pwc-archive/papers-with-abstracts huggingface dataset:
Fast Object Class Labelling via Speech
Authors: Michael Gygli, Vittorio Ferrari
Date: 2018-11-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object

Object class labelling is the task of annotating images with labels on the
presence or absence of objects from a given class vocabulary. Simply asking one
yes/no question per class, however, has a cost that is linear in the vocabulary
size and is thus inefficient for large vocabularies. Modern approaches rely on
a hierarchical organization of the vocabulary to reduce annotation time, but
remain expensive (several minutes per image for the 200 classes in ILSVRC).
Instead, we propose a new interface where classes are annotated via speech.
Speaking is fast and allows for direct access to the class name, without
searching through a list or hierarchy. As additional advantages, annotators can
simultaneously speak and scan the image for objects, the interface can be kept
extremely simple, and using it requires less mouse movement. As annotators
using our interface should only say words from a given class vocabulary, we
propose a dedicated task that trains them to do so. Through experiments on COCO
and ILSVRC, we show our method yields high-quality annotations at 2.3x - 14.9x
less annotation time than existing methods.
Backdoor Decomposable Monotone Circuits and their Propagation Complete Encodings
Authors: Petr Kučera, Petr Savický
Date: 2018-11-23 00:00:00
Tasks: Sentence

We describe a compilation language of backdoor decomposable monotone circuits (BDMCs) which generalizes several concepts appearing in the literature, e.g. DNNFs and backdoor trees. A $\mathcal{C}$-BDMC sentence is a monotone circuit which satisfies decomposability property (such as in DNNF) in which the inputs (or leaves) are associated with CNF encodings from a given base class $\mathcal{C}$. We consider the class of propagation complete (PC) encodings as a base class and we show that PC-BDMCs are polynomially equivalent to PC encodings. Additionally, we use this to determine the properties of PC-BDMCs and PC encodings with respect to the knowledge compilation map including the list of efficient operations on the languages.
What is known about Vertex Cover Kernelization?
Authors: Michael R. Fellows, Lars Jaffke, Aliz Izabella Király, Frances A. Rosamond, Mathias Weller
Date: 2018-11-23 00:00:00
Tasks: Survey

We are pleased to dedicate this survey on kernelization of the Vertex Cover problem, to Professor Juraj Hromkovi\v{c} on the occasion of his 60th birthday. The Vertex Cover problem is often referred to as the Drosophila of parameterized complexity. It enjoys a long history. New and worthy perspectives will always be demonstrated first with concrete results here. This survey discusses several research directions in Vertex Cover kernelization. The Barrier Degree of Vertex Cover kernelization is discussed. We have reduction rules that kernelize vertices of small degree, including in this paper new results that reduce graphs almost to minimum degree five. Can this process go on forever? What is the minimum vertex-degree barrier for polynomial-time kernelization? Assuming the Exponential-Time Hypothesis, there is a minimum degree barrier. The idea of automated kernelization is discussed. We here report the first experimental results of an AI-guided branching algorithm for Vertex Cover whose logic seems amenable for application in finding reduction rules to kernelize small-degree vertices. The survey highlights a central open problem in parameterized complexity. Happy Birthday, Juraj!
Joint Neural Architecture Search and Quantization
Authors: Yukang Chen, Gaofeng Meng, Qian Zhang, Xinbang Zhang, Liangchen Song, Shiming Xiang, Chunhong Pan
Date: 2018-11-23 00:00:00
Tasks: Model Compression, Neural Architecture Search, Quantization

Designing neural architectures is a fundamental step in deep learning
applications. As a partner technique, model compression on neural networks has
been widely investigated to gear the needs that the deep learning algorithms
could be run with the limited computation resources on mobile devices.
Currently, both the tasks of architecture design and model compression require
expertise tricks and tedious trials. In this paper, we integrate these two
tasks into one unified framework, which enables the joint architecture search
with quantization (compression) policies for neural networks. This method is
named JASQ. Here our goal is to automatically find a compact neural network
model with high performance that is suitable for mobile devices. Technically, a
multi-objective evolutionary search algorithm is introduced to search the
models under the balance between model size and performance accuracy. In
experiments, we find that our approach outperforms the methods that search only
for architectures or only for quantization policies. 1) Specifically, given
existing networks, our approach can provide them with learning-based
quantization policies, and outperforms their 2 bits, 4 bits, 8 bits, and 16
bits counterparts. It can yield higher accuracies than the float models, for
example, over 1.02% higher accuracy on MobileNet-v1. 2) What is more, under the
balance between model size and performance accuracy, two models are obtained
with joint search of architectures and quantization policies: a high-accuracy
model and a small model, JASQNet and JASQNet-Small that achieves 2.97% error
rate with 0.9 MB on CIFAR-10.
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context
Authors: Antoine Neuraz, Leonardo Campillos Llanos, Anita Burgun, Sophie Rosset
Date: 2018-11-23 00:00:00
Tasks: Data Augmentation, General Classification, intent-classification, Intent Classification, Natural Language Queries, Natural Language Understanding, slot-filling, Slot Filling

In the biomedical domain, the lack of sharable datasets often limit the
possibility of developing natural language processing systems, especially
dialogue applications and natural language understanding models. To overcome
this issue, we explore data generation using templates and terminologies and
data augmentation approaches. Namely, we report our experiments using
paraphrasing and word representations learned on a large EHR corpus with
Fasttext and ELMo, to learn a NLU model without any available dataset. We
evaluate on a NLU task of natural language queries in EHRs divided in
slot-filling and intent classification sub-tasks. On the slot-filling task, we
obtain a F-score of 0.76 with the ELMo representation; and on the
classification task, a mean F-score of 0.71. Our results show that this method
could be used to develop a baseline system.
MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image
Authors: Jinglu Wang, Bo Sun, Yan Lu
Date: 2018-11-23 00:00:00
Tasks: 3D Object Reconstruction, 3D Object Reconstruction From A Single Image, Decoder, Object Reconstruction, regression

In this paper, we address the problem of reconstructing an object's surface
from a single image using generative networks. First, we represent a 3D surface
with an aggregation of dense point clouds from multiple views. Each point cloud
is embedded in a regular 2D grid aligned on an image plane of a viewpoint,
making the point cloud convolution-favored and ordered so as to fit into deep
network architectures. The point clouds can be easily triangulated by
exploiting connectivities of the 2D grids to form mesh-based surfaces. Second,
we propose an encoder-decoder network that generates such kind of multiple
view-dependent point clouds from a single image by regressing their 3D
coordinates and visibilities. We also introduce a novel geometric loss that is
able to interpret discrepancy over 3D surfaces as opposed to 2D projective
planes, resorting to the surface discretization on the constructed meshes. We
demonstrate that the multi-view point regression network outperforms
state-of-the-art methods with a significant improvement on challenging
datasets.
Learning Multiple Defaults for Machine Learning Algorithms
Authors: Florian Pfisterer, Jan N. van Rijn, Philipp Probst, Andreas Müller, Bernd Bischl
Date: 2018-11-23 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work good enough on a wide variety of datasets. To address this problem, different automatic hyperparameter configuration algorithms have been proposed, which select an optimal configuration per dataset. This principled approach usually improves performance but adds additional algorithmic complexity and computational costs to the training procedure. As an alternative to this, we propose learning a set of complementary default values from a large database of prior empirical results. Selecting an appropriate configuration on a new dataset then requires only a simple, efficient and embarrassingly parallel search over this set. We demonstrate the effectiveness and efficiency of the approach we propose in comparison to random search and Bayesian Optimization.
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Authors: Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé, Nils Thuerey
Date: 2018-11-23 00:00:00
Tasks: Image Super-Resolution, Motion Compensation, Super-Resolution, Translation, Video Generation, Video Super-Resolution

Our work explores temporal self-supervision for GAN-based video generation tasks. While adversarial training successfully yields generative models for a variety of areas, temporal relationships in the generated data are much less explored. Natural temporal changes are crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation. For the former, state-of-the-art methods often favor simpler norm losses such as $L^2$ over adversarial training. However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail. For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies. In contrast, we focus on improving learning objectives and propose a temporally self-supervised algorithm. For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail. We also propose a novel Ping-Pong loss to improve the long-term temporal consistency. It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features. Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution. A series of user studies confirm the rankings computed with these metrics. Code, data, models, and results are provided at https://github.com/thunil/TecoGAN. The project page https://ge.in.tum.de/publications/2019-tecogan-chu/ contains supplemental materials.
Explicit Interaction Model towards Text Classification
Authors: Cunxiao Du, Zhaozheng Chin, Fuli Feng, Lei Zhu, Tian Gan, Liqiang Nie
Date: 2018-11-23 00:00:00
Tasks: Classification, General Classification, model, Multi Class Text Classification, Sentiment Analysis, text-classification, Text Classification

Text classification is one of the fundamental tasks in natural language
processing. Recently, deep neural networks have achieved promising performance
in the text classification task compared to shallow models. Despite of the
significance of deep models, they ignore the fine-grained (matching signals
between words and classes) classification clues since their classifications
mainly rely on the text-level representations. To address this problem, we
introduce the interaction mechanism to incorporate word-level matching signals
into the text classification task. In particular, we design a novel framework,
EXplicit interAction Model (dubbed as EXAM), equipped with the interaction
mechanism. We justified the proposed approach on several benchmark datasets
including both multi-label and multi-class text classification tasks. Extensive
experimental results demonstrate the superiority of the proposed method. As a
byproduct, we have released the codes and parameter settings to facilitate
other researches.
On the Importance of Strong Baselines in Bayesian Deep Learning
Authors: Jishnu Mukhoti, Pontus Stenetorp, Yarin Gal
Date: 2018-11-23 00:00:00
Tasks: Deep Learning

Like all sub-fields of machine learning Bayesian Deep Learning is driven by
empirical validation of its theoretical proposals. Given the many aspects of an
experiment it is always possible that minor or even major experimental flaws
can slip by both authors and reviewers. One of the most popular experiments
used to evaluate approximate inference techniques is the regression experiment
on UCI datasets. However, in this experiment, models which have been trained to
convergence have often been compared with baselines trained only for a fixed
number of iterations. We find that a well-established baseline, Monte Carlo
dropout, when evaluated under the same experimental settings shows significant
improvements. In fact, the baseline outperforms or performs competitively with
methods that claimed to be superior to the very same baseline method when they
were introduced. Hence, by exposing this flaw in experimental procedure, we
highlight the importance of using identical experimental setups to evaluate,
compare, and benchmark methods in Bayesian Deep Learning.
High-Dimensional Robust Mean Estimation in Nearly-Linear Time
Authors: Yu Cheng, Ilias Diakonikolas, Rong Ge
Date: 2018-11-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the fundamental problem of high-dimensional mean estimation in a
robust model where a constant fraction of the samples are adversarially
corrupted. Recent work gave the first polynomial time algorithms for this
problem with dimension-independent error guarantees for several families of
structured distributions.
  In this work, we give the first nearly-linear time algorithms for
high-dimensional robust mean estimation. Specifically, we focus on
distributions with (i) known covariance and sub-gaussian tails, and (ii)
unknown bounded covariance. Given $N$ samples on $\mathbb{R}^d$, an
$\epsilon$-fraction of which may be arbitrarily corrupted, our algorithms run
in time $\tilde{O}(Nd) / \mathrm{poly}(\epsilon)$ and approximate the true mean
within the information-theoretically optimal error, up to constant factors.
Previous robust algorithms with comparable error guarantees have running times
$\tilde{\Omega}(N d^2)$, for $\epsilon = \Omega(1)$.
  Our algorithms rely on a natural family of SDPs parameterized by our current
guess $\nu$ for the unknown mean $\mu^\star$. We give a win-win analysis
establishing the following: either a near-optimal solution to the primal SDP
yields a good candidate for $\mu^\star$ -- independent of our current guess
$\nu$ -- or the dual SDP yields a new guess $\nu'$ whose distance from
$\mu^\star$ is smaller by a constant factor. We exploit the special structure
of the corresponding SDPs to show that they are approximately solvable in
nearly-linear time. Our approach is quite general, and we believe it can also
be applied to obtain nearly-linear time algorithms for other high-dimensional
robust learning problems.
Fine Grained Classification of Personal Data Entities
Authors: Riddhiman Dasgupta, Balaji Ganesan, Aswin Kannan, Berthold Reinwald, Arun Kumar
Date: 2018-11-23 00:00:00
Tasks: Classification, General Classification

Entity Type Classification can be defined as the task of assigning category
labels to entity mentions in documents. While neural networks have recently
improved the classification of general entity mentions, pattern matching and
other systems continue to be used for classifying personal data entities (e.g.
classifying an organization as a media company or a government institution for
GDPR, and HIPAA compliance). We propose a neural model to expand the class of
personal data entities that can be classified at a fine grained level, using
the output of existing pattern matching systems as additional contextual
features. We introduce new resources, a personal data entities hierarchy with
134 types, and two datasets from the Wikipedia pages of elected representatives
and Enron emails. We hope these resource will aid research in the area of
personal data discovery, and to that effect, we provide baseline results on
these datasets, and compare our method with state of the art models on
OntoNotes dataset.
Learning pronunciation from a foreign language in speech synthesis networks
Authors: Young-Gun Lee, Suwon Shon, Taesu Kim
Date: 2018-11-23 00:00:00
Tasks: Speech Synthesis

Although there are more than 6,500 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflects their native language's characteristics. This motivates us to investigate how the speech synthesis network learns the pronunciation from datasets from different languages. In this study, we are interested in analyzing and taking advantage of multilingual speech synthesis network. First, we train the speech synthesis network bilingually in English and Korean and analyze how the network learns the relations of phoneme pronunciation between the languages. Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages. Consequently, the trained networks can synthesize the English speakers' Korean speech and vice versa. Using this result, we propose a training framework to utilize information from a different language. To be specific, we pre-train a speech synthesis network using datasets from both high-resource language and low-resource language, then we fine-tune the network using the low-resource language dataset. Finally, we conducted more simulations on 10 different languages to show it is generally extendable to other languages.
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Authors: Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
Date: 2018-11-23 00:00:00
Tasks: Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

Humans convey their intentions through the usage of both verbal and nonverbal
behaviors during face-to-face communication. Speaker intentions often vary
dynamically depending on different nonverbal contexts, such as vocal patterns
and facial expressions. As a result, when modeling human language, it is
essential to not only consider the literal meaning of the words but also the
nonverbal contexts in which these words appear. To better model human language,
we first model expressive nonverbal representations by analyzing the
fine-grained visual and acoustic patterns that occur during word segments. In
addition, we seek to capture the dynamic nature of nonverbal intents by
shifting word representations based on the accompanying nonverbal behaviors. To
this end, we propose the Recurrent Attended Variation Embedding Network (RAVEN)
that models the fine-grained structure of nonverbal subword sequences and
dynamically shifts word representations based on nonverbal cues. Our proposed
model achieves competitive performance on two publicly available datasets for
multimodal sentiment analysis and emotion recognition. We also visualize the
shifted word representations in different nonverbal contexts and summarize
common patterns regarding multimodal variations of word representations.
Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution
Authors: Yang You, Yujing Lou, Qi Liu, Yu-Wing Tai, Lizhuang Ma, Cewu Lu, Weiming Wang
Date: 2018-11-23 00:00:00
Tasks: 3D Feature Matching, Data Augmentation

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.
A Sufficient Condition for Convergences of Adam and RMSProp
Authors: Fangyu Zou, Li Shen, Zequn Jie, Weizhong Zhang, Wei Liu
Date: 2018-11-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Stochastic Optimization

Adam and RMSProp are two of the most influential adaptive stochastic algorithms for training deep neural networks, which have been pointed out to be divergent even in the convex setting via a few simple counterexamples. Many attempts, such as decreasing an adaptive learning rate, adopting a big batch size, incorporating a temporal decorrelation technique, seeking an analogous surrogate, etc., have been tried to promote Adam/RMSProp-type algorithms to converge. In contrast with existing approaches, we introduce an alternative easy-to-check sufficient condition, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam/RMSProp for solving large-scale non-convex stochastic optimization. Moreover, we show that the convergences of several variants of Adam, such as AdamNC, AdaEMA, etc., can be directly implied via the proposed sufficient condition in the non-convex setting. In addition, we illustrate that Adam is essentially a specifically weighted AdaGrad with exponential moving average momentum, which provides a novel perspective for understanding Adam and RMSProp. This observation coupled with this sufficient condition gives much deeper interpretations on their divergences. At last, we validate the sufficient condition by applying Adam and RMSProp to tackle a certain counterexample and train deep neural networks. Numerical results are exactly in accord with our theoretical analysis.
Learning to Discover, Ground and Use Words with Segmental Neural Language Models
Authors: Kazuya Kawakami, Chris Dyer, Phil Blunsom
Date: 2018-11-23 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling, Segmentation

We propose a segmental neural language model that combines the generalization power of neural networks with the ability to discover word-like units that are latent in unsegmented character sequences. In contrast to previous segmentation models that treat word segmentation as an isolated task, our model unifies word discovery, learning how words fit together to form sentences, and, by conditioning the model on visual context, how words' meanings ground in representations of non-linguistic modalities. Experiments show that the unconditional model learns predictive distributions better than character LSTM models, discovers words competitively with nonparametric Bayesian word segmentation models, and that modeling language conditional on visual context improves performance on both.