pwc-archive/papers-with-abstracts huggingface dataset:
Minimizing Supervision for Free-space Segmentation
Authors: Satoshi Tsutsui, Tommi Kerola, Shunta Saito, David J. Crandall
Date: 2017-11-16 00:00:00
Tasks: Autonomous Driving, Autonomous Navigation, Segmentation, Semantic Segmentation, Weakly supervised segmentation

Identifying "free-space," or safely driveable regions in the scene ahead, is
a fundamental task for autonomous navigation. While this task can be addressed
using semantic segmentation, the manual labor involved in creating pixelwise
annotations to train the segmentation model is very costly. Although weakly
supervised segmentation addresses this issue, most methods are not designed for
free-space. In this paper, we observe that homogeneous texture and location are
two key characteristics of free-space, and develop a novel, practical framework
for free-space segmentation with minimal human supervision. Our experiments
show that our framework performs better than other weakly supervised methods
while using less supervision. Our work demonstrates the potential for
performing free-space segmentation without tedious and costly manual
annotation, which will be important for adapting autonomous driving systems to
different types of vehicles and environments
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
Authors: Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiao-Li Li
Date: 2018-04-12 00:00:00
Tasks: Decision Making

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e., a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine-grained interactions between group members. In our MoSAN model, each sub-attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub-Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state-of-the-art performance but also improves standard baselines by a considerable margin.
TransRev: Modeling Reviews as Translations from Users to Items
Authors: Alberto Garcia-Duran, Roberto Gonzalez, Daniel Onoro-Rubio, Mathias Niepert, Hui Li
Date: 2018-01-30 00:00:00
Tasks: Product Recommendation, Recommendation Systems, regression, Relational Reasoning, Sentiment Analysis

The text of a review expresses the sentiment a customer has towards a
particular product. This is exploited in sentiment analysis where machine
learning models are used to predict the review score from the text of the
review. Furthermore, the products costumers have purchased in the past are
indicative of the products they will purchase in the future. This is what
recommender systems exploit by learning models from purchase information to
predict the items a customer might be interested in. We propose TransRev, an
approach to the product recommendation problem that integrates ideas from
recommender systems, sentiment analysis, and multi-relational learning into a
joint learning objective. TransRev learns vector representations for users,
items, and reviews. The embedding of a review is learned such that (a) it
performs well as input feature of a regression model for sentiment prediction;
and (b) it always translates the reviewer embedding to the embedding of the
reviewed items. This allows TransRev to approximate a review embedding at test
time as the difference of the embedding of each item and the user embedding.
The approximated review embedding is then used with the regression model to
predict the review score for each item. TransRev outperforms state of the art
recommender systems on a large number of benchmark data sets. Moreover, it is
able to retrieve, for each user and item, the review text from the training set
whose embedding is most similar to the approximated review embedding.
Visualizing the Feature Importance for Black Box Models
Authors: Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl
Date: 2018-04-18 00:00:00
Tasks: Feature Importance

In recent years, a large amount of model-agnostic methods to improve the
transparency, trustability and interpretability of machine learning models have
been developed. We introduce local feature importance as a local version of a
recent model-agnostic global feature importance method. Based on local feature
importance, we propose two visual tools: partial importance (PI) and individual
conditional importance (ICI) plots which visualize how changes in a feature
affect the model performance on average, as well as for individual
observations. Our proposed methods are related to partial dependence (PD) and
individual conditional expectation (ICE) plots, but visualize the expected
(conditional) feature importance instead of the expected (conditional)
prediction. Furthermore, we show that averaging ICI curves across observations
yields a PI curve, and integrating the PI curve with respect to the
distribution of the considered feature results in the global feature
importance. Another contribution of our paper is the Shapley feature
importance, which fairly distributes the overall performance of a model among
the features according to the marginal contributions and which can be used to
compare the feature importance across different models.
Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition Optimization
Authors: Tianyi Lin, Chenyou Fan, Mengdi Wang
Date: 2018-02-07 00:00:00

We consider the nonsmooth convex composition optimization problem where the objective is a composition of two finite-sum functions and analyze stochastic compositional variance reduced gradient (SCVRG) methods for them. SCVRG and its variants have recently drawn much attention given their edge over stochastic compositional gradient descent (SCGD); but the theoretical analysis exclusively assumes strong convexity of the objective, which excludes several important examples such as Lasso, logistic regression, principle component analysis and deep neural nets. In contrast, we prove non-asymptotic incremental first-order oracle (IFO) complexity of SCVRG or its novel variants for nonsmooth convex composition optimization and show that they are provably faster than SCGD and gradient descent. More specifically, our method achieves the total IFO complexity of $O\left((m+n)\log\left(1/\epsilon\right)+1/\epsilon^3\right)$ which improves that of $O\left(1/\epsilon^{3.5}\right)$ and $O\left((m+n)/\sqrt{\epsilon}\right)$ obtained by SCGD and accelerated gradient descent (AGD) respectively. Experimental results confirm that our methods outperform several existing methods, e.g., SCGD and AGD, on sparse mean-variance optimization problem.
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
Authors: Matt Post, David Vilar
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Translation

The end-to-end nature of neural machine translation (NMT) removes many ways
of manually guiding the translation process that were available in older
paradigms. Recent work, however, has introduced a new capability: lexically
constrained or guided decoding, a modification to beam search that forces the
inclusion of pre-specified words and phrases in the output. However, while
theoretically sound, existing approaches have computational complexities that
are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017)
in the number of constraints. We present a algorithm for lexically constrained
decoding with a complexity of O(1) in the number of constraints. We demonstrate
the algorithms remarkable ability to properly place these constraints, and use
it to explore the shaky relationship between model and BLEU scores. Our
implementation is available as part of Sockeye.
PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation
Authors: Ana García del Molino, Michael Gygli
Date: 2018-04-18 00:00:00
Tasks: Highlight Detection

Highlight detection models are typically trained to identify cues that make
visual content appealing or interesting for the general public, with the
objective of reducing a video to such moments. However, the "interestingness"
of a video segment or image is subjective. Thus, such highlight models provide
results of limited relevance for the individual user. On the other hand,
training one model per user is inefficient and requires large amounts of
personal information which is typically not available. To overcome these
limitations, we present a global ranking model which conditions on each
particular user's interests. Rather than training one model per user, our model
is personalized via its inputs, which allows it to effectively adapt its
predictions, given only a few user-specific examples. To train this model, we
create a large-scale dataset of users and the GIFs they created, giving us an
accurate indication of their interests. Our experiments show that using the
user history substantially improves the prediction accuracy. On our test set of
850 videos, our model improves the recall by 8% with respect to generic
highlight detectors. Furthermore, our method proves more precise than the
user-agnostic baselines even with just one person-specific example.
Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling
Authors: Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu
Date: 2017-09-26 00:00:00
Proceeding: CVPR 2018 6

This paper proposes a multi-grid method for learning energy-based generative ConvNet models of images. For each grid, we learn an energy-based probabilistic model where the energy function is defined by a bottom-up convolutional neural network (ConvNet or CNN). Learning such a model requires generating synthesized examples from the model. Within each iteration of our learning algorithm, for each observed training image, we generate synthesized images at multiple grids by initializing the finite-step MCMC sampling from a minimal 1 x 1 version of the training image. The synthesized image at each subsequent grid is obtained by a finite-step MCMC initialized from the synthesized image generated at the previous coarser grid. After obtaining the synthesized examples, the parameters of the models at multiple grids are updated separately and simultaneously based on the differences between synthesized and observed examples. We show that this multi-grid method can learn realistic energy-based generative ConvNet models, and it outperforms the original contrastive divergence (CD) and persistent CD.
Real-Time Seamless Single Shot 6D Object Pose Prediction
Authors: Bugra Tekin, Sudipta N. Sinha, Pascal Fua
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: 6D Pose Estimation using RGB, Drone Pose Estimation, GPU, Object, Pose Estimation, Pose Prediction, Prediction

We propose a single-shot approach for simultaneously detecting an object in
an RGB image and predicting its 6D pose without requiring multiple stages or
having to examine multiple hypotheses. Unlike a recently proposed single-shot
technique for this task (Kehl et al., ICCV'17) that only predicts an
approximate 6D pose that must then be refined, ours is accurate enough not to
require additional post-processing. As a result, it is much faster - 50 fps on
a Titan X (Pascal) GPU - and more suitable for real-time processing. The key
component of our method is a new CNN architecture inspired by the YOLO network
design that directly predicts the 2D image locations of the projected vertices
of the object's 3D bounding box. The object's 6D pose is then estimated using a
PnP algorithm.
  For single object and multiple object pose estimation on the LINEMOD and
OCCLUSION datasets, our approach substantially outperforms other recent
CNN-based approaches when they are all used without post-processing. During
post-processing, a pose refinement step can be used to boost the accuracy of
the existing methods, but at 10 fps or less, they are much slower than our
method.
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Authors: Julie Yixuan Zhu, Chao Zhang, Huichu Zhang, Shi Zhi, Victor O. K. Li, Jiawei Han, Yu Zheng
Date: 2016-10-22 00:00:00

Many countries are suffering from severe air pollution. Understanding how
different air pollutants accumulate and propagate is critical to making
relevant public policies. In this paper, we use urban big data (air quality
data and meteorological data) to identify the \emph{spatiotemporal (ST) causal
pathways} for air pollutants. This problem is challenging because: (1) there
are numerous noisy and low-pollution periods in the raw air quality data, which
may lead to unreliable causality analysis, (2) for large-scale data in the ST
space, the computational complexity of constructing a causal structure is very
high, and (3) the \emph{ST causal pathways} are complex due to the interactions
of multiple pollutants and the influence of environmental factors. Therefore,
we present \emph{p-Causality}, a novel pattern-aided causality analysis
approach that combines the strengths of \emph{pattern mining} and
\emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST
causal pathways}. First, \emph{Pattern mining} helps suppress the noise by
capturing frequent evolving patterns (FEPs) of each monitoring sensor, and
greatly reduce the complexity by selecting the pattern-matched sensors as
"causers". Then, \emph{Bayesian learning} carefully encodes the local and ST
causal relations with a Gaussian Bayesian network (GBN)-based graphical model,
which also integrates environmental influences to minimize biases in the final
results. We evaluate our approach with three real-world data sets containing
982 air quality sensors, in three regions of China from 01-Jun-2013 to
19-Dec-2015. Results show that our approach outperforms the traditional causal
structure learning methods in time efficiency, inference accuracy and
interpretability.
Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines
Authors: Fenggen Yu, Yan Zhang, Kai Xu, Ali Mahdavi-Amiri, Hao Zhang
Date: 2018-04-18 00:00:00
Tasks: Clustering

We present a semi-supervised co-analysis method for learning 3D shape styles from projected feature lines, achieving style patch localization with only weak supervision. Given a collection of 3D shapes spanning multiple object categories and styles, we perform style co-analysis over projected feature lines of each 3D shape and then backproject the learned style features onto the 3D shapes. Our core analysis pipeline starts with mid-level patch sampling and pre-selection of candidate style patches. Projective features are then encoded via patch convolution. Multi-view feature integration and style clustering are carried out under the framework of partially shared latent factor (PSLF) learning, a multi-view feature learning scheme. PSLF achieves effective multi-view feature fusion by distilling and exploiting consistent and complementary feature information from multiple views, while also selecting style patches from the candidates. Our style analysis approach supports both unsupervised and semi-supervised analysis. For the latter, our method accepts both user-specified shape labels and style-ranked triplets as clustering constraints.We demonstrate results from 3D shape style analysis and patch localization as well as improvements over state-of-the-art methods. We also present several applications enabled by our style analysis.
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?
Authors: Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Janani Padmanabhan, Graham Neubig
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Machine Translation, NMT, Translation, Word Embeddings

The performance of Neural Machine Translation (NMT) systems often suffers in
low-resource scenarios where sufficiently large-scale parallel corpora cannot
be obtained. Pre-trained word embeddings have proven to be invaluable for
improving performance in natural language analysis tasks, which often suffer
from paucity of data. However, their utility for NMT has not been extensively
explored. In this work, we perform five sets of experiments that analyze when
we can expect pre-trained word embeddings to help in NMT tasks. We show that
such embeddings can be surprisingly effective in some cases -- providing gains
of up to 20 BLEU points in the most favorable setting.
RSGAN: Face Swapping and Editing using Face and Hair Representation in Latent Spaces
Authors: Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima
Date: 2018-04-10 00:00:00
Tasks: Attribute, Face Swapping, Generative Adversarial Network

In this paper, we present an integrated system for automatically generating
and editing face images through face swapping, attribute-based editing, and
random face parts synthesis. The proposed system is based on a deep neural
network that variationally learns the face and hair regions with large-scale
face image datasets. Different from conventional variational methods, the
proposed network represents the latent spaces individually for faces and hairs.
We refer to the proposed network as region-separative generative adversarial
network (RSGAN). The proposed network independently handles face and hair
appearances in the latent spaces, and then, face swapping is achieved by
replacing the latent-space representations of the faces, and reconstruct the
entire face image with them. This approach in the latent space robustly
performs face swapping even for images which the previous methods result in
failure due to inappropriate fitting or the 3D morphable models. In addition,
the proposed system can further edit face-swapped images with the same network
by manipulating visual attributes or by composing them with randomly generated
face or hair parts.
ViS-HuD: Using Visual Saliency to Improve Human Detection with Convolutional Neural Networks
Authors: Vandit Gajjar, Yash Khandhediya, Ayesha Gurnani, Viraj Mavani, Mehul S. Raval
Date: 2018-02-21 00:00:00
Tasks: Human Detection, image-classification, Image Classification, Saliency Detection

The paper presents a technique to improve human detection in still images
using deep learning. Our novel method, ViS-HuD, computes visual saliency map
from the image. Then the input image is multiplied by the map and product is
fed to the Convolutional Neural Network (CNN) which detects humans in the
image. A visual saliency map is generated using ML-Net and human detection is
carried out using DetectNet. ML-Net is pre-trained on SALICON while, DetectNet
is pre-trained on ImageNet database for visual saliency detection and image
classification respectively. The CNNs of ViS-HuD were trained on two
challenging databases - Penn Fudan and TUD-Brussels Benchmark. Experimental
results demonstrate that the proposed method achieves state-of-the-art
performance on Penn Fudan Dataset with 91.4% human detection accuracy and it
achieves average miss-rate of 53% on the TUDBrussels benchmark.
Polyglot Semantic Parsing in APIs
Authors: Kyle Richardson, Jonathan Berant, Jonas Kuhn
Date: 2018-03-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Parsing, Translation

Traditional approaches to semantic parsing (SP) work by training individual
models for each available parallel dataset of text-meaning pairs. In this
paper, we explore the idea of polyglot semantic translation, or learning
semantic parsing models that are trained on multiple datasets and natural
languages. In particular, we focus on translating text to code signature
representations using the software component datasets of Richardson and Kuhn
(2017a,b). The advantage of such models is that they can be used for parsing a
wide variety of input natural languages and output programming languages, or
mixed input languages, using a single unified model. To facilitate modeling of
this type, we develop a novel graph-based decoding framework that achieves
state-of-the-art performance on the above datasets, and apply this method to
two other benchmark SP tasks.
Dropout Sampling for Robust Object Detection in Open-Set Conditions
Authors: Dimity Miller, Lachlan Nicholson, Feras Dayoub, Niko Sünderhauf
Date: 2017-10-18 00:00:00
Tasks: image-classification, Image Classification, Object, object-detection, Object Detection, Robust Object Detection, Variational Inference

Dropout Variational Inference, or Dropout Sampling, has been recently
proposed as an approximation technique for Bayesian Deep Learning and evaluated
for image classification and regression tasks. This paper investigates the
utility of Dropout Sampling for object detection for the first time. We
demonstrate how label uncertainty can be extracted from a state-of-the-art
object detection system via Dropout Sampling. We evaluate this approach on a
large synthetic dataset of 30,000 images, and a real-world dataset captured by
a mobile robot in a versatile campus environment. We show that this uncertainty
can be utilized to increase object detection performance under the open-set
conditions that are typically encountered in robotic vision. A Dropout Sampling
network is shown to achieve a 12.3% increase in recall (for the same precision
score as a standard network) and a 15.1% increase in precision (for the same
recall score as the standard network).
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics
Authors: Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi
Date: 2017-03-08 00:00:00
Tasks: Deep Learning, Privacy Preserving

Internet of Things (IoT) devices and applications are being deployed in our homes and workplaces. These devices often rely on continuous data collection to feed machine learning models. However, this approach introduces several privacy and efficiency challenges, as the service operator can perform unwanted inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep neural networks for cooperative, privacy-preserving analytics. To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result. In order to ensure that the user's device contains no extra information except what is necessary for the main task and preventing any secondary inference on the data, we introduce Siamese fine-tuning. We evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also assess the local inference cost of different layers on a modern handset. Our evaluations show that by using Siamese fine-tuning and at a small processing cost, we can greatly reduce the level of unnecessary, potentially sensitive information in the personal data, and thus achieving the desired trade-off between utility, privacy, and performance.