pwc-archive/papers-with-abstracts huggingface dataset:
A Variational U-Net for Conditional Appearance and Shape Generation
Authors: Patrick Esser, Ekaterina Sutter, Björn Ommer
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Conditional Image Generation, Image Generation

Deep generative models have demonstrated great performance in image
synthesis. However, results deteriorate in case of spatial deformations, since
they generate images of objects directly, rather than modeling the intricate
interplay of their inherent shape and appearance. We present a conditional
U-Net for shape-guided image generation, conditioned on the output of a
variational autoencoder for appearance. The approach is trained end-to-end on
images, without requiring samples of the same object with varying pose or
appearance. Experiments show that the model enables conditional image
generation and transfer. Therefore, either shape or appearance can be retained
from a query image, while freely altering the other. Moreover, appearance can
be sampled due to its stochastic latent representation, while preserving shape.
In quantitative and qualitative experiments on COCO, DeepFashion, shoes,
Market-1501 and handbags, the approach demonstrates significant improvements
over the state-of-the-art.
Self-Attention with Relative Position Representations
Authors: Peter Shaw, Jakob Uszkoreit, Ashish Vaswani
Date: 2018-03-06 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, Position, Translation

Relying entirely on an attention mechanism, the Transformer introduced by
Vaswani et al. (2017) achieves state-of-the-art results for machine
translation. In contrast to recurrent and convolutional neural networks, it
does not explicitly model relative or absolute position information in its
structure. Instead, it requires adding representations of absolute positions to
its inputs. In this work we present an alternative approach, extending the
self-attention mechanism to efficiently consider representations of the
relative positions, or distances between sequence elements. On the WMT 2014
English-to-German and English-to-French translation tasks, this approach yields
improvements of 1.3 BLEU and 0.3 BLEU over absolute position representations,
respectively. Notably, we observe that combining relative and absolute position
representations yields no further improvement in translation quality. We
describe an efficient implementation of our method and cast it as an instance
of relation-aware self-attention mechanisms that can generalize to arbitrary
graph-labeled inputs.
Online algorithms for POMDPs with continuous state, action, and observation spaces
Authors: Zachary Sunberg, Mykel Kochenderfer
Date: 2017-09-18 00:00:00

Online solvers for partially observable Markov decision processes have been
applied to problems with large discrete state spaces, but continuous state,
action, and observation spaces remain a challenge. This paper begins by
investigating double progressive widening (DPW) as a solution to this
challenge. However, we prove that this modification alone is not sufficient
because the belief representations in the search tree collapse to a single
particle causing the algorithm to converge to a policy that is suboptimal
regardless of the computation time. This paper proposes and evaluates two new
algorithms, POMCPOW and PFT-DPW, that overcome this deficiency by using
weighted particle filtering. Simulation results show that these modifications
allow the algorithms to be successful where previous approaches fail.
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
Authors: Swami Sankaranarayanan, Yogesh Balaji, Carlos D. Castillo, Rama Chellappa
Date: 2017-04-06 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Generative Adversarial Network, Object Recognition

Domain Adaptation is an actively researched problem in Computer Vision. In
this work, we propose an approach that leverages unsupervised data to bring the
source and target distributions closer in a learned joint feature space. We
accomplish this by inducing a symbiotic relationship between the learned
embedding and a generative adversarial network. This is in contrast to methods
which use the adversarial framework for realistic data generation and
retraining deep models with such data. We demonstrate the strength and
generality of our approach by performing experiments on three different tasks
with varying levels of difficulty: (1) Digit classification (MNIST, SVHN and
USPS datasets) (2) Object recognition using OFFICE dataset and (3) Domain
adaptation from synthetic to real data. Our method achieves state-of-the art
performance in most experimental settings and by far the only GAN-based method
that has been shown to work well across different datasets such as OFFICE and
DIGITS.
An efficient CNN for spectral reconstruction from RGB images
Authors: Yigit Baran Can, Radu Timofte
Date: 2018-04-12 00:00:00
Tasks: Image Super-Resolution, Spectral Reconstruction, Spectral Super-Resolution, Super-Resolution

Recently, the example-based single image spectral reconstruction from RGB
images task, aka, spectral super-resolution was approached by means of deep
learning by Galliani et al. The proposed very deep convolutional neural network
(CNN) achieved superior performance on recent large benchmarks. However,
Aeschbacher et al showed that comparable performance can be achieved by shallow
learning method based on A+, a method introduced for image super-resolution by
Timofte et al. In this paper, we propose a moderately deep CNN model and
substantially improve the reported performance on three spectral reconstruction
standard benchmarks: ICVL, CAVE, and NUS.
Fast Counting in Machine Learning Applications
Authors: Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt, Grant Iraci, Jaroslaw Zola
Date: 2018-04-12 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency

We propose scalable methods to execute counting queries in machine learning
applications. To achieve memory and computational efficiency, we abstract
counting queries and their context such that the counts can be aggregated as a
stream. We demonstrate performance and scalability of the resulting approach on
random queries, and through extensive experimentation using Bayesian networks
learning and association rule mining. Our methods significantly outperform
commonly used ADtrees and hash tables, and are practical alternatives for
processing large-scale data.
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
Authors: Colin Lockard, Xin Luna Dong, Arash Einolghozati, Prashant Shiralkar
Date: 2018-04-12 00:00:00
Tasks: Relation, Relation Extraction

The web contains countless semi-structured websites, which can be a rich
source of information for populating knowledge bases. Existing methods for
extracting relations from the DOM trees of semi-structured webpages can achieve
high precision and recall only when manual annotations for each website are
available. Although there have been efforts to learn extractors from
automatically-generated labels, these methods are not sufficiently robust to
succeed in settings with complex schemas and information-rich websites.
  In this paper we present a new method for automatic extraction from
semi-structured websites based on distant supervision. We automatically
generate training labels by aligning an existing knowledge base with a web page
and leveraging the unique structural characteristics of semi-structured
websites. We then train a classifier based on the potentially noisy and
incomplete labels to predict new relation instances. Our method can compete
with annotation-based techniques in the literature in terms of extraction
quality. A large-scale experiment on over 400,000 pages from dozens of
multi-lingual long-tail websites harvested 1.25 million facts at a precision of
90%.
Contextual Salience for Fast and Accurate Sentence Vectors
Authors: Eric Zelikman, Richard Socher
Date: 2018-03-22 00:00:00
Tasks: Document Summarization, General Classification, Sentence, Sentence Embeddings, Sentiment Analysis, Sentiment Classification

Unsupervised vector representations of sentences or documents are a major building block for many language tasks such as sentiment classification. However, current methods are uninterpretable and slow or require large training datasets. Recent word vector-based proposals implicitly assume that distances in a word embedding space are equally important, regardless of context. We introduce contextual salience (CoSal), a measure of word importance that uses the distribution of context vectors to normalize distances and weights. CoSal relies on the insight that unusual word vectors disproportionately affect phrase vectors. A bag-of-words model with CoSal-based weights produces accurate unsupervised sentence or document representations for classification, requiring little computation to evaluate and only a single covariance calculation to ``train." CoSal supports small contexts, out-of context words and outperforms SkipThought on most benchmarks, beats tf-idf on all benchmarks, and is competitive with the unsupervised state-of-the-art.
Universal Sentence Encoder
Authors: Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Date: 2018-03-29 00:00:00
Tasks: Conversational Response Selection, Semantic Textual Similarity, Sentence, Sentence Embeddings, Sentiment Analysis, Subjectivity Analysis, Text Classification, Transfer Learning, Word Embeddings

We present models for encoding sentences into embedding vectors that
specifically target transfer learning to other NLP tasks. The models are
efficient and result in accurate performance on diverse transfer tasks. Two
variants of the encoding models allow for trade-offs between accuracy and
compute resources. For both variants, we investigate and report the
relationship between model complexity, resource consumption, the availability
of transfer task training data, and task performance. Comparisons are made with
baselines that use word level transfer learning via pretrained word embeddings
as well as baselines do not use any transfer learning. We find that transfer
learning using sentence embeddings tends to outperform word level transfer.
With transfer learning via sentence embeddings, we observe surprisingly good
performance with minimal amounts of supervised training data for a transfer
task. We obtain encouraging results on Word Embedding Association Tests (WEAT)
targeted at detecting model bias. Our pre-trained sentence encoding models are
made freely available for download and on TF Hub.
Causal Inference via Kernel Deviance Measures
Authors: Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Date: 2018-04-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Discovery, Causal Inference, Time Series, Time Series Analysis

Discovering the causal structure among a set of variables is a fundamental
problem in many areas of science. In this paper, we propose Kernel Conditional
Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery
method based on purely observational data. From a novel interpretation of the
notion of asymmetry between cause and effect, we derive a corresponding
asymmetry measure using the framework of reproducing kernel Hilbert spaces.
Based on this, we propose three decision rules for causal discovery. We
demonstrate the wide applicability of our method across a range of diverse
synthetic datasets. Furthermore, we test our method on real-world time series
data and the real-world benchmark dataset Tubingen Cause-Effect Pairs where we
outperform existing state-of-the-art methods.
Connectivity in Random Annulus Graphs and the Geometric Block Model
Authors: Sainyam Galhotra, Arya Mazumdar, Soumyabrata Pal, Barna Saha
Date: 2018-04-12 00:00:00
Tasks: Community Detection, Stochastic Block Model

We provide new connectivity results for {\em vertex-random graphs} or {\em random annulus graphs} which are significant generalizations of random geometric graphs. Random geometric graphs (RGG) are one of the most basic models of random graphs for spatial networks proposed by Gilbert in 1961, shortly after the introduction of the Erd\H{o}s-R\'{en}yi random graphs. They resemble social networks in many ways (e.g. by spontaneously creating cluster of nodes with high modularity). The connectivity properties of RGG have been studied since its introduction, and analyzing them has been significantly harder than their Erd\H{o}s-R\'{en}yi counterparts due to correlated edge formation. Our next contribution is in using the connectivity of random annulus graphs to provide necessary and sufficient conditions for efficient recovery of communities for {\em the geometric block model} (GBM). The GBM is a probabilistic model for community detection defined over an RGG in a similar spirit as the popular {\em stochastic block model}, which is defined over an Erd\H{o}s-R\'{en}yi random graph. The geometric block model inherits the transitivity properties of RGGs and thus models communities better than a stochastic block model. However, analyzing them requires fresh perspectives as all prior tools fail due to correlation in edge formation. We provide a simple and efficient algorithm that can recover communities in GBM exactly with high probability in the regime of connectivity.
Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm
Authors: Amirhossein Javaheri, Hadi Zayyani, Mario A. T. Figueiredo, Farrokh Marvasti
Date: 2018-04-12 00:00:00

This paper investigates the problem of sparse signal recovery in the presence
of additive impulsive noise. The heavytailed impulsive noise is well modelled
with stable distributions. Since there is no explicit formulation for the
probability density function of $S\alpha S$ distribution, alternative
approximations like Generalized Gaussian Distribution (GGD) are used which
impose $\ell_p$-norm fidelity on the residual error. In this paper, we exploit
a Continuous Mixed Norm (CMN) for robust sparse recovery instead of
$\ell_p$-norm. We show that in blind conditions, i.e., in case where the
parameters of noise distribution are unknown, incorporating CMN can lead to
near optimal recovery. We apply Alternating Direction Method of Multipliers
(ADMM) for solving the problem induced by utilizing CMN for robust sparse
recovery. In this approach, CMN is replaced with a surrogate function and
Majorization-Minimization technique is incorporated to solve the problem.
Simulation results confirm the efficiency of the proposed method compared to
some recent algorithms in the literature for impulsive noise robust sparse
recovery.
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Authors: Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B. Tenenbaum, William T. Freeman
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Reconstruction, 3D Shape Modeling, 3D Shape Reconstruction, Multi-Task Learning, Pose Estimation, Retrieval, Viewpoint Estimation

We study 3D shape modeling from a single image and make contributions to it
in three aspects. First, we present Pix3D, a large-scale benchmark of diverse
image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications
in shape-related tasks including reconstruction, retrieval, viewpoint
estimation, etc. Building such a large-scale dataset, however, is highly
challenging; existing datasets either contain only synthetic data, or lack
precise alignment between 2D images and 3D shapes, or only have a small number
of images. Second, we calibrate the evaluation criteria for 3D shape
reconstruction through behavioral studies, and use them to objectively and
systematically benchmark cutting-edge reconstruction algorithms on Pix3D.
Third, we design a novel model that simultaneously performs 3D reconstruction
and pose estimation; our multi-task learning approach achieves state-of-the-art
performance on both tasks.
Deformation Aware Image Compression
Authors: Tamar Rott Shaham, Tomer Michaeli
Date: 2018-04-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Compression, SSIM

Lossy compression algorithms aim to compactly encode images in a way which
enables to restore them with minimal error. We show that a key limitation of
existing algorithms is that they rely on error measures that are extremely
sensitive to geometric deformations (e.g. SSD, SSIM). These force the encoder
to invest many bits in describing the exact geometry of every fine detail in
the image, which is obviously wasteful, because the human visual system is
indifferent to small local translations. Motivated by this observation, we
propose a deformation-insensitive error measure that can be easily incorporated
into any existing compression scheme. As we show, optimal compression under our
criterion involves slightly deforming the input image such that it becomes more
"compressible". Surprisingly, while these small deformations are barely
noticeable, they enable the CODEC to preserve details that are otherwise
completely lost. Our technique uses the CODEC as a "black box", thus allowing
simple integration with arbitrary compression methods. Extensive experiments,
including user studies, confirm that our approach significantly improves the
visual quality of many CODECs. These include JPEG, JPEG2000, WebP, BPG, and a
recent deep-net method.
On Identifying Anomalies in Tor Usage with Applications in Detecting Internet Censorship
Authors: Joss Wright, Alexander Darer, Oliver Farnan
Date: 2015-07-21 00:00:00
Tasks: Anomaly Detection, Blocking

We develop a means to detect ongoing per-country anomalies in the daily usage
metrics of the Tor anonymous communication network, and demonstrate the
applicability of this technique to identifying likely periods of internet
censorship and related events. The presented approach identifies contiguous
anomalous periods, rather than daily spikes or drops, and allows anomalies to
be ranked according to deviation from expected behaviour.
  The developed method is implemented as a running tool, with outputs published
daily by mailing list. This list highlights per-country anomalous Tor usage,
and produces a daily ranking of countries according to the level of detected
anomalous behaviour. This list has been active since August 2016, and is in use
by a number of individuals, academics, and NGOs as an early warning system for
potential censorship events.
  We focus on Tor, however the presented approach is more generally applicable
to usage data of other services, both individually and in combination. We
demonstrate that combining multiple data sources allows more specific
identification of likely Tor blocking events. We demonstrate the our approach
in comparison to existing anomaly detection tools, and against both known
historical internet censorship events and synthetic datasets. Finally, we
detail a number of significant recent anomalous events and behaviours
identified by our tool.
Lazily Adapted Constant Kinky Inference for Nonparametric Regression and Model-Reference Adaptive Control
Authors: Jan-Peter Calliess
Date: 2016-12-31 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes, regression

Techniques known as Nonlinear Set Membership prediction, Lipschitz Interpolation or Kinky Inference are approaches to machine learning that utilise presupposed Lipschitz properties to compute inferences over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori they offer convergence guarantees as well as bounds around the predictions. Considering a more general setting that builds on Hoelder continuity relative to pseudo-metrics, we propose an online method for estimating the Hoelder constant online from function value observations that possibly are corrupted by bounded observational errors. Utilising this to compute adaptive parameters within a kinky inference rule gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function in the limit of increasingly dense data to within a worst-case error bound that depends on the level of observational uncertainty. We apply our method in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks. For discrete-time systems, we provide guarantees on the tracking success of our learning-based controllers both for the batch and the online learning setting.
Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks
Authors: Carlo Vittorio Cannistraci, Alessandro Muscoloni
Date: 2018-04-12 00:00:00
Tasks: Clustering, Community Detection

Affinity propagation is one of the most effective unsupervised pattern
recognition algorithms for data clustering in high-dimensional feature space.
However, the numerous attempts to test its performance for community detection
in complex networks have been attaining results very far from the state of the
art methods such as Infomap and Louvain. Yet, all these studies agreed that the
crucial problem is to convert the unweighted network topology in a
'smart-enough' node dissimilarity matrix that is able to properly address the
message passing procedure behind affinity propagation clustering. Here we
introduce a conceptual innovation and we discuss how to leverage network latent
geometry notions in order to design dissimilarity matrices for affinity
propagation community detection. Our results demonstrate that the latent
geometry inspired dissimilarity measures we design bring affinity propagation
to equal or outperform current state of the art methods for community
detection. These findings are solidly proven considering both synthetic
'realistic' networks (with known ground-truth communities) and real networks
(with community metadata), even when the data structure is corrupted by noise
artificially induced by missing or spurious connectivity.
Towards integrating spatial localization in convolutional neural networks for brain image segmentation
Authors: Pierre-Antoine Ganaye, Michaël Sdika, Hugues Benoit-Cattin
Date: 2018-04-12 00:00:00
Tasks: Brain Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation is an established while rapidly evolving field in
medical imaging. In this paper we focus on the segmentation of brain Magnetic
Resonance Images (MRI) into cerebral structures using convolutional neural
networks (CNN). CNNs achieve good performance by finding effective high
dimensional image features describing the patch content only. In this work, we
propose different ways to introduce spatial constraints into the network to
further reduce prediction inconsistencies.
  A patch based CNN architecture was trained, making use of multiple scales to
gather contextual information. Spatial constraints were introduced within the
CNN through a distance to landmarks feature or through the integration of a
probability atlas. We demonstrate experimentally that using spatial information
helps to reduce segmentation inconsistencies.