pwc-archive/papers-with-abstracts huggingface dataset:
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Authors: Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, Wei Wang
Date: 2018-08-16 00:00:00
Proceeding: WSDM '19 Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining 2019 2
Tasks: Graph Classification, Graph Neural Network, Graph Similarity

Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance. The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into a vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. To the best of our knowledge, we are among the first to adopt neural networks to explicitly model the similarity between two graphs, and provide a new direction for future research on graph similarity computation and graph similarity search.
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
Authors: Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu
Date: 2018-08-16 00:00:00

Adaptive gradient methods are workhorses in deep learning. However, the convergence guarantees of adaptive gradient methods for nonconvex optimization have not been thoroughly studied. In this paper, we provide a fine-grained convergence analysis for a general class of adaptive gradient methods including AMSGrad, RMSProp and AdaGrad. For smooth nonconvex functions, we prove that adaptive gradient methods in expectation converge to a first-order stationary point. Our convergence rate is better than existing results for adaptive gradient methods in terms of dimension. In addition, we also prove high probability bounds on the convergence rates of AMSGrad, RMSProp as well as AdaGrad, which have not been established before. Our analyses shed light on better understanding the mechanism behind adaptive gradient methods in optimizing nonconvex objectives.
Predicting Human Trustfulness from Facebook Language
Authors: Mohammadzaman Zamani, Anneke Buffone, H. Andrew Schwartz
Date: 2018-08-16 00:00:00
Proceeding: WS 2018 6

Trustfulness -- one's general tendency to have confidence in unknown people
or situations -- predicts many important real-world outcomes such as mental
health and likelihood to cooperate with others such as clinicians. While
data-driven measures of interpersonal trust have previously been introduced,
here, we develop the first language-based assessment of the personality trait
of trustfulness by fitting one's language to an accepted questionnaire-based
trust score. Further, using trustfulness as a type of case study, we explore
the role of questionnaire size as well as word count in developing
language-based predictive models of users' psychological traits. We find that
leveraging a longer questionnaire can yield greater test set accuracy, while,
for training, we find it beneficial to include users who took smaller
questionnaires which offers more observations for training. Similarly, after
noting a decrease in individual prediction error as word count increased, we
found a word count-weighted training scheme was helpful when there were very
few users in the first place.
Fast and Accurate, Convolutional Neural Network Based Approach for Object Detection from UAV
Authors: Xiaoliang Wang, Peng Cheng, Xinchuan Liu, Benedict Uzochukwu
Date: 2018-08-16 00:00:00
Tasks: Management, Object, object-detection, Object Detection

Unmanned Aerial Vehicles (UAVs), have intrigued different people from all
walks of life, because of their pervasive computing capabilities. UAV equipped
with vision techniques, could be leveraged to establish navigation autonomous
control for UAV itself. Also, object detection from UAV could be used to
broaden the utilization of drone to provide ubiquitous surveillance and
monitoring services towards military operation, urban administration and
agriculture management. As the data-driven technologies evolved, machine
learning algorithm, especially the deep learning approach has been intensively
utilized to solve different traditional computer vision research problems.
Modern Convolutional Neural Networks based object detectors could be divided
into two major categories: one-stage object detector and two-stage object
detector. In this study, we utilize some representative CNN based object
detectors to execute the computer vision task over Stanford Drone Dataset
(SDD). State-of-the-art performance has been achieved in utilizing focal loss
dense detector RetinaNet based approach for object detection from UAV in a fast
and accurate manner.
Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
Authors: C. -H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, I-Hung Lin, Yi-Chieh Liu, Hao-Hsiang Yang, Jesper Tegner
Date: 2018-08-16 00:00:00
Tasks: General Classification

Automatic clinical diagnosis of retinal diseases has emerged as a promising
approach to facilitate discovery in areas with limited access to specialists.
Based on the fact that fundus structure and vascular disorders are the main
characteristics of retinal diseases, we propose a novel visual-assisted
diagnosis hybrid model mixing the support vector machine (SVM) and deep neural
networks (DNNs). Furthermore, we present a new clinical retina dataset, called
EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using
EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model
performance is comparable to the professional ophthalmologists.
Context-Aware Visual Policy Network for Sequence-Level Image Captioning
Authors: Daqing Liu, Zheng-Jun Zha, Hanwang Zhang, Yongdong Zhang, Feng Wu
Date: 2018-08-16 00:00:00
Tasks: Deep Reinforcement Learning, Image Captioning, Reinforcement Learning, Reinforcement Learning (RL)

Many vision-language tasks can be reduced to the problem of sequence
prediction for natural language output. In particular, recent advances in image
captioning use deep reinforcement learning (RL) to alleviate the "exposure
bias" during training: ground-truth subsequence is exposed in every step
prediction, which introduces bias in test when only predicted subsequence is
seen. However, existing RL-based image captioning methods only focus on the
language policy while not the visual policy (e.g., visual attention), and thus
fail to capture the visual context that are crucial for compositional reasoning
such as visual relationships (e.g., "man riding horse") and comparisons (e.g.,
"smaller cat"). To fill the gap, we propose a Context-Aware Visual Policy
network (CAVP) for sequence-level image captioning. At every time step, CAVP
explicitly accounts for the previous visual attentions as the context, and then
decides whether the context is helpful for the current word generation given
the current visual attention. Compared against traditional visual attention
that only fixes a single image region at every step, CAVP can attend to complex
visual compositions over time. The whole image captioning model --- CAVP and
its subsequent language policy network --- can be efficiently optimized
end-to-end by using an actor-critic policy gradient method with respect to any
caption evaluation metric. We demonstrate the effectiveness of CAVP by
state-of-the-art performances on MS-COCO offline split and online server, using
various metrics and sensible visualizations of qualitative visual context. The
code is available at https://github.com/daqingliu/CAVP
Story Disambiguation: Tracking Evolving News Stories across News and Social Streams
Authors: Bichen Shi, Thanh-Binh Le, Neil Hurley, Georgiana Ifrim
Date: 2018-08-16 00:00:00
Tasks: Articles, Entity Disambiguation, Learning-To-Rank

Following a particular news story online is an important but difficult task,
as the relevant information is often scattered across different domains/sources
(e.g., news articles, blogs, comments, tweets), presented in various formats
and language styles, and may overlap with thousands of other stories. In this
work we join the areas of topic tracking and entity disambiguation, and propose
a framework named Story Disambiguation - a cross-domain story tracking approach
that builds on real-time entity disambiguation and a learning-to-rank framework
to represent and update the rich semantic structure of news stories. Given a
target news story, specified by a seed set of documents, the goal is to
effectively select new story-relevant documents from an incoming document
stream. We represent stories as entity graphs and we model the story tracking
problem as a learning-to-rank task. This enables us to track content with high
accuracy, from multiple domains, in real-time. We study a range of text, entity
and graph based features to understand which type of features are most
effective for representing stories. We further propose new semi-supervised
learning techniques to automatically update the story representation over time.
Our empirical study shows that we outperform the accuracy of state-of-the-art
methods for tracking mixed-domain document streams, while requiring fewer
labeled data to seed the tracked stories. This is particularly the case for
local news stories that are easily over shadowed by other trending stories, and
for complex news stories with ambiguous content in noisy stream environments.
Stream Reasoning on Expressive Logics
Authors: Gulay Unel
Date: 2018-08-14 00:00:00
Tasks: Management

Data streams occur widely in various real world applications. The research on
streaming data mainly focuses on the data management, query evaluation and
optimization on these data, however the work on reasoning procedures for
streaming knowledge bases on both the assertional and terminological levels is
very limited. Typically reasoning services on large knowledge bases are very
expensive, and need to be applied continuously when the data is received as a
stream. Hence new techniques for optimizing this continuous process is needed
for developing efficient reasoners on streaming data. In this paper, we survey
the related research on reasoning on expressive logics that can be applied to
this setting, and point to further research directions in this area.
Improved Language Modeling by Decoding the Past
Authors: Siddhartha Brahma
Date: 2018-08-14 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Modeling, Language Modelling

Highly regularized LSTMs achieve impressive results on several benchmark
datasets in language modeling. We propose a new regularization method based on
decoding the last token in the context using the predicted distribution of the
next token. This biases the model towards retaining more contextual
information, in turn improving its ability to predict the next token. With
negligible overhead in the number of parameters and training time, our Past
Decode Regularization (PDR) method achieves a word level perplexity of 55.6 on
the Penn Treebank and 63.5 on the WikiText-2 datasets using a single softmax.
We also show gains by using PDR in combination with a mixture-of-softmaxes,
achieving a word level perplexity of 53.8 and 60.5 on these datasets. In
addition, our method achieves 1.169 bits-per-character on the Penn Treebank
Character dataset for character level language modeling. These results
constitute a new state-of-the-art in their respective settings.
Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings
Authors: Shubham Bhardwaj
Date: 2018-08-14 00:00:00
Tasks: Word Embeddings

Word embeddings aims to map sense of the words into a lower dimensional
vector space in order to reason over them. Training embeddings on domain
specific data helps express concepts more relevant to their use case but comes
at a cost of accuracy when data is less. Our effort is to minimise this by
infusing syntactic knowledge into the embeddings. We propose a graph based
embedding algorithm inspired from node2vec. Experimental results have shown
that our algorithm improves the syntactic strength and gives robust performance
on meagre data.
Building Better Nurse Scheduling Algorithms
Authors: Uwe Aickelin, Paul White
Date: 2008-03-20 00:00:00
Tasks: Evolutionary Algorithms, Scheduling

The aim of this research is twofold: Firstly, to model and solve a complex
nurse scheduling problem with an integer programming formulation and
evolutionary algorithms. Secondly, to detail a novel statistical method of
comparing and hence building better scheduling algorithms by identifying
successful algorithm modifications. The comparison method captures the results
of algorithms in a single figure that can then be compared using traditional
statistical techniques. Thus, the proposed method of comparing algorithms is an
objective procedure designed to assist in the process of improving an
algorithm. This is achieved even when some results are non-numeric or missing
due to infeasibility. The final algorithm outperforms all previous evolutionary
algorithms, which relied on human expertise for modification.
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
Authors: Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein
Date: 2018-08-20 00:00:00
Tasks: Denoising, Image Denoising

We propose a fully-convolutional neural-network architecture for image
denoising which is simple yet powerful. Its structure allows to exploit the
gradual nature of the denoising process, in which shallow layers handle local
noise statistics, while deeper layers recover edges and enhance textures. Our
method advances the state-of-the-art when trained for different noise levels
and distributions (both Gaussian and Poisson). In addition, we show that making
the denoiser class-aware by exploiting semantic class information boosts
performance, enhances textures and reduces artifacts.
Video-to-Video Synthesis
Authors: Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, Bryan Catanzaro
Date: 2018-08-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: 2k, Semantic Segmentation, Video deraining, Video Prediction, Video-to-Video Synthesis

We study the problem of video-to-video synthesis, whose goal is to learn a
mapping function from an input source video (e.g., a sequence of semantic
segmentation masks) to an output photorealistic video that precisely depicts
the content of the source video. While its image counterpart, the
image-to-image synthesis problem, is a popular topic, the video-to-video
synthesis problem is less explored in the literature. Without understanding
temporal dynamics, directly applying existing image synthesis approaches to an
input video often results in temporally incoherent videos of low visual
quality. In this paper, we propose a novel video-to-video synthesis approach
under the generative adversarial learning framework. Through carefully-designed
generator and discriminator architectures, coupled with a spatio-temporal
adversarial objective, we achieve high-resolution, photorealistic, temporally
coherent video results on a diverse set of input formats including segmentation
masks, sketches, and poses. Experiments on multiple benchmarks show the
advantage of our method compared to strong baselines. In particular, our model
is capable of synthesizing 2K resolution videos of street scenes up to 30
seconds long, which significantly advances the state-of-the-art of video
synthesis. Finally, we apply our approach to future video prediction,
outperforming several state-of-the-art competing systems.
Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
Authors: Yunzhe Li, Yujia Xue, Lei Tian
Date: 2018-06-11 00:00:00

Imaging through scattering is an important, yet challenging problem.
Tremendous progress has been made by exploiting the deterministic input-output
relation for a static medium. However, this approach is highly susceptible to
speckle decorrelations - small perturbations to the scattering medium lead to
model errors and severe degradation of the imaging performance. In addition,
this is complicated by the large number of phase-sensitive measurements
required for characterizing the input-output `transmission matrix'. Our goal
here is to develop a new framework that is highly scalable to both medium
perturbations and measurement requirement. To do so, we abandon the traditional
deterministic approach, instead propose a statistical framework that permits
higher representation power to encapsulate a wide range of statistical
variations needed for model generalization. Specifically, we develop a
convolutional neural network (CNN) that takes intensity-only speckle patterns
as input and predicts unscattered object as output. Importantly, instead of
characterizing a single input-output relation of a fixed medium, we train our
CNN to learn statistical information contained in several scattering media of
the same class. We then show that the CNN is able to generalize over a
completely different set of scattering media from the same class, demonstrating
its superior adaptability to medium perturbations. In our proof of concept
experiment, we first train our CNN using speckle patterns captured on diffusers
having the same macroscopic parameter (e.g. grits); the trained CNN is then
able to make high-quality reconstruction from speckle patterns that were
captured from an entirely different set of diffusers of the same grits. Our
work paves the way to a highly scalable deep learning approach for imaging
through scattering media.
Learning Monocular Depth by Distilling Cross-domain Stereo Networks
Authors: Xiaoyang Guo, Hongsheng Li, Shuai Yi, Jimmy Ren, Xiaogang Wang
Date: 2018-08-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Autonomous Driving, Depth Estimation, Monocular Depth Estimation, Scene Understanding, Stereo Matching, Stereo Matching Hand

Monocular depth estimation aims at estimating a pixelwise depth map for a
single image, which has wide applications in scene understanding and autonomous
driving. Existing supervised and unsupervised methods face great challenges.
Supervised methods require large amounts of depth measurement data, which are
generally difficult to obtain, while unsupervised methods are usually limited
in estimation accuracy. Synthetic data generated by graphics engines provide a
possible solution for collecting large amounts of depth data. However, the
large domain gaps between synthetic and realistic data make directly training
with them challenging. In this paper, we propose to use the stereo matching
network as a proxy to learn depth from synthetic data and use predicted stereo
disparity maps for supervising the monocular depth estimation network.
Cross-domain synthetic data could be fully utilized in this novel framework.
Different strategies are proposed to ensure learned depth perception capability
well transferred across different domains. Our extensive experiments show
state-of-the-art results of monocular depth estimation on KITTI dataset.
The Deconfounded Recommender: A Causal Inference Approach to Recommendation
Authors: Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei
Date: 2018-08-20 00:00:00
Tasks: Causal Inference, Recommendation Systems

The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user and movie, what would the rating be if we "forced" the user to watch the movie? To this end, we develop a causal approach to recommendation, one where watching a movie is a "treatment" and a user's rating is an "outcome." The problem is there may be unobserved confounders, variables that affect both which movies the users watch and how they rate them; unobserved confounders impede causal predictions with observational data. To solve this problem, we develop the deconfounded recommender, a way to use classical recommendation models for causal recommendation. Following Wang & Blei [23], the deconfounded recommender involves two probabilistic models. The first models which movies the users watch; it provides a substitute for the unobserved confounders. The second one models how each user rates each movie; it employs the substitute to help account for confounders. This two-stage approach removes bias due to confounding. It improves recommendation and enjoys stable performance against interventions on test sets.
Peptide-Spectra Matching from Weak Supervision
Authors: Samuel S. Schoenholz, Sean Hackett, Laura Deming, Eugene Melamud, Navdeep Jaitly, Fiona McAllister, Jonathon O'Brien, George Dahl, Bryson Bennett, Andrew M. Dai, Daphne Koller
Date: 2018-08-20 00:00:00
Tasks: Reranking

As in many other scientific domains, we face a fundamental problem when using
machine learning to identify proteins from mass spectrometry data: large ground
truth datasets mapping inputs to correct outputs are extremely difficult to
obtain. Instead, we have access to imperfect hand-coded models crafted by
domain experts. In this paper, we apply deep neural networks to an important
step of the protein identification problem, the pairing of mass spectra with
short sequences of amino acids called peptides. We train our model to
differentiate between top scoring results from a state-of-the art classical
system and hard-negative second and third place results. Our resulting model is
much better at identifying peptides with spectra than the model used to
generate its training data. In particular, we achieve a 43% improvement over
standard matching methods and a 10% improvement over a combination of the
matching method and an industry standard cross-spectra reranking tool.
Importantly, in a more difficult experimental regime that reflects current
challenges facing biologists, our advantage over the previous state-of-the-art
grows to 15% even after reranking. We believe this approach will generalize to
other challenging scientific problems.