pwc-archive/papers-with-abstracts huggingface dataset:
Recommending Dream Jobs in a Biased Real World
Authors: Nadia Fawaz
Date: 2019-05-10 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems

Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existing societal biases in the data such as the professional gender gap, to biases introduced by the data collection or modeling processes. These biases impact the performance of various components of recommender systems, from offline training, to evaluation and online serving of recommendations in production systems. Specific techniques can help reduce bias at each stage of a recommender system. Reducing bias in our recommender systems is crucial to successfully recommending dream jobs to hundreds of millions members worldwide, while being true to LinkedIn's vision: "To create economic opportunity for every member of the global workforce".
Extensions to Justification Theory
Authors: Simon Marynissen
Date: 2019-05-09 00:00:00

Justification theory is a unifying framework for semantics of non-monotonic logics. It is built on the notion of a justification, which intuitively is a graph that explains the truth value of certain facts in a structure. Knowledge representation languages covered by justification theory include logic programs, argumentation frameworks, inductive definitions, and nested inductive and coinductive definitions. In addition, justifications are also used for implementation purposes. They are used to compute unfounded sets in modern ASP solvers, can be used to check for relevance of atoms in complete search algorithms, and recent lazy grounding algorithms are built on top of them. In this extended abstract, we lay out possible extensions to justification theory.
Toward Standardized Classification of Foveated Displays
Authors: Josef Spjut, Ben Boudaoud, Jonghyun Kim, Trey Greer, Rachel Albert, Michael Stengel, Kaan Aksit, David Luebke
Date: 2019-05-03 00:00:00
Tasks: Classification, Foveation, General Classification

Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.
A New Anchor Word Selection Method for the Separable Topic Discovery
Authors: Kun He, Wu Wang, Xiaosen Wang, John E. Hopcroft
Date: 2019-05-10 00:00:00
Tasks: Word Similarity

Separable Non-negative Matrix Factorization (SNMF) is an important method for topic modeling, where "separable" assumes every topic contains at least one anchor word, defined as a word that has non-zero probability only on that topic. SNMF focuses on the word co-occurrence patterns to reveal topics by two steps: anchor word selection and topic recovery. The quality of the anchor words strongly influences the quality of the extracted topics. Existing anchor word selection algorithm is to greedily find an approximate convex hull in a high-dimensional word co-occurrence space. In this work, we propose a new method for the anchor word selection by associating the word co-occurrence probability with the words similarity and assuming that the most different words on semantic are potential candidates for the anchor words. Therefore, if the similarity of a word-pair is very low, then the two words are very likely to be the anchor words. According to the statistical information of text corpora, we can get the similarity of all word-pairs. We build the word similarity graph where the nodes correspond to words and weights on edges stand for the word-pair similarity. Following this way, we design a greedy method to find a minimum edge-weight anchor clique of a given size in the graph for the anchor word selection. Extensive experiments on real-world corpus demonstrate the effectiveness of the proposed anchor word selection method that outperforms the common convex hull-based methods on the revealed topic quality. Meanwhile, our method is much faster than typical SNMF based method.
Detecting Vietnamese Opinion Spam
Authors: T. H. H Duong, T. D. Vu, V. M. Ngo
Date: 2019-05-09 00:00:00
Tasks: General Classification

Recently, Vietnamese Natural Language Processing has been researched by experts in academic and business. However, the existing papers have been focused only on information classification or extraction from documents. Nowadays, with quickly development of the e-commerce websites, forums and social networks, the products, people, organizations or wonders are targeted of comments or reviews of the network communities. Many people often use that reviews to make their decision on something. Whereas, there are many people or organizations use the reviews to mislead readers. Therefore, it is so necessary to detect those bad behaviors in reviews. In this paper, we research this problem and propose an appropriate method for detecting Vietnamese reviews being spam or non-spam. The accuracy of our method is up to 90%.
Naive Bayes with Correlation Factor for Text Classification Problem
Authors: Jiangning Chen, Zhibo Dai, Juntao Duan, Heinrich Matzinger, Ionel Popescu
Date: 2019-05-08 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Bayes estimator to solve this problem. A correlation factor is introduced to incorporate the correlation among different classes. Experimental results show that our estimator achieves a better accuracy compared with traditional Naive Bayes in real world data.
A Scalable Learned Index Scheme in Storage Systems
Authors: Pengfei Li, Yu Hua, Pengfei Zuo, Jingnan Jia
Date: 2019-05-08 00:00:00

Index structures are important for efficient data access, which have been widely used to improve the performance in many in-memory systems. Due to high in-memory overheads, traditional index structures become difficult to process the explosive growth of data, let alone providing low latency and high throughput performance with limited system resources. The promising learned indexes leverage deep-learning models to complement existing index structures and obtain significant memory savings. However, the learned indexes fail to become scalable due to the heavy inter-model dependency and expensive retraining. To address these problems, we propose a scalable learned index scheme to construct different linear regression models according to the data distribution. Moreover, the used models are independent so as to reduce the complexity of retraining and become easy to partition and store the data into different pages, blocks or distributed systems. Our experimental results show that compared with state-of-the-art schemes, AIDEL improves the insertion performance by about 2$\times$ and provides comparable lookup performance, while efficiently supporting scalability.
A data-driven proxy to Stoke's flow in porous media
Authors: Ali Takbiri-Borujeni, Hadi Kazemi, Nasser Nasrabadi
Date: 2019-04-25 00:00:00

The objective for this work is to develop a data-driven proxy to
high-fidelity numerical flow simulations using digital images. The proposed
model can capture the flow field and permeability in a large verity of digital
porous media based on solid grain geometry and pore size distribution by
detailed analyses of the local pore geometry and the local flow fields. To
develop the model, the detailed pore space geometry and simulation runs data
from 3500 two-dimensional high-fidelity Lattice Boltzmann simulation runs are
used to train and to predict the solutions with a high accuracy in much less
computational time. The proposed methodology harness the enormous amount of
generated data from high-fidelity flow simulations to decode the often
under-utilized patterns in simulations and to accurately predict solutions to
new cases. The developed model can truly capture the physics of the problem and
enhance prediction capabilities of the simulations at a much lower cost. These
predictive models, in essence, do not spatio-temporally reduce the order of the
problem. They, however, possess the same numerical resolutions as their Lattice
Boltzmann simulations equivalents do with the great advantage that their
solutions can be achieved by significant reduction in computational costs
(speed and memory).
A novel DDPG method with prioritized experience replay
Authors: Yuenan Hou, Lifeng Liu, Qing Wei, Xudong Xu, Chunlin Chen
Date: 2017-10-01 00:00:00
Proceeding: IEEE International Conference on Systems, Man and Cybernetics (SMC) 2017 10
Tasks: continuous-control, Continuous Control, MuJoCo, OpenAI Gym

Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator. To further improve the efficiency of the experience replay mechanism in DDPG and thus speeding up the training process, in this paper, a prioritized experience replay method is proposed for the DDPG algorithm, where prioritized sampling is adopted instead of uniform sampling. The proposed DDPG with prioritized experience replay is tested with an inverted pendulum task via OpenAI Gym. The experimental results show that DDPG with prioritized experience replay can reduce the training time and improve the stability of the training process, and is less sensitive to the changes of some hyperparameters such as the size of replay buffer, minibatch and the updating rate of the target network.
Free-breathing and ungated cardiac cine using navigator-less spiral SToRM
Authors: Abdul Haseeb Ahmed, Yasir Mohsin, Ruixi Zhou, Yang Yang, Michael Salerno, Prashant Nagpal, Mathews Jacob
Date: 2019-01-16 00:00:00

Purpose: To develop a free-breathing and ungated cardiac MRI scheme using an
iterative kernel low-rank algorithm and self-gated spiral sequence. Methods:
The data were acquired continuously over 8-16 seconds per slice without ECG
gating or breath holding using a golden-angle gradient echo spiral sequence.
The reconstruction scheme relies on the manifold structure of a dynamic data to
recover it from the highly undersampled measurements. An iterative kernel
low-rank algorithm is introduced to estimate the manifold structure of the
images, or equivalently the manifold Laplacian matrix, from the central k-space
regions. The iterative algorithm, coupled with the non-Cartesian acquisitions,
eliminates the need for dedicated navigators to estimate the manifold
Laplacian, unlike previous manifold methods, thus improving sampling
efficiency. Results: The proposed method is demonstrated in patients with
different breathing patterns and cardiac rates. The experiments show the
ability of our proposed iterative strategy to reduce spatial and temporal
blurring compared to state-of-the-art methods. Conclusion: The iterative SToRM
algorithm facilitates the extension of manifold regularization to
navigator-less spiral acquisitions, thus improving sampling efficiency. The
proposed scheme eliminates the need for breath-holding and ECG gating, while
facilitating the imaging of the cardiac function in different respiratory
phases.
Relay: A New IR for Machine Learning Frameworks
Authors: Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, Zachary Tatlock
Date: 2018-09-26 00:00:00
Tasks: BIG-bench Machine Learning, Recommendation Systems, Translation

Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet.
Joint Source-Target Self Attention with Locality Constraints
Authors: José A. R. Fonollosa, Noe Casas, Marta R. Costa-jussà
Date: 2019-05-16 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, Translation

The dominant neural machine translation models are based on the encoder-decoder structure, and many of them rely on an unconstrained receptive field over source and target sequences. In this paper we study a new architecture that breaks with both conventions. Our simplified architecture consists in the decoder part of a transformer model, based on self-attention, but with locality constraints applied on the attention receptive field. As input for training, both source and target sentences are fed to the network, which is trained as a language model. At inference time, the target tokens are predicted autoregressively starting with the source sequence as previous tokens. The proposed model achieves a new state of the art of 35.7 BLEU on IWSLT'14 German-English and matches the best reported results in the literature on the WMT'14 English-German and WMT'14 English-French translation benchmarks.
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
Authors: Wuyang Chen, Ziyu Jiang, Zhangyang Wang, Kexin Cui, Xiaoning Qian
Date: 2019-05-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, Land Cover Classification, Segmentation, Semantic Segmentation

Segmentation of ultra-high resolution images is increasingly demanded, yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits. Current approaches either downsample an ultra-high resolution image or crop it into small patches for separate processing. In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy. We propose collaborative Global-Local Networks (GLNet) to effectively preserve both global and local information in a highly memory-efficient manner. GLNet is composed of a global branch and a local branch, taking the downsampled entire image and its cropped local patches as respective inputs. For segmentation, GLNet deeply fuses feature maps from two branches, capturing both the high-resolution fine structures from zoomed-in local patches and the contextual dependency from the downsampled input. To further resolve the potential class imbalance problem between background and foreground regions, we present a coarse-to-fine variant of GLNet, also being memory-efficient. Extensive experiments and analyses have been performed on three real-world ultra-high aerial and medical image datasets (resolution up to 30 million pixels). With only one single 1080Ti GPU and less than 2GB memory used, our GLNet yields high-quality segmentation results and achieves much more competitive accuracy-memory usage trade-offs compared to state-of-the-arts.
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Authors: Feng Li, Xiaohe Wu, WangMeng Zuo, David Zhang, Lei Zhang
Date: 2019-05-16 00:00:00

Correlation filters (CFs) have been continuously advancing the state-of-the-art tracking performance and have been extensively studied in the recent few years. Most of the existing CF trackers adopt a cosine window to spatially reweight base image to alleviate boundary discontinuity. However, cosine window emphasizes more on the central region of base image and has the risk of contaminating negative training samples during model learning. On the other hand, spatial regularization deployed in many recent CF trackers plays a similar role as cosine window by enforcing spatial penalty on CF coefficients. Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization. When simply removing cosine window, CF with spatial regularization still suffers from small degree of boundary discontinuity. To tackle this issue, binary and Gaussian shaped mask functions are further introduced for eliminating boundary discontinuity while reweighting the estimation error of each training sample, and can be incorporated with multiple CF trackers with spatial regularization. In comparison to the counterparts with cosine window, our methods are effective in handling boundary discontinuity and sample contamination, thereby benefiting tracking performance. Extensive experiments on three benchmarks show that our methods perform favorably against the state-of-the-art trackers using either handcrafted or deep CNN features. The code is publicly available at https://github.com/lifeng9472/Removing_cosine_window_from_CF_trackers.
Random Sampling for Distributed Coded Matrix Multiplication
Authors: Wei-Ting Chang, Ravi Tandon
Date: 2019-05-16 00:00:00

Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent interest in using coding to speed up distributed matrix multiplication, that are robust to stragglers (i.e., machines that may perform slower computations). In many scenarios, instead of exact computation, approximate matrix multiplication, i.e., allowing for a tolerable error is also sufficient. Such approximate schemes make use of randomization techniques to speed up the computation process. In this paper, we initiate the study of approximate coded matrix multiplication, and investigate the joint synergies offered by randomization and coding. Specifically, we propose two coded randomized sampling schemes that use (a) codes to achieve a desired recovery threshold and (b) random sampling to obtain approximation of the matrix multiplication. Tradeoffs between the recovery threshold and approximation error obtained through random sampling are investigated for a class of coded matrix multiplication schemes.
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Authors: Sheshera Mysore, Zach Jensen, Edward Kim, Kevin Huang, Haw-Shiuan Chang, Emma Strubell, Jeffrey Flanigan, Andrew McCallum, Elsa Olivetti
Date: 2019-05-16 00:00:00
Proceeding: WS 2019 8

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.
Monocular Plan View Networks for Autonomous Driving
Authors: Dequan Wang, Coline Devin, Qi-Zhi Cai, Philipp Krähenbühl, Trevor Darrell
Date: 2019-05-16 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection

Convolutions on monocular dash cam videos capture spatial invariances in the image plane but do not explicitly reason about distances and depth. We propose a simple transformation of observations into a bird's eye view, also known as plan view, for end-to-end control. We detect vehicles and pedestrians in the first person view and project them into an overhead plan view. This representation provides an abstraction of the environment from which a deep network can easily deduce the positions and directions of entities. Additionally, the plan view enables us to leverage advances in 3D object detection in conjunction with deep policy learning. We evaluate our monocular plan view network on the photo-realistic Grand Theft Auto V simulator. A network using both a plan view and front view causes less than half as many collisions as previous detection-based methods and an order of magnitude fewer collisions than pure pixel-based policies.
Dynamically Fused Graph Network for Multi-hop Reasoning
Authors: Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei LI, Wei-Nan Zhang, Yong Yu
Date: 2019-05-16 00:00:00
Proceeding: ACL 2019 7
Tasks: Question Answering

Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. However, many difficult questions require multiple supporting evidence from scattered text among two or more documents. In this paper, we propose Dynamically Fused Graph Network(DFGN), a novel method to answer those questions requiring multiple scattered evidence and reasoning over them. Inspired by human's step-by-step reasoning behavior, DFGN includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores along the entity graph dynamically built from the text, and gradually finds relevant supporting entities from the given documents. We evaluate DFGN on HotpotQA, a public TBQA dataset requiring multi-hop reasoning. DFGN achieves competitive results on the public board. Furthermore, our analysis shows DFGN produces interpretable reasoning chains.
On Variational Bounds of Mutual Information
Authors: Ben Poole, Sherjil Ozair, Aaron van den Oord, Alexander A. Alemi, George Tucker
Date: 2019-05-16 00:00:00
Tasks: Representation Learning

Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable objectives, recent work has turned to variational bounds parameterized by neural networks, but the relationships and tradeoffs between these bounds remains unclear. In this work, we unify these recent developments in a single framework. We find that the existing variational lower bounds degrade when the MI is large, exhibiting either high bias or high variance. To address this problem, we introduce a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance. On high-dimensional, controlled problems, we empirically characterize the bias and variance of the bounds and their gradients and demonstrate the effectiveness of our new bounds for estimation and representation learning.