual modality. One more major contribution is that we propose a novel conditional multimodal fusion scheme to combine the information from offline and online modality which takes into account the real origin of the data being fed to our network and thus it combines adaptively. An exhaustive experiment has been done on a data set consisting of English and six Indic scripts. Our proposed framework clearly outperforms different frameworks based on traditional classifiers along with handcrafted features and deep learning based methods with a clear margin. Extensive experiments show that using only character level training data can achieve state-of-art performance similar to that obtained with traditional training using word level data in our framework.
Semantic Vector Spaces for Broadening Consideration of Consequences
Authors: Douglas Summers Stay
Date: 2018-02-23 00:00:00
Tasks: Common Sense Reasoning

Reasoning systems with too simple a model of the world and human intent are
unable to consider potential negative side effects of their actions and modify
their plans to avoid them (e.g., avoiding potential errors). However,
hand-encoding the enormous and subtle body of facts that constitutes common
sense into a knowledge base has proved too difficult despite decades of work.
Distributed semantic vector spaces learned from large text corpora, on the
other hand, can learn representations that capture shades of meaning of
common-sense concepts and perform analogical and associational reasoning in
ways that knowledge bases are too rigid to perform, by encoding concepts and
the relations between them as geometric structures. These have, however, the
disadvantage of being unreliable, poorly understood, and biased in their view
of the world by the source material. This chapter will discuss how these
approaches may be combined in a way that combines the best properties of each
for understanding the world and human intentions in a richer way.
Computation of optimal transport and related hedging problems via penalization and neural networks
Authors: Stephan Eckstein, Michael Kupper
Date: 2018-02-23 00:00:00
Tasks: Portfolio Optimization

This paper presents a widely applicable approach to solving (multi-marginal,
martingale) optimal transport and related problems via neural networks. The
core idea is to penalize the optimization problem in its dual formulation and
reduce it to a finite dimensional one which corresponds to optimizing a neural
network with smooth objective function. We present numerical examples from
optimal transport, martingale optimal transport, portfolio optimization under
uncertainty and generative adversarial networks that showcase the generality
and effectiveness of the approach.
Can Neural Networks Understand Logical Entailment?
Authors: Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette
Date: 2018-02-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Inductive Bias

We introduce a new dataset of logical entailments for the purpose of
measuring models' ability to capture and exploit the structure of logical
expressions against an entailment prediction task. We use this task to compare
a series of architectures which are ubiquitous in the sequence-processing
literature, in addition to a new model class---PossibleWorldNets---which
computes entailment as a "convolution over possible worlds". Results show that
convolutional networks present the wrong inductive bias for this class of
problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM
RNNs due to their enhanced ability to exploit the syntax of logic, and
PossibleWorldNets outperform all benchmarks.
Training wide residual networks for deployment using a single bit for each weight
Authors: Mark D. McDonnell
Date: 2018-02-23 00:00:00
Proceeding: ICLR 2018 1

For fast and energy-efficient deployment of trained deep neural networks on
resource-constrained embedded hardware, each learned weight parameter should
ideally be represented and stored using a single bit. Error-rates usually
increase when this requirement is imposed. Here, we report large improvements
in error rates on multiple datasets, for deep convolutional neural networks
deployed with 1-bit-per-weight. Using wide residual networks as our main
baseline, our approach simplifies existing methods that binarize weights by
applying the sign function in training; we apply scaling factors for each layer
with constant unlearned values equal to the layer-specific standard deviations
used for initialization. For CIFAR-10, CIFAR-100 and ImageNet, and models with
1-bit-per-weight requiring less than 10 MB of parameter memory, we achieve
error rates of 3.9%, 18.5% and 26.0% / 8.5% (Top-1 / Top-5) respectively. We
also considered MNIST, SVHN and ImageNet32, achieving 1-bit-per-weight test
results of 0.27%, 1.9%, and 41.3% / 19.1% respectively. For CIFAR, our error
rates halve previously reported values, and are within about 1% of our
error-rates for the same network with full-precision weights. For networks that
overfit, we also show significant improvements in error rate by not learning
batch normalization scale and offset parameters. This applies to both full
precision and 1-bit-per-weight networks. Using a warm-restart learning-rate
schedule, we found that training for 1-bit-per-weight is just as fast as
full-precision networks, with better accuracy than standard schedules, and
achieved about 98%-99% of peak performance in just 62 training epochs for
CIFAR-10/100. For full training code and trained models in MATLAB, Keras and
PyTorch see https://github.com/McDonnell-Lab/1-bit-per-weight/ .
6D Pose Estimation using an Improved Method based on Point Pair Features
Authors: Joel Vidal, Chyi-Yeu Lin, Robert Martí
Date: 2018-02-23 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Pose Estimation

The Point Pair Feature (Drost et al. 2010) has been one of the most
successful 6D pose estimation method among model-based approaches as an
efficient, integrated and compromise alternative to the traditional local and
global pipelines. During the last years, several variations of the algorithm
have been proposed. Among these extensions, the solution introduced by
Hinterstoisser et al. (2016) is a major contribution. This work presents a
variation of this PPF method applied to the SIXD Challenge datasets presented
at the 3rd International Workshop on Recovering 6D Object Pose held at the ICCV
2017. We report an average recall of 0.77 for all datasets and overall recall
of 0.82, 0.67, 0.85, 0.37, 0.97 and 0.96 for hinterstoisser, tless, tudlight,
rutgers, tejani and doumanoglou datasets, respectively.
Closed-form solution to cooperative visual-inertial structure from motion
Authors: Agostino Martinelli
Date: 2018-02-23 00:00:00
Tasks: Form, Sensor Fusion

This paper considers the problem of visual-inertial sensor fusion in the
cooperative case and it provides new theoretical contributions, which regard
its observability and its resolvability in closed form. The case of two agents
is investigated. Each agent is equipped with inertial sensors (accelerometer
and gyroscope) and with a monocular camera. By using the monocular camera, each
agent can observe the other agent. No additional camera observations (e.g., of
external point features in the environment) are considered. All the inertial
sensors are assumed to be affected by a bias. First, the entire observable
state is analytically derived. This state includes the absolute scale, the
relative velocity between the two agents, the three Euler angles that express
the rotation between the two agent frames and all the accelerometer and
gyroscope biases. Second, the paper provides the extension of the closed-form
solution given in [19] (which holds for a single agent) to the aforementioned
cooperative case. The impact of the presence of the bias on the performance of
this closed-form solution is investigated. As in the case of a single agent,
this performance is significantly sensitive to the presence of a bias on the
gyroscope, while, the presence of a bias on the accelerometer is negligible.
Finally, a simple and effective method to obtain the gyroscope bias is
proposed. Extensive simulations clearly show that the proposed method is
successful. It is amazing that, it is possible to automatically retrieve the
absolute scale and simultaneously calibrate the gyroscopes not only without any
prior knowledge (as in [13]), but also without external point features in the
environment.
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
Authors: Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Date: 2018-02-23 00:00:00

We study the problem of robustly learning multi-dimensional histograms. A
$d$-dimensional function $h: D \rightarrow \mathbb{R}$ is called a
$k$-histogram if there exists a partition of the domain $D \subseteq
\mathbb{R}^d$ into $k$ axis-aligned rectangles such that $h$ is constant within
each such rectangle. Let $f: D \rightarrow \mathbb{R}$ be a $d$-dimensional
probability density function and suppose that $f$ is $\mathrm{OPT}$-close, in
$L_1$-distance, to an unknown $k$-histogram (with unknown partition). Our goal
is to output a hypothesis that is $O(\mathrm{OPT}) + \epsilon$ close to $f$, in
$L_1$-distance. We give an algorithm for this learning problem that uses $n =
\tilde{O}_d(k/\epsilon^2)$ samples and runs in time $\tilde{O}_d(n)$. For any
fixed dimension, our algorithm has optimal sample complexity, up to logarithmic
factors, and runs in near-linear time. Prior to our work, the time complexity
of the $d=1$ case was well-understood, but significant gaps in our
understanding remained even for $d=2$.
MonoPerfCap: Human Performance Capture from Monocular Video
Authors: Weipeng Xu, Avishek Chatterjee, Michael Zollhöfer, Helge Rhodin, Dushyant Mehta, Hans-Peter Seidel, Christian Theobalt
Date: 2017-08-07 00:00:00
Tasks: Monocular Reconstruction, Pose Estimation, Video Editing

We present the first marker-less approach for temporally coherent 3D
performance capture of a human with general clothing from monocular video. Our
approach reconstructs articulated human skeleton motion as well as medium-scale
non-rigid surface deformations in general scenes. Human performance capture is
a challenging problem due to the large range of articulation, potentially fast
motion, and considerable non-rigid deformations, even from multi-view data.
Reconstruction from monocular video alone is drastically more challenging,
since strong occlusions and the inherent depth ambiguity lead to a highly
ill-posed reconstruction problem. We tackle these challenges by a novel
approach that employs sparse 2D and 3D human pose detections from a
convolutional neural network using a batch-based pose estimation strategy.
Joint recovery of per-batch motion allows to resolve the ambiguities of the
monocular reconstruction problem based on a low dimensional trajectory
subspace. In addition, we propose refinement of the surface geometry based on
fully automatically extracted silhouettes to enable medium-scale non-rigid
alignment. We demonstrate state-of-the-art performance capture results that
enable exciting applications such as video editing and free viewpoint video,
previously infeasible from monocular video. Our qualitative and quantitative
evaluation demonstrates that our approach significantly outperforms previous
monocular methods in terms of accuracy, robustness and scene complexity that
can be handled.
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
Authors: Hai Ye, Xin Jiang, Zhunchen Luo, WenHan Chao
Date: 2018-02-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Text Generation

In this paper, we propose to study the problem of COURT VIEW GENeration from
the fact description in a criminal case. The task aims to improve the
interpretability of charge prediction systems and help automatic legal document
generation. We formulate this task as a text-to-text natural language
generation (NLG) problem. Sequenceto-sequence model has achieved cutting-edge
performances in many NLG tasks. However, due to the non-distinctions of fact
descriptions, it is hard for Seq2Seq model to generate charge-discriminative
court views. In this work, we explore charge labels to tackle this issue. We
propose a label-conditioned Seq2Seq model with attention for this problem, to
decode court views conditioned on encoded charge labels. Experimental results
show the effectiveness of our method.
Alternating Direction Graph Matching
Authors: D. Khuê Lê-Huu, Nikos Paragios
Date: 2016-11-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Graph Matching

In this paper, we introduce a graph matching method that can account for
constraints of arbitrary order, with arbitrary potential functions. Unlike
previous decomposition approaches that rely on the graph structures, we
introduce a decomposition of the matching constraints. Graph matching is then
reformulated as a non-convex non-separable optimization problem that can be
split into smaller and much-easier-to-solve subproblems, by means of the
alternating direction method of multipliers. The proposed framework is modular,
scalable, and can be instantiated into different variants. Two instantiations
are studied exploring pairwise and higher-order constraints. Experimental
results on widely adopted benchmarks involving synthetic and real examples
demonstrate that the proposed solutions outperform existing pairwise graph
matching methods, and competitive with the state of the art in higher-order
settings.