pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods
Authors: Manu Goyal, Amanda Oakley, Priyanka Bansal, Darren Dancey, Moi Hoon Yap
Date: 2019-02-02 00:00:00
Tasks: Segmentation

Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the numbers of skin cancers, there is a growing need of computerized analysis for skin lesions. The state-of-the-art public available datasets for skin lesions are often accompanied with very limited amount of segmentation ground truth labeling as it is laborious and expensive. The lesion boundary segmentation is vital to locate the lesion accurately in dermoscopic images and lesion diagnosis of different skin lesion types. In this work, we propose the use of fully automated deep learning ensemble methods for accurate lesion boundary segmentation in dermoscopic images. We trained the Mask-RCNN and DeepLabv3+ methods on ISIC-2017 segmentation training set and evaluate the performance of the ensemble networks on ISIC-2017 testing set. Our results showed that the best proposed ensemble method segmented the skin lesions with Jaccard index of 79.58% for the ISIC-2017 testing set. The proposed ensemble method outperformed FrCN, FCN, U-Net, and SegNet in Jaccard Index by 2.48%, 7.42%, 17.95%, and 9.96% respectively. Furthermore, the proposed ensemble method achieved an accuracy of 95.6% for some representative clinically benign cases, 90.78% for the melanoma cases, and 91.29% for the seborrheic keratosis cases on ISIC-2017 testing set, exhibiting better performance than FrCN, FCN, U-Net, and SegNet.
Complexity, Statistical Risk, and Metric Entropy of Deep Nets Using Total Path Variation
Authors: Andrew R. Barron, Jason M. Klusowski
Date: 2019-02-02 00:00:00

For any ReLU network there is a representation in which the sum of the
absolute values of the weights into each node is exactly $1$, and the input
layer variables are multiplied by a value $V$ coinciding with the total
variation of the path weights. Implications are given for Gaussian complexity,
Rademacher complexity, statistical risk, and metric entropy, all of which are
shown to be proportional to $V$. There is no dependence on the number of nodes
per layer, except for the number of inputs $d$. For estimation with
sub-Gaussian noise, the mean square generalization error bounds that can be
obtained are of order $V \sqrt{L + \log d}/\sqrt{n}$, where $L$ is the number
of layers and $n$ is the sample size.
PointCloud Saliency Maps
Authors: Tianhang Zheng, Changyou Chen, Junsong Yuan, Bo Li, Kui Ren
Date: 2018-11-28 00:00:00
Proceeding: ICCV 2019 10

3D point-cloud recognition with PointNet and its variants has received remarkable progress. A missing ingredient, however, is the ability to automatically evaluate point-wise importance w.r.t.\! classification performance, which is usually reflected by a saliency map. A saliency map is an important tool as it allows one to perform further processes on point-cloud data. In this paper, we propose a novel way of characterizing critical points and segments to build point-cloud saliency maps. Our method assigns each point a score reflecting its contribution to the model-recognition loss. The saliency map explicitly explains which points are the key for model recognition. Furthermore, aggregations of highly-scored points indicate important segments/subsets in a point-cloud. Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator. To overcome this issue, we approximate point-dropping with a differentiable procedure of shifting points towards the cloud centroid. Consequently, each saliency score can be efficiently measured by the corresponding gradient of the loss w.r.t the point under the spherical coordinates. Extensive evaluations on several state-of-the-art point-cloud recognition models, including PointNet, PointNet++ and DGCNN, demonstrate the veracity and generality of our proposed saliency map. Code for experiments is released on \url{https://github.com/tianzheng4/PointCloud-Saliency-Maps}.
Variational Bayesian Decision-making for Continuous Utilities
Authors: Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
Date: 2019-02-02 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decision Making, Variational Inference

Bayesian decision theory outlines a rigorous framework for making optimal decisions based on maximizing expected utility over a model posterior. However, practitioners often do not have access to the full posterior and resort to approximate inference strategies. In such cases, taking the eventual decision-making task into account while performing the inference allows for calibrating the posterior approximation to maximize the utility. We present an automatic pipeline that co-opts continuous utilities into variational inference algorithms to account for decision-making. We provide practical strategies for approximating and maximizing the gain, and empirically demonstrate consistent improvement when calibrating approximations for specific utilities.
Certified Reinforcement Learning with Logic Guidance
Authors: Hosein Hasanbeig, Daniel Kroening, Alessandro Abate
Date: 2019-02-02 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems. However, applications in safety-critical domains require a systematic and formal approach to specifying requirements as tasks or goals. We propose a model-free RL algorithm that enables the use of Linear Temporal Logic (LTL) to formulate a goal for unknown continuous-state/action Markov Decision Processes (MDPs). The given LTL property is translated into a Limit-Deterministic Generalised Buchi Automaton (LDGBA), which is then used to shape a synchronous reward function on-the-fly. Under certain assumptions, the algorithm is guaranteed to synthesise a control policy whose traces satisfy the LTL specification with maximal probability.
RELINE: Point-of-Interest Recommendations using Multiple Network Embeddings
Authors: Giannis Christoforidis, Pavlos Kefalas, Apostolos N. Papadopoulos, Yannis Manolopoulos
Date: 2019-02-02 00:00:00
Tasks: Recommendation Systems

The rapid growth of users' involvement in Location-Based Social Networks
(LBSNs) has led to the expeditious growth of the data on a global scale. The
need of accessing and retrieving relevant information close to users'
preferences is an open problem which continuously raises new challenges for
recommendation systems. The exploitation of Points-of-Interest (POIs)
recommendation by existing models is inadequate due to the sparsity and the
cold start problems. To overcome these problems many models were proposed in
the literature, but most of them ignore important factors such as: geographical
proximity, social influence, or temporal and preference dynamics, which tackle
their accuracy while personalize their recommendations. In this work, we
investigate these problems and present a unified model that jointly learns
users and POI dynamics. Our proposal is termed RELINE (REcommendations with
muLtIple Network Embeddings). More specifically, RELINE captures: i) the
social, ii) the geographical, iii) the temporal influence, and iv) the users'
preference dynamics, by embedding eight relational graphs into one shared
latent space. We have evaluated our approach against state-of-the-art methods
with three large real-world datasets in terms of accuracy. Additionally, we
have examined the effectiveness of our approach against the cold-start problem.
Performance evaluation results demonstrate that significant performance
improvement is achieved in comparison to existing state-of-the-art methods.
High-dimensional semi-supervised learning: in search for optimal inference of the mean
Authors: Yuqian Zhang, Jelena Bradic
Date: 2019-02-02 00:00:00

We provide a high-dimensional semi-supervised inference framework focused on
the mean and variance of the response. Our data are comprised of an extensive
set of observations regarding the covariate vectors and a much smaller set of
labeled observations where we observe both the response as well as the
covariates. We allow the size of the covariates to be much larger than the
sample size and impose weak conditions on a statistical form of the data. We
provide new estimators of the mean and variance of the response that extend
some of the recent results presented in low-dimensional models. In particular,
at times we will not necessitate consistent estimation of the functional form
of the data. Together with estimation of the population mean and variance, we
provide their asymptotic distribution and confidence intervals where we
showcase gains in efficiency compared to the sample mean and variance. Our
procedure, with minor modifications, is then presented to make important
contributions regarding inference about average treatment effects. We also
investigate the robustness of estimation and coverage and showcase widespread
applicability and generality of the proposed method.
Generating Dialogue Agents via Automated Planning
Authors: Adi Botea, Christian Muise, Shubham Agarwal, Oznur Alkan, Ondrej Bajgar, Elizabeth Daly, Akihiro Kishimoto, Luis Lastras, Radu Marinescu, Josef Ondrej, Pablo Pedemonte, Miroslav Vodolan
Date: 2019-02-02 00:00:00
Tasks: Question Answering

Dialogue systems have many applications such as customer support or question
answering. Typically they have been limited to shallow single turn
interactions. However more advanced applications such as career coaching or
planning a trip require a much more complex multi-turn dialogue. Current
limitations of conversational systems have made it difficult to support
applications that require personalization, customization and context dependent
interactions. We tackle this challenging problem by using domain-independent AI
planning to automatically create dialogue plans, customized to guide a dialogue
towards achieving a given goal. The input includes a library of atomic dialogue
actions, an initial state of the dialogue, and a goal. Dialogue plans are
plugged into a dialogue system capable to orchestrate their execution. Use
cases demonstrate the viability of the approach. Our work on dialogue planning
has been integrated into a product, and it is in the process of being deployed
into another.
Learning Linear Dynamical Systems with Semi-Parametric Least Squares
Authors: Max Simchowitz, Ross Boczar, Benjamin Recht
Date: 2019-02-02 00:00:00
Tasks: Active Learning

We analyze a simple prefiltered variation of the least squares estimator for
the problem of estimation with biased, semi-parametric noise, an error model
studied more broadly in causal statistics and active learning. We prove an
oracle inequality which demonstrates that this procedure provably mitigates the
variance introduced by long-term dependencies. We then demonstrate that
prefiltered least squares yields, to our knowledge, the first algorithm that
provably estimates the parameters of partially-observed linear systems that
attains rates which do not not incur a worst-case dependence on the rate at
which these dependencies decay. The algorithm is provably consistent even for
systems which satisfy the weaker marginal stability condition obeyed by many
classical models based on Newtonian mechanics. In this context, our
semi-parametric framework yields guarantees for both stochastic and worst-case
noise.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
Authors: Shreyas S. Shivakumar, Ty Nguyen, Ian D. Miller, Steven W. Chen, Vijay Kumar, Camillo J. Taylor
Date: 2019-02-02 00:00:00
Tasks: Depth Completion, Super-Resolution

In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image. Our approach draws inspiration from related work on super-resolution and in-painting. We propose a novel architecture that seeks to pull contextual cues separately from the intensity image and the depth features and then fuse them later in the network. We argue that this approach effectively exploits the relationship between the two modalities and produces accurate results while respecting salient image structures. We present experimental results to demonstrate that our approach is comparable with state of the art methods and generalizes well across multiple datasets.
Domain invariant hierarchical embedding for grocery products recognition
Authors: Alessio Tonioni, Luigi Di Stefano
Date: 2019-02-02 00:00:00

Recognizing packaged grocery products based solely on appearance is still an
open issue for modern computer vision systems due to peculiar challenges.
Firstly, the number of different items to be recognized is huge (i.e., in the
order of thousands) and rapidly changing over time. Moreover, there exist a
significant domain shift between the images that should be recognized at test
time, taken in stores by cheap cameras, and those available for training,
usually just one or a few studio-quality images per product. We propose an
end-to-end architecture comprising a GAN to address the domain shift at
training time and a deep CNN trained on the samples generated by the GAN to
learn an embedding of product images that enforces a hierarchy between product
categories. At test time, we perform recognition by means of K-NN search
against a database consisting of just one reference image per product.
Experiments addressing recognition of products present in the training datasets
as well as different ones unseen at training time show that our approach
compares favourably to state-of-the-art methods on the grocery recognition task
and generalize fairly well to similar ones.
Graph Neural Networks with Generated Parameters for Relation Extraction
Authors: Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, Maosong Sun
Date: 2019-02-02 00:00:00
Proceeding: ACL 2019 7
Tasks: Relation, Relational Reasoning, Relation Extraction

Recently, progress has been made towards improving relational reasoning in
machine learning field. Among existing models, graph neural networks (GNNs) is
one of the most effective approaches for multi-hop relational reasoning. In
fact, multi-hop relational reasoning is indispensable in many natural language
processing tasks such as relation extraction. In this paper, we propose to
generate the parameters of graph neural networks (GP-GNNs) according to natural
language sentences, which enables GNNs to process relational reasoning on
unstructured text inputs. We verify GP-GNNs in relation extraction from text.
Experimental results on a human-annotated dataset and two distantly supervised
datasets show that our model achieves significant improvements compared to
baselines. We also perform a qualitative analysis to demonstrate that our model
could discover more accurate relations by multi-hop relational reasoning.
Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey
Authors: Erion Çano, Maurizio Morisio
Date: 2019-02-02 00:00:00
Tasks: Sentiment Analysis, Survey, Word Embeddings

This work investigates the role of factors like training method, training
corpus size and thematic relevance of texts in the performance of word
embedding features on sentiment analysis of tweets, song lyrics, movie reviews
and item reviews. We also explore specific training or post-processing methods
that can be used to enhance the performance of word embeddings in certain tasks
or domains. Our empirical observations indicate that models trained with
multithematic texts that are large and rich in vocabulary are the best in
answering syntactic and semantic word analogy questions. We further observe
that influence of thematic relevance is stronger on movie and phone reviews,
but weaker on tweets and lyrics. These two later domains are more sensitive to
corpus size and training method, with Glove outperforming Word2vec. "Injecting"
extra intelligence from lexicons or generating sentiment specific word
embeddings are two prominent alternatives for increasing performance of word
embedding features.
Parameter-Efficient Transfer Learning for NLP
Authors: Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly
Date: 2019-02-02 00:00:00
Tasks: Image Classification, Text Classification, Transfer Learning

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.
How to Write High-quality News on Social Network? Predicting News Quality by Mining Writing Style
Authors: Yuting Yang, Juan Cao, Mingyan Lu, Jintao Li, Chia-Wen Lin
Date: 2019-02-02 00:00:00

Rapid development of Internet technologies promotes traditional newspapers to report news on social networks. However, people on social networks may have different needs which naturally arises the question: whether can we analyze the influence of writing style on news quality automatically and assist writers in improving news quality? It's challenging due to writing style and 'quality' are hard to measure. First, we use 'popularity' as the measure of 'quality'. It is natural on social networks but brings new problems: popularity are also influenced by event and publisher. So we design two methods to alleviate their influence. Then, we proposed eight types of linguistic features (53 features in all) according eight writing guidelines and analyze their relationship with news quality. The experimental results show these linguistic features influence greatly on news quality. Based on it, we design a news quality assessment model on social network (SNQAM). SNQAM performs excellently on predicting quality, presenting interpretable quality score and giving accessible suggestions on how to improve it according to writing guidelines we referred to.
Online Multi-Object Tracking with Dual Matching Attention Networks
Authors: Ji Zhu, Hua Yang, Nian Liu, Minyoung Kim, Wenjun Zhang, Ming-Hsuan Yang
Date: 2019-02-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking

In this paper, we propose an online Multi-Object Tracking (MOT) approach
which integrates the merits of single object tracking and data association
methods in a unified framework to handle noisy detections and frequent
interactions between targets. Specifically, for applying single object tracking
in MOT, we introduce a cost-sensitive tracking loss based on the
state-of-the-art visual tracker, which encourages the model to focus on hard
negative distractors during online learning. For data association, we propose
Dual Matching Attention Networks (DMAN) with both spatial and temporal
attention mechanisms. The spatial attention module generates dual attention
maps which enable the network to focus on the matching patterns of the input
image pair, while the temporal attention module adaptively allocates different
levels of attention to different samples in the tracklet to suppress noisy
observations. Experimental results on the MOT benchmark datasets show that the
proposed algorithm performs favorably against both online and offline trackers
in terms of identity-preserving metrics.
On the bias, risk and consistency of sample means in multi-armed bandits
Authors: Jaehyeok Shin, Aaditya Ramdas, Alessandro Rinaldo
Date: 2019-02-02 00:00:00
Tasks: Multi-Armed Bandits, Selection bias

The sample mean is among the most well studied estimators in statistics, having many desirable properties such as unbiasedness and consistency. However, when analyzing data collected using a multi-armed bandit (MAB) experiment, the sample mean is biased and much remains to be understood about its properties. For example, when is it consistent, how large is its bias, and can we bound its mean squared error? This paper delivers a thorough and systematic treatment of the bias, risk and consistency of MAB sample means. Specifically, we identify four distinct sources of selection bias (sampling, stopping, choosing and rewinding) and analyze them both separately and together. We further demonstrate that a new notion of \emph{effective sample size} can be used to bound the risk of the sample mean under suitable loss functions. We present several carefully designed examples to provide intuition on the different sources of selection bias we study. Our treatment is nonparametric and algorithm-agnostic, meaning that it is not tied to a specific algorithm or goal. In a nutshell, our proofs combine variational representations of information-theoretic divergences with new martingale concentration inequalities.
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Authors: Haowei He, Gao Huang, Yang Yuan
Date: 2019-02-02 00:00:00
Proceeding: NeurIPS 2019 12

Despite the non-convex nature of their loss functions, deep neural networks
are known to generalize well when optimized with stochastic gradient descent
(SGD). Recent work conjectures that SGD with proper configuration is able to
find wide and flat local minima, which have been proposed to be associated with
good generalization performance. In this paper, we observe that local minima of
modern deep networks are more than being flat or sharp. Specifically, at a
local minimum there exist many asymmetric directions such that the loss
increases abruptly along one side, and slowly along the opposite side--we
formally define such minima as asymmetric valleys. Under mild assumptions, we
prove that for asymmetric valleys, a solution biased towards the flat side
generalizes better than the exact minimizer. Further, we show that simply
averaging the weights along the SGD trajectory gives rise to such biased
solutions implicitly. This provides a theoretical explanation for the
intriguing phenomenon observed by Izmailov et al. (2018). In addition, we
empirically find that batch normalization (BN) appears to be a major cause for
asymmetric valleys.
Deep Learning for Vertex Reconstruction of Neutrino-Nucleus Interaction Events with Combined Energy and Time Data
Authors: Linghao Song, Fan Chen, Steven R. Young, Catherine D. Schuman, Gabriel Perdue, Thomas E. Potok
Date: 2019-02-02 00:00:00
Tasks: Classification, General Classification, regression

We present a deep learning approach for vertex reconstruction of
neutrino-nucleus interaction events, a problem in the domain of high energy
physics. In this approach, we combine both energy and timing data that are
collected in the MINERvA detector to perform classification and regression
tasks. We show that the resulting network achieves higher accuracy than
previous results while requiring a smaller model size and less training time.
In particular, the proposed model outperforms the state-of-the-art by 4.00% on
classification accuracy. For the regression task, our model achieves 0.9919 on
the coefficient of determination, higher than the previous work (0.96).