pwc-archive/papers-with-abstracts huggingface dataset:
Collective Learning From Diverse Datasets for Entity Typing in the Wild
Authors: Abhishek Abhishek, Amar Prakash Azad, Balaji Ganesan, Ashish Anand, Amit Awekar
Date: 2018-10-20 00:00:00
Tasks: Entity Typing, Sentence

Entity typing (ET) is the problem of assigning labels to given entity mentions in a sentence. Existing works for ET require knowledge about the domain and target label set for a given test instance. ET in the absence of such knowledge is a novel problem that we address as ET in the wild. We hypothesize that the solution to this problem is to build supervised models that generalize better on the ET task as a whole, rather than a specific dataset. In this direction, we propose a Collective Learning Framework (CLF), which enables learning from diverse datasets in a unified way. The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets. Then it builds a single neural network classifier using UHLS, label mapping, and a partial loss function. The single classifier predicts the finest possible label across all available domains even though these labels may not be present in any domain-specific dataset. We also propose a set of evaluation schemes and metrics to evaluate the performance of models in this novel problem. Extensive experimentation on seven diverse real-world datasets demonstrates the efficacy of our CLF.
Improved Techniques for GAN based Facial Inpainting
Authors: Avisek Lahiri, Arnav Jain, Divyasri Nadendla, Prabir Kumar Biswas
Date: 2018-10-20 00:00:00
Tasks: Face Recognition, Facial Inpainting, Generative Adversarial Network

In this paper we present several architectural and optimization recipes for
generative adversarial network(GAN) based facial semantic inpainting. Current
benchmark models are susceptible to initial solutions of non-convex
optimization criterion of GAN based inpainting. We present an end-to-end
trainable parametric network to deterministically start from good initial
solutions leading to more photo realistic reconstructions with significant
optimization speed up. For the first time, we show how to efficiently extend
GAN based single image inpainter models to sequences by a)learning to
initialize a temporal window of solutions with a recurrent neural network and
b)imposing a temporal smoothness loss(during iterative optimization) to respect
the redundancy in temporal dimension of a sequence. We conduct comprehensive
empirical evaluations on CelebA images and pseudo sequences followed by real
life videos of VidTIMIT dataset. The proposed method significantly outperforms
current GAN based state-of-the-art in terms of reconstruction quality with a
simultaneous speedup of over 15$\times$. We also show that our proposed model
is better in preserving facial identity in a sequence even without explicitly
using any face recognition module during training.
Sequential Context Encoding for Duplicate Removal
Authors: Lu Qi, Shu Liu, Jianping Shi, Jiaya Jia
Date: 2018-10-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decoder, Object, object-detection, Object Detection

Duplicate removal is a critical step to accomplish a reasonable amount of
predictions in prevalent proposal-based object detection frameworks. Albeit
simple and effective, most previous algorithms utilize a greedy process without
making sufficient use of properties of input data. In this work, we design a
new two-stage framework to effectively select the appropriate proposal
candidate for each object. The first stage suppresses most of easy negative
object proposals, while the second stage selects true positives in the reduced
proposal set. These two stages share the same network structure, \ie, an
encoder and a decoder formed as recurrent neural networks (RNN) with global
attention and context gate. The encoder scans proposal candidates in a
sequential manner to capture the global context information, which is then fed
to the decoder to extract optimal proposals. In our extensive experiments, the
proposed method outperforms other alternatives by a large margin.
Image Inpainting via Generative Multi-column Convolutional Neural Networks
Authors: Yi Wang, Xin Tao, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia
Date: 2018-10-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Inpainting

In this paper, we propose a generative multi-column network for image
inpainting. This network synthesizes different image components in a parallel
manner within one stage. To better characterize global structures, we design a
confidence-driven reconstruction loss while an implicit diversified MRF
regularization is adopted to enhance local details. The multi-column network
combined with the reconstruction and MRF loss propagates local and global
information derived from context to the target inpainting regions. Extensive
experiments on challenging street view, face, natural objects and scenes
manifest that our method produces visual compelling results even without
previously common post-processing.
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement
Authors: Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, Ming-Hsuan Yang
Date: 2018-10-20 00:00:00
Tasks: Computational Efficiency, Denoising, Motion Compensation, Motion Estimation, Optical Flow Estimation, Super-Resolution, Video Enhancement, Video Frame Interpolation

Motion estimation (ME) and motion compensation (MC) have been widely used for classical video frame interpolation systems over the past decades. Recently, a number of data-driven frame interpolation methods based on convolutional neural networks have been proposed. However, existing learning based methods typically estimate either flow or compensation kernels, thereby limiting performance on both computational efficiency and interpolation accuracy. In this work, we propose a motion estimation and compensation driven neural network for video frame interpolation. A novel adaptive warping layer is developed to integrate both optical flow and interpolation kernels to synthesize target frame pixels. This layer is fully differentiable such that both the flow and kernel estimation networks can be optimized jointly. The proposed model benefits from the advantages of motion estimation and compensation methods without using hand-crafted features. Compared to existing methods, our approach is computationally efficient and able to generate more visually appealing results. Furthermore, the proposed MEMC-Net can be seamlessly adapted to several video enhancement tasks, e.g., super-resolution, denoising, and deblocking. Extensive quantitative and qualitative evaluations demonstrate that the proposed method performs favorably against the state-of-the-art video frame interpolation and enhancement algorithms on a wide range of datasets.
Attribute-aware Collaborative Filtering: Survey and Classification
Authors: Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin
Date: 2018-10-20 00:00:00
Tasks: Attribute, Classification, Collaborative Filtering, General Classification, Survey

Attribute-aware CF models aims at rating prediction given not only the
historical rating from users to items, but also the information associated with
users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This
paper surveys works in the past decade developing attribute-aware CF systems,
and discovered that mathematically they can be classified into four different
categories. We provide the readers not only the high level mathematical
interpretation of the existing works in this area but also the mathematical
insight for each category of models. Finally we provide in-depth experiment
results comparing the effectiveness of the major works in each category.
Design of robust H_inf fuzzy output feedback controller for affine nonlinear systems:Fuzzy Lyapunov function approach
Authors: Leila Rajabpour, Mokhtar Shasadeghi, Alireza Barzegar
Date: 2018-10-20 00:00:00

In this paper, we propose a new systematic approach based on nonquadratic
Lyapunov function and technique of introducing slack matrices, for a class of
affine nonlinear systems with disturbance. To achieve the goal, first, the
affine nonlinear system is represented via Takagi-Sugeno (T-S) fuzzy bilinear
model. Subsequently, the robust H_inf controller is designed based on parallel
distributed compensation (PDC) scheme. Then, the stability conditions are
derived in terms of linear matrix inequalities (LMIs) by utilizing Lyapunov
function. Moreover, some slack matrices are proposed to reduce the
conservativeness of the LMI stability conditions. Finally, for illustrating the
merits and verifying the effectiveness of the proposed approach, the
application of an isothermal continuous stirred tank reactor (CSTR) for Van de
Vusse reactor is discussed in details.
BCR-Net: a neural network based on the nonstandard wavelet form
Authors: Yuwei Fan, Cindy Orozco Bohorquez, Lexing Ying
Date: 2018-10-20 00:00:00
Tasks: Form

This paper proposes a novel neural network architecture inspired by the
nonstandard form proposed by Beylkin, Coifman, and Rokhlin in [Communications
on Pure and Applied Mathematics, 44(2), 141-183]. The nonstandard form is a
highly effective wavelet-based compression scheme for linear integral
operators. In this work, we first represent the matrix-vector product algorithm
of the nonstandard form as a linear neural network where every scale of the
multiresolution computation is carried out by a locally connected linear
sub-network. In order to address nonlinear problems, we propose an extension,
called BCR-Net, by replacing each linear sub-network with a deeper and more
powerful nonlinear one. Numerical results demonstrate the efficiency of the new
architecture by approximating nonlinear maps that arise in homogenization
theory and stochastic computation.
Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion
Authors: Wenjun Yan, Yuanyuan Wang, Zeju Li, Rob J. van der Geest, Qian Tao
Date: 2018-10-20 00:00:00
Tasks: Diagnostic, Left Ventricle Segmentation, LV Segmentation, Optical Flow Estimation, Segmentation

Quantitative assessment of left ventricle (LV) function from cine MRI has
significant diagnostic and prognostic value for cardiovascular disease
patients. The temporal movement of LV provides essential information on the
contracting/relaxing pattern of heart, which is keenly evaluated by clinical
experts in clinical practice. Inspired by the expert way of viewing Cine MRI,
we propose a new CNN module that is able to incorporate the temporal
information into LV segmentation from cine MRI. In the proposed CNN, the
optical flow (OF) between neighboring frames is integrated and aggregated at
feature level, such that temporal coherence in cardiac motion can be taken into
account during segmentation. The proposed module is integrated into the U-net
architecture without need of additional training. Furthermore, dilated
convolution is introduced to improve the spatial accuracy of segmentation.
Trained and tested on the Cardiac Atlas database, the proposed network resulted
in a Dice index of 95% and an average perpendicular distance of 0.9 pixels for
the middle LV contour, significantly outperforming the original U-net that
processes each frame individually. Notably, the proposed method improved the
temporal coherence of LV segmentation results, especially at the LV apex and
base where the cardiac motion is difficult to follow.
Learning Models with Uniform Performance via Distributionally Robust Optimization
Authors: John Duchi, Hongseok Namkoong
Date: 2018-10-20 00:00:00
Tasks: Stochastic Optimization

A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects. We develop and analyze a distributionally robust stochastic optimization (DRO) framework that learns a model providing good performance against perturbations to the data-generating distribution. We give a convex formulation for the problem, providing several convergence guarantees. We prove finite-sample minimax upper and lower bounds, showing that distributional robustness sometimes comes at a cost in convergence rates. We give limit theorems for the learned parameters, where we fully specify the limiting distribution so that confidence intervals can be computed. On real tasks including generalizing to unknown subpopulations, fine-grained recognition, and providing good tail performance, the distributionally robust approach often exhibits improved performance.
Renormalized Normalized Maximum Likelihood and Three-Part Code Criteria For Learning Gaussian Networks
Authors: Borzou Alipourfard, Jean X. Gao
Date: 2018-10-20 00:00:00

Score based learning (SBL) is a promising approach for learning Bayesian networks in the discrete domain. However, when employing SBL in the continuous domain, one is either forced to move the problem to the discrete domain or use metrics such as BIC/AIC, and these approaches are often lacking. Discretization can have an undesired impact on the accuracy of the results, and BIC/AIC can fall short of achieving the desired accuracy. In this paper, we introduce two new scoring metrics for scoring Bayesian networks in the continuous domain: the three-part minimum description length and the renormalized normalized maximum likelihood metric. We rely on the minimum description length principle in formulating these metrics. The metrics proposed are free of hyperparameters, decomposable, and are asymptotically consistent. We evaluate our solution by studying the convergence rate of the learned graph to the generating network and, also, the structural hamming distance of the learned graph to the generating network. Our evaluations show that the proposed metrics outperform their competitors, the BIC/AIC metrics. Furthermore, using the proposed RNML metric, SBL will have the fastest rate of convergence with the smallest structural hamming distance to the generating network.
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
Authors: Mark Hamilton, Sudarshan Raghunathan, Ilya Matiach, Andrew Schonhoffer, Anand Raman, Eli Barzilay, Karthik Rajendran, Dalitso Banda, Casey Jisoo Hong, Manon Knoertzer, Ben Brodsky, Minsoo Thigpen, Janhavi Suresh Mahajan, Courtney Cochrane, Abhiram Eswaran, Ari Green
Date: 2018-10-20 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, object-detection, Object Detection

We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation. Furthermore, we present a novel system called Spark Serving that allows users to run any Apache Spark program as a distributed, sub-millisecond latency web service backed by their existing Spark Cluster. All MMLSpark contributions have the same API to enable simple composition across frameworks and usage across batch, streaming, and RESTful web serving scenarios on static, elastic, or serverless clusters. We showcase MMLSpark by creating a method for deep object detection capable of learning without human labeled data and demonstrate its effectiveness for Snow Leopard conservation.
Quantifying the Burden of Exploration and the Unfairness of Free Riding
Authors: Christopher Jung, Sampath Kannan, Neil Lutz
Date: 2018-10-20 00:00:00

We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have $n$ different decision makers (agents). In the simple stochastic setting, we show that a "free-riding" agent observing another "self-reliant" agent can achieve just $O(1)$ regret, as opposed to the regret lower bound of $\Omega (\log t)$ when one decision maker is playing in isolation. This result holds whenever the self-reliant agent's strategy satisfies either one of two assumptions: (1) each arm is pulled at least $\gamma \ln t$ times in expectation for a constant $\gamma$ that we compute, or (2) the self-reliant agent achieves $o(t)$ realized regret with high probability. Both of these assumptions are satisfied by standard zero-regret algorithms. Under the second assumption, we further show that the free rider only needs to observe the number of times each arm is pulled by the self-reliant agent, and not the rewards realized. In the linear contextual setting, each arm has a distribution over parameter vectors, each agent has a context vector, and the reward realized when an agent pulls an arm is the inner product of that agent's context vector with a parameter vector sampled from the pulled arm's distribution. We show that the free rider can achieve $O(1)$ regret in this setting whenever the free rider's context is a small (in $L_2$-norm) linear combination of other agents' contexts and all other agents pull each arm $\Omega (\log t)$ times with high probability. Again, this condition on the self-reliant players is satisfied by standard zero-regret algorithms like UCB. We also prove a number of lower bounds.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Authors: Xin Tang, Shanbo Cheng, Loc Do, Zhiyu Min, Feng Ji, Heng Yu, Ji Zhang, Haiqin Chen
Date: 2018-10-20 00:00:00
Tasks: Machine Translation, Semantic Similarity, Semantic Textual Similarity, Sentence, STS, Translation

Measuring the semantic similarity between two sentences (or Semantic Textual
Similarity - STS) is fundamental in many NLP applications. Despite the
remarkable results in supervised settings with adequate labeling, little
attention has been paid to this task in low-resource languages with
insufficient labeling. Existing approaches mostly leverage machine translation
techniques to translate sentences into rich-resource language. These approaches
either beget language biases, or be impractical in industrial applications
where spoken language scenario is more often and rigorous efficiency is
required. In this work, we propose a multilingual framework to tackle the STS
task in a low-resource language e.g. Spanish, Arabic , Indonesian and Thai, by
utilizing the rich annotation data in a rich resource language, e.g. English.
Our approach is extended from a basic monolingual STS framework to a shared
multilingual encoder pretrained with translation task to incorporate
rich-resource language data. By exploiting the nature of a shared multilingual
encoder, one sentence can have multiple representations for different target
translation language, which are used in an ensemble model to improve similarity
evaluation. We demonstrate the superiority of our method over other state of
the art approaches on SemEval STS task by its significant improvement on non-MT
method, as well as an online industrial product where MT method fails to beat
baseline while our approach still has consistently improvements.
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
Authors: Eda Okur, Hakan Demir, Arzucan Özgür
Date: 2018-10-20 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

Recently, due to the increasing popularity of social media, the necessity for
extracting information from informal text types, such as microblog texts, has
gained significant attention. In this study, we focused on the Named Entity
Recognition (NER) problem on informal text types for Turkish. We utilized a
semi-supervised learning approach based on neural networks. We applied a fast
unsupervised method for learning continuous representations of words in vector
space. We made use of these obtained word embeddings, together with language
independent features that are engineered to work better on informal text types,
for generating a Turkish NER system on microblog texts. We evaluated our
Turkish NER system on Twitter messages and achieved better F-score performances
than the published results of previously proposed NER systems on Turkish
tweets. Since we did not employ any language dependent features, we believe
that our method can be easily adapted to microblog texts in other
morphologically rich languages.
Learning Material-Aware Local Descriptors for 3D Shapes
Authors: Hubert Lin, Melinos Averkiou, Evangelos Kalogerakis, Balazs Kovacs, Siddhant Ranade, Vladimir G. Kim, Siddhartha Chaudhuri, Kavita Bala
Date: 2018-10-20 00:00:00
Tasks: Material Classification, Retrieval

Material understanding is critical for design, geometric modeling, and
analysis of functional objects. We enable material-aware 3D shape analysis by
employing a projective convolutional neural network architecture to learn
material- aware descriptors from view-based representations of 3D points for
point-wise material classification or material- aware retrieval. Unfortunately,
only a small fraction of shapes in 3D repositories are labeled with physical
mate- rials, posing a challenge for learning methods. To address this
challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material
labels. We focus on furniture models which exhibit interesting structure and
material variabil- ity. In addition, we also contribute a high-quality expert-
labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We
further apply a mesh-aware con- ditional random field, which incorporates
rotational and reflective symmetries, to smooth our local material predic-
tions across neighboring surface patches. We demonstrate the effectiveness of
our learned descriptors for automatic texturing, material-aware retrieval, and
physical simulation. The dataset and code will be publicly available.
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods
Authors: Robert M. Freund, Paul Grigas, Rahul Mazumder
Date: 2018-10-20 00:00:00
Tasks: Binary Classification, General Classification, regression

Logistic regression is one of the most popular methods in binary
classification, wherein estimation of model parameters is carried out by
solving the maximum likelihood (ML) optimization problem, and the ML estimator
is defined to be the optimal solution of this problem. It is well known that
the ML estimator exists when the data is non-separable, but fails to exist when
the data is separable. First-order methods are the algorithms of choice for
solving large-scale instances of the logistic regression problem. In this
paper, we introduce a pair of condition numbers that measure the degree of
non-separability or separability of a given dataset in the setting of binary
classification, and we study how these condition numbers relate to and inform
the properties and the convergence guarantees of first-order methods. When the
training data is non-separable, we show that the degree of non-separability
naturally enters the analysis and informs the properties and convergence
guarantees of two standard first-order methods: steepest descent (for any given
norm) and stochastic gradient descent. Expanding on the work of Bach, we also
show how the degree of non-separability enters into the analysis of linear
convergence of steepest descent (without needing strong convexity), as well as
the adaptive convergence of stochastic gradient descent. When the training data
is separable, first-order methods rather curiously have good empirical success,
which is not well understood in theory. In the case of separable data, we
demonstrate how the degree of separability enters into the analysis of $\ell_2$
steepest descent and stochastic gradient descent for delivering
approximate-maximum-margin solutions with associated computational guarantees
as well. This suggests that first-order methods can lead to statistically
meaningful solutions in the separable case, even though the ML solution does
not exist.