pwc-archive/papers-with-abstracts huggingface dataset:
ODN: Opening the Deep Network for Open-set Action Recognition
Authors: Yu Shu, Yemin Shi, Yao-Wei Wang, Yixiong Zou, Qingsheng Yuan, Yonghong Tian
Date: 2019-01-23 00:00:00
Tasks: Action Recognition, Open Set Action Recognition, Temporal Action Localization, Triplet

In recent years, the performance of action recognition has been significantly
improved with the help of deep neural networks. Most of the existing action
recognition works hold the \textit{closed-set} assumption that all action
categories are known beforehand while deep networks can be well trained for
these categories. However, action recognition in the real world is essentially
an \textit{open-set} problem, namely, it is impossible to know all action
categories beforehand and consequently infeasible to prepare sufficient
training samples for those emerging categories. In this case, applying
closed-set recognition methods will definitely lead to unseen-category errors.
To address this challenge, we propose the Open Deep Network (ODN) for the
open-set action recognition task. Technologically, ODN detects new categories
by applying a multi-class triplet thresholding method, and then dynamically
reconstructs the classification layer and "opens" the deep network by adding
predictors for new categories continually. In order to transfer the learned
knowledge to the new category, two novel methods, Emphasis Initialization and
Allometry Training, are adopted to initialize and incrementally train the new
predictor so that only few samples are needed to fine-tune the model. Extensive
experiments show that ODN can effectively detect and recognize new categories
with little human intervention, thus applicable to the open-set action
recognition tasks in the real world. Moreover, ODN can even achieve comparable
performance to some closed-set methods.
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
Authors: Nairouz Mrabah, Naimul Mefraz Khan, Riadh Ksantini, Zied Lachiri
Date: 2019-01-23 00:00:00
Tasks: Clustering, Deep Clustering, Image Clustering

In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static during the training process. The absence of concrete supervision suggests that smooth dynamics should be integrated. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that overcomes a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.
Adaptive Exact Learning of Decision Trees from Membership Queries
Authors: Nader H. Bshouty, Catherine A. Haddad-Zaknoon
Date: 2019-01-23 00:00:00
Tasks: scientific discovery

In this paper we study the adaptive learnability of decision trees of depth
at most $d$ from membership queries. This has many applications in automated
scientific discovery such as drugs development and software update problem.
Feldman solves the problem in a randomized polynomial time algorithm that asks
$\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic
polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve
the query complexity of both algorithms. We give a randomized polynomial time
algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a
deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$
queries.
Automated Essay Scoring based on Two-Stage Learning
Authors: Jiawei Liu, Yang Xu, Yaguang Zhu
Date: 2019-01-23 00:00:00
Tasks: Automated Essay Scoring, Vocal Bursts Valence Prediction

Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates the advantages of both feature-engineered and end-to-end AES models. In experiments, we compare TSLF against a number of strong baselines, and the results demonstrate the effectiveness and robustness of our models. TSLF surpasses all the baselines on five-eighths of prompts and achieves new state-of-the-art average performance when without negative samples. After adding some adversarial essays to the original datasets, TSLF outperforms the feature-engineered and end-to-end baselines to a great extent, and shows great robustness.
Thompson Sampling for a Fatigue-aware Online Recommendation System
Authors: Yunjuan Wang, Theja Tulabandhula
Date: 2019-01-23 00:00:00
Tasks: Thompson Sampling

In this paper we consider an online recommendation setting, where a platform
recommends a sequence of items to its users at every time period. The users
respond by selecting one of the items recommended or abandon the platform due
to fatigue from seeing less useful items. Assuming a parametric stochastic
model of user behavior, which captures positional effects of these items as
well as the abandoning behavior of users, the platform's goal is to recommend
sequences of items that are competitive to the single best sequence of items in
hindsight, without knowing the true user model a priori. Naively applying a
stochastic bandit algorithm in this setting leads to an exponential dependence
on the number of items. We propose a new Thompson sampling based algorithm with
expected regret that is polynomial in the number of items in this combinatorial
setting, and performs extremely well in practice.
Deep-Learning Inversion of Seismic Data
Authors: Shucai Li, Bin Liu, Yuxiao Ren, Yangkang Chen, Senlin Yang, Yunhai Wang, Peng Jiang
Date: 2019-01-23 00:00:00
Tasks: Deep Learning, Seismic Inversion, Time Series Analysis

We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i.e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs). The conventional way of addressing this ill-posed inversion problem is through iterative algorithms, which suffer from poor nonlinear mapping and strong nonuniqueness. Other attempts may either import human intervention errors or underuse seismic data. The challenge for DNNs mainly lies in the weak spatial correspondence, the uncertain reflection-reception relationship between seismic data and velocity model, as well as the time-varying property of seismic data. To tackle these challenges, we propose end-to-end seismic inversion networks (SeisInvNets) with novel components to make the best use of all seismic data. Specifically, we start with every seismic trace and enhance it with its neighborhood information, its observation setup, and the global context of its corresponding seismic profile. From the enhanced seismic traces, the spatially aligned feature maps can be learned and further concatenated to reconstruct a velocity model. In general, we let every seismic trace contribute to the reconstruction of the whole velocity model by finding spatial correspondence. The proposed SeisInvNet consistently produces improvements over the baselines and achieves promising performance on our synthesized and proposed SeisInv data set according to various evaluation metrics. The inversion results are more consistent with the target from the aspects of velocity values, subsurface structures, and geological interfaces. Moreover, the mechanism and the generalization of the proposed method are discussed and verified. Nevertheless, the generalization of deep-learning-based inversion methods on real data is still challenging and considering physics may be one potential solution.
Joint group and residual sparse coding for image compressive sensing
Authors: Lizhao Li, Song Xiao
Date: 2019-01-23 00:00:00
Tasks: Compressive Sensing

Nonlocal self-similarity and group sparsity have been widely utilized in
image compressive sensing (CS). However, when the sampling rate is low, the
internal prior information of degraded images may be not enough for accurate
restoration, resulting in loss of image edges and details. In this paper, we
propose a joint group and residual sparse coding method for CS image recovery
(JGRSC-CS). In the proposed JGRSC-CS, patch group is treated as the basic unit
of sparse coding and two dictionaries (namely internal and external
dictionaries) are applied to exploit the sparse representation of each group
simultaneously. The internal self-adaptive dictionary is used to remove
artifacts, and an external Gaussian Mixture Model (GMM) dictionary, learned
from clean training images, is used to enhance details and texture. To make the
proposed method effective and robust, the split Bregman method is adopted to
reconstruct the whole image. Experimental results manifest the proposed
JGRSC-CS algorithm outperforms existing state-of-the-art methods in both peak
signal to noise ratio (PSNR) and visual quality.
Robust computation with rhythmic spike patterns
Authors: E. Paxon Frady, Friedrich T. Sommer
Date: 2019-01-23 00:00:00

Information coding by precise timing of spikes can be faster and more
energy-efficient than traditional rate coding. However, spike-timing codes are
often brittle, which has limited their use in theoretical neuroscience and
computing applications. Here, we propose a novel type of attractor neural
network in complex state space, and show how it can be leveraged to construct
spiking neural networks with robust computational properties through a
phase-to-timing mapping. Building on Hebbian neural associative memories, like
Hopfield networks, we first propose threshold phasor associative memory (TPAM)
networks. Complex phasor patterns whose components can assume continuous-valued
phase angles and binary magnitudes can be stored and retrieved as stable fixed
points in the network dynamics. TPAM achieves high memory capacity when storing
sparse phasor patterns, and we derive the energy function that governs its
fixed point attractor dynamics. Second, through simulation experiments we show
how the complex algebraic computations in TPAM can be approximated by a
biologically plausible network of integrate-and-fire neurons with synaptic
delays and recurrently connected inhibitory interneurons. The fixed points of
TPAM in the complex domain are commensurate with stable periodic states of
precisely timed spiking activity that are robust to perturbation. The link
established between rhythmic firing patterns and complex attractor dynamics has
implications for the interpretation of spike patterns seen in neuroscience, and
can serve as a framework for computation in emerging neuromorphic devices.
A First Look at Deep Learning Apps on Smartphones
Authors: Mengwei Xu, Jiawei Liu, Yuanqiang Liu, Felix Xiaozhu Lin, Yunxin Liu, Xuanzhe Liu
Date: 2018-11-08 00:00:00
Tasks: Deep Learning

We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep learning in the wild. To this end, we build a new static tool that dissects apps and analyzes their deep learning functions. Our study answers threefold questions: what are the early adopter apps of deep learning, what do they use deep learning for, and how do their deep learning models look like. Our study has strong implications for app developers, smartphone vendors, and deep learning R\&D. On one hand, our findings paint a promising picture of deep learning for smartphones, showing the prosperity of mobile deep learning frameworks as well as the prosperity of apps building their cores atop deep learning. On the other hand, our findings urge optimizations on deep learning models deployed on smartphones, the protection of these models, and validation of research ideas on these models.
Neural-Guided Symbolic Regression with Asymptotic Constraints
Authors: Li Li, Minjie Fan, Rishabh Singh, Patrick Riley
Date: 2019-01-23 00:00:00
Tasks: regression, Symbolic Regression

Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about the unknown expression not only provide more information beyond just values at some inputs, but also effectively constrain the search space. We identify the asymptotic constraints of leading polynomial powers as the function approaches zero and infinity as useful constraints and create a system to use them for symbolic regression. The first part of the system is a conditional production rule generating neural network which preferentially generates production rules to construct expressions with the desired leading powers, producing novel expressions outside the training domain. The second part, which we call Neural-Guided Monte Carlo Tree Search, uses the network during a search to find an expression that conforms to a set of data points and desired leading powers. Lastly, we provide an extensive experimental validation on thousands of target expressions showing the efficacy of our system compared to exiting methods for finding unknown functions outside of the training set.
Max-margin Class Imbalanced Learning with Gaussian Affinity
Authors: Munawar Hayat, Salman Khan, Waqas Zamir, Jianbing Shen, Ling Shao
Date: 2019-01-23 00:00:00
Tasks: Classification, Clustering, Diversity, General Classification

Real-world object classes appear in imbalanced ratios. This poses a
significant challenge for classifiers which get biased towards frequent
classes. We hypothesize that improving the generalization capability of a
classifier should improve learning on imbalanced datasets. Here, we introduce
the first hybrid loss function that jointly performs classification and
clustering in a single formulation. Our approach is based on an `affinity
measure' in Euclidean space that leads to the following benefits: (1) direct
enforcement of maximum margin constraints on classification boundaries, (2) a
tractable way to ensure uniformly spaced and equidistant cluster centers, (3)
flexibility to learn multiple class prototypes to support diversity and
discriminability in feature space. Our extensive experiments demonstrate the
significant performance improvements on visual classification and verification
tasks on multiple imbalanced datasets. The proposed loss can easily be plugged
in any deep architecture as a differentiable block and demonstrates robustness
against different levels of data imbalance and corrupted labels.
Unified estimation framework for unnormalized models with statistical efficiency
Authors: Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi, Takeru Matsuda
Date: 2019-01-23 00:00:00
Tasks: parameter estimation

The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some consistent estimators have been proposed earlier, the problem of statistical efficiency remains. In this study, we propose a unified, statistically efficient estimation framework for unnormalized models and several efficient estimators, whose asymptotic variance is the same as the MLE. The computational cost of these estimators is also reasonable and they can be employed whether the sample space is discrete or continuous. The loss functions of the proposed estimators are derived by combining the following two methods: (1) density-ratio matching using Bregman divergence, and (2) plugging-in nonparametric estimators. We also analyze the properties of the proposed estimators when the unnormalized models are misspecified. The experimental results demonstrate the advantages of our method over existing approaches.
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz
Authors: Shaojie Tang, Jing Yuan
Date: 2019-01-23 00:00:00
Tasks: Active Learning, Assortment Optimization, Question Selection

Personality quiz is a powerful tool that enables costumer segmentation by actively asking them questions, and marketers are using it as an effective method of generating leads and increasing e-commerce sales. In this paper, we study the problem of how to select and sequence a group of quiz questions so as to optimize the quality of customer segmentation. We assume that the customer will sequentially scan the list of questions. After reading a question, the customer makes two, possibly correlated, random decisions: 1) she first decides whether to answer this question or not, and then 2) decides whether to continue reading the next question or not. We further assume that the utility of questions that have been answered can be captured by a monotone and submodular function. In general, our problem falls into the category of non-adaptive active learning based customer profiling. Note that under the our model, the probability of a question being answered depends on the location of that question, as well as the set of other questions placed ahead of that question, this makes our problem fundamentally different from existing studies on submodular optimization. We develop a series of question selection and sequencing strategies with provable performance bound. Although we focus on the application of quiz design in this paper, our results apply to a broad range of applications, including assortment optimization with position bias effect.
Exploring Uncertainty in Conditional Multi-Modal Retrieval Systems
Authors: Ahmed Taha, Yi-Ting Chen, Xitong Yang, Teruhisa Misu, Larry Davis
Date: 2019-01-23 00:00:00
Tasks: Action Understanding, Person Re-Identification, regression, Retrieval, Triplet

We cast visual retrieval as a regression problem by posing triplet loss as a
regression loss. This enables epistemic uncertainty estimation using dropout as
a Bayesian approximation framework in retrieval. Accordingly, Monte Carlo (MC)
sampling is leveraged to boost retrieval performance. Our approach is evaluated
on two applications: person re-identification and autonomous car driving.
Comparable state-of-the-art results are achieved on multiple datasets for the
former application.
  We leverage the Honda driving dataset (HDD) for autonomous car driving
application. It provides multiple modalities and similarity notions for
ego-motion action understanding. Hence, we present a multi-modal conditional
retrieval network. It disentangles embeddings into separate representations to
encode different similarities. This form of joint learning eliminates the need
to train multiple independent networks without any performance degradation.
Quantitative evaluation highlights our approach competence, achieving 6%
improvement in a highly uncertain environment.
Product-Aware Answer Generation in E-Commerce Question-Answering
Authors: Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan
Date: 2019-01-23 00:00:00
Tasks: Answer Generation, Question Answering

In e-commerce portals, generating answers for product-related questions has
become a crucial task. In this paper, we propose the task of product-aware
answer generation, which tends to generate an accurate and complete answer from
large-scale unlabeled e-commerce reviews and product attributes. Unlike
existing question-answering problems, answer generation in e-commerce confronts
three main challenges: (1) Reviews are informal and noisy; (2) joint modeling
of reviews and key-value product attributes is challenging; (3) traditional
methods easily generate meaningless answers. To tackle above challenges, we
propose an adversarial learning based model, named PAAG, which is composed of
three components: a question-aware review representation module, a key-value
memory network encoding attributes, and a recurrent neural network as a
sequence generator. Specifically, we employ a convolutional discriminator to
distinguish whether our generated answer matches the facts. To extract the
salience part of reviews, an attention-based review reader is proposed to
capture the most relevant words given the question. Conducted on a large-scale
real-world e-commerce dataset, our extensive experiments verify the
effectiveness of each module in our proposed model. Moreover, our experiments
show that our model achieves the state-of-the-art performance in terms of both
automatic metrics and human evaluations.
Simultaneous Subspace Clustering and Cluster Number Estimating based on Triplet Relationship
Authors: Jie Liang, Jufeng Yang, Ming-Ming Cheng, Paul L. Rosin, Liang Wang
Date: 2019-01-23 00:00:00
Tasks: Clustering, Model Selection, Triplet

In this paper we propose a unified framework to simultaneously discover the
number of clusters and group the data points into them using subspace
clustering. Real data distributed in a high-dimensional space can be
disentangled into a union of low-dimensional subspaces, which can benefit
various applications. To explore such intrinsic structure, state-of-the-art
subspace clustering approaches often optimize a self-representation problem
among all samples, to construct a pairwise affinity graph for spectral
clustering. However, a graph with pairwise similarities lacks robustness for
segmentation, especially for samples which lie on the intersection of two
subspaces. To address this problem, we design a hyper-correlation based data
structure termed as the \textit{triplet relationship}, which reveals high
relevance and local compactness among three samples. The triplet relationship
can be derived from the self-representation matrix, and be utilized to
iteratively assign the data points to clusters. Three samples in each triplet
are encouraged to be highly correlated and are considered as a meta-element
during clustering, which show more robustness than pairwise relationships when
segmenting two densely distributed subspaces. Based on the triplet
relationship, we propose a unified optimizing scheme to automatically calculate
clustering assignments. Specifically, we optimize a model selection reward and
a fusion reward by simultaneously maximizing the similarity of triplets from
different clusters while minimizing the correlation of triplets from same
cluster. The proposed algorithm also automatically reveals the number of
clusters and fuses groups to avoid over-segmentation. Extensive experimental
results on both synthetic and real-world datasets validate the effectiveness
and robustness of the proposed method.
Context-Sensitive Malicious Spelling Error Correction
Authors: Hongyu Gong, Yuchen Li, Suma Bhat, Pramod Viswanath
Date: 2019-01-23 00:00:00
Tasks: Spam detection, Spelling Correction, Word Embeddings

Misspelled words of the malicious kind work by changing specific keywords and
are intended to thwart existing automated applications for cyber-environment
control such as harassing content detection on the Internet and email spam
detection. In this paper, we focus on malicious spelling correction, which
requires an approach that relies on the context and the surface forms of
targeted keywords. In the context of two applications--profanity detection and
email spam detection--we show that malicious misspellings seriously degrade
their performance. We then propose a context-sensitive approach for malicious
spelling correction using word embeddings and demonstrate its superior
performance compared to state-of-the-art spell checkers.