pwc-archive/papers-with-abstracts huggingface dataset:
Feature Concatenation Multi-view Subspace Clustering
Authors: Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, Yaochen Li
Date: 2019-01-30 00:00:00
Tasks: Clustering, Multi-view Subspace Clustering

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features straightforward. However, feature concatenation is a natural way to combine multi-view data. To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data. Specifically, multi-view data are concatenated into a joint representation firstly, then, $l_{2,1}$-norm is integrated into the objective function to deal with the sample-specific and cluster-specific corruptions of multiple views. Moreover, a graph regularized FCMSC is also proposed in this paper to explore both the consensus information and complementary information of multi-view data for clustering. It is noteworthy that the obtained coefficient matrix is not derived by simply applying the Low-Rank Representation (LRR) to concatenated features directly. Finally, an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimize the objective functions. Comprehensive experiments on six real-world datasets illustrate the superiority of the proposed methods over several state-of-the-art approaches for multi-view clustering.
On the Calibration of Multiclass Classification with Rejection
Authors: Chenri Ni, Nontawat Charoenphakdee, Junya Honda, Masashi Sugiyama
Date: 2019-01-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Classification, General Classification

We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification. First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case. We analyze this approach for the multiclass case and derive a general condition for calibration to the Bayes-optimal solution, which suggests that calibration is hard to achieve by general loss functions unlike the binary case. Next, we consider another traditional approach based on confidence scores, in which the existing work focuses on a specific class of losses. We propose rejection criteria for more general losses for this approach and guarantee calibration to the Bayes-optimal solution. Finally, we conduct experiments to validate the relevance of our theoretical findings.
Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation
Authors: Jongyeong Lee, Nontawat Charoenphakdee, Seiichi Kuroki, Masashi Sugiyama
Date: 2019-01-30 00:00:00
Tasks: Binary Classification, Classification, Domain Adaptation, General Classification, Multi-class Classification, Unsupervised Domain Adaptation

Appropriately evaluating the discrepancy between domains is essential for the success of unsupervised domain adaptation. In this paper, we first point out that existing discrepancy measures are less informative when complex models such as deep neural networks are used, in addition to the facts that they can be computationally highly demanding and their range of applications is limited only to binary classification. We then propose a novel domain discrepancy measure, called the paired hypotheses discrepancy (PHD), to overcome these shortcomings. PHD is computationally efficient and applicable to multi-class classification. Through generalization error bound analysis, we theoretically show that PHD is effective even for complex models. Finally, we demonstrate the practical usefulness of PHD through experiments.
Evaluating Bregman Divergences for Probability Learning from Crowd
Authors: F. A. Mena, R. Ñanculef
Date: 2019-01-30 00:00:00

The crowdsourcing scenarios are a good example of having a probability
distribution over some categories showing what the people in a global
perspective thinks. Learn a predictive model of this probability distribution
can be of much more valuable that learn only a discriminative model that gives
the most likely category of the data. Here we present differents models that
adapts having probability distribution as target to train a machine learning
model. We focus on the Bregman divergences framework to used as objective
function to minimize. The results show that special care must be taken when
build a objective function and consider a equal optimization on neural network
in Keras framework.
Geometric structure of graph Laplacian embeddings
Authors: Nicolas Garcia Trillos, Franca Hoffmann, Bamdad Hosseini
Date: 2019-01-30 00:00:00
Tasks: Clustering

We analyze the spectral clustering procedure for identifying coarse structure
in a data set $x_1, \dots, x_n$, and in particular study the geometry of graph
Laplacian embeddings which form the basis for spectral clustering algorithms.
More precisely, we assume that the data is sampled from a mixture model
supported on a manifold $\mathcal{M}$ embedded in $\mathbb{R}^d$, and pick a
connectivity length-scale $\varepsilon>0$ to construct a kernelized graph
Laplacian. We introduce a notion of a well-separated mixture model which only
depends on the model itself, and prove that when the model is well separated,
with high probability the embedded data set concentrates on cones that are
centered around orthogonal vectors. Our results are meaningful in the regime
where $\varepsilon = \varepsilon(n)$ is allowed to decay to zero at a slow
enough rate as the number of data points grows. This rate depends on the
intrinsic dimension of the manifold on which the data is supported.
Adversarial Metric Attack and Defense for Person Re-identification
Authors: Song Bai, Yingwei Li, Yuyin Zhou, Qizhu Li, Philip H. S. Torr
Date: 2019-01-30 00:00:00
Tasks: Adversarial Attack, Benchmarking, General Classification, Person Re-Identification

Person re-identification (re-ID) has attracted much attention recently due to its great importance in video surveillance. In general, distance metrics used to identify two person images are expected to be robust under various appearance changes. However, our work observes the extreme vulnerability of existing distance metrics to adversarial examples, generated by simply adding human-imperceptible perturbations to person images. Hence, the security danger is dramatically increased when deploying commercial re-ID systems in video surveillance. Although adversarial examples have been extensively applied for classification analysis, it is rarely studied in metric analysis like person re-identification. The most likely reason is the natural gap between the training and testing of re-ID networks, that is, the predictions of a re-ID network cannot be directly used during testing without an effective metric. In this work, we bridge the gap by proposing Adversarial Metric Attack, a parallel methodology to adversarial classification attacks. Comprehensive experiments clearly reveal the adversarial effects in re-ID systems. Meanwhile, we also present an early attempt of training a metric-preserving network, thereby defending the metric against adversarial attacks. At last, by benchmarking various adversarial settings, we expect that our work can facilitate the development of adversarial attack and defense in metric-based applications.
Support Recovery in the Phase Retrieval Model: Information-Theoretic Fundamental Limits
Authors: Lan V. Truong, Jonathan Scarlett
Date: 2019-01-30 00:00:00
Tasks: Retrieval

The support recovery problem consists of determining a sparse subset of variables that is relevant in generating a set of observations. In this paper, we study the support recovery problem in the phase retrieval model consisting of noisy phaseless measurements, which arises in a diverse range of settings such as optical detection, X-ray crystallography, electron microscopy, and coherent diffractive imaging. Our focus is on information-theoretic fundamental limits under an approximate recovery criterion, considering both discrete and Gaussian models for the sparse non-zero entries, along with Gaussian measurement matrices. In both cases, our bounds provide sharp thresholds with near-matching constant factors in several scaling regimes on the sparsity and signal-to-noise ratio. As a key step towards obtaining these results, we develop new concentration bounds for the conditional information content of log-concave random variables, which may be of independent interest.
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
Authors: Ziling Wu, Abdulaziz Alorf, Ting Yang, Ling Li, Yunhui Zhu
Date: 2019-01-30 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction, Image Reconstruction

Convolutional Neural Networks (CNN) based image reconstruction methods have
been intensely used for X-ray computed tomography (CT) reconstruction
applications. Despite great success, good performance of this data-based
approach critically relies on a representative big training data set and a
dense convoluted deep network. The indiscriminating convolution connections
over all dense layers could be prone to over-fitting, where sampling biases are
wrongly integrated as features for the reconstruction. In this paper, we report
a robust hierarchical synthesis reconstruction approach, where training data is
pre-processed to separate the information on the domains where sampling biases
are suspected. These split bands are then trained separately and combined
successively through a hierarchical synthesis network. We apply the
hierarchical synthesis reconstruction for two important and classical
tomography reconstruction scenarios: the spares-view reconstruction and the
phase reconstruction. Our simulated and experimental results show that
comparable or improved performances are achieved with a dramatic reduction of
network complexity and computational cost. This method can be generalized to a
wide range of applications including material characterization, in-vivo
monitoring and dynamic 4D imaging.
Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces
Authors: Baoxiang Wang, Nidhi Hegde
Date: 2019-01-30 00:00:00
Tasks: Privacy Preserving, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider differentially private algorithms for reinforcement learning in continuous spaces, such that neighboring reward functions are indistinguishable. This protects the reward information from being exploited by methods such as inverse reinforcement learning. Existing studies that guarantee differential privacy are not extendable to infinite state spaces, as the noise level to ensure privacy will scale accordingly to infinity. Our aim is to protect the value function approximator, without regard to the number of states queried to the function. It is achieved by adding functional noise to the value function iteratively in the training. We show rigorous privacy guarantees by a series of analyses on the kernel of the noise space, the probabilistic bound of such noise samples, and the composition over the iterations. We gain insight into the utility analysis by proving the algorithm's approximate optimality when the state space is discrete. Experiments corroborate our theoretical findings and show improvement over existing approaches.
Predicting quantum advantage by quantum walk with convolutional neural networks
Authors: Alexey A. Melnikov, Leonid E. Fedichkin, Alexander Alodjants
Date: 2019-01-30 00:00:00

Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enabling in these cases quantum algorithmic applications and quantum-enhanced energy transfer. However, little is known about the possible advantages on arbitrary graphs not having explicit symmetries. For these graphs one would need to perform simulations of classical and quantum walk dynamics to check if the speedup occurs, which could take a long computational time. Here we present a new approach for the solution of the quantum speedup problem, which is based on a machine learning algorithm that predicts the quantum advantage by just looking at a graph. The convolutional neural network, which we designed specifically to learn from graphs, observes simulated examples and learns complex features of graphs that lead to a quantum advantage, allowing to identify graphs that exhibit quantum advantage without performing any quantum walk or random walk simulations. The performance of our approach is evaluated for line and random graphs, where classification was always better than random guess even for the most challenging cases. Our findings pave the way to an automated elaboration of novel large-scale quantum circuits utilizing quantum walk based algorithms, and to simulating high-efficiency energy transfer in biophotonics and material science.
End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis
Authors: Lin Xu, Qixian Zhou, Ke Gong, Xiaodan Liang, Jianheng Tang, Liang Lin
Date: 2019-01-30 00:00:00
Tasks: Decision Making, Dialogue Management, Management, Medical Diagnosis, Natural Language Understanding, Task-Oriented Dialogue Systems, Text Generation

Beyond current conversational chatbots or task-oriented dialogue systems that
have attracted increasing attention, we move forward to develop a dialogue
system for automatic medical diagnosis that converses with patients to collect
additional symptoms beyond their self-reports and automatically makes a
diagnosis. Besides the challenges for conversational dialogue systems (e.g.
topic transition coherency and question understanding), automatic medical
diagnosis further poses more critical requirements for the dialogue rationality
in the context of medical knowledge and symptom-disease relations. Existing
dialogue systems (Madotto, Wu, and Fung 2018; Wei et al. 2018; Li et al. 2017)
mostly rely on data-driven learning and cannot be able to encode extra expert
knowledge graph. In this work, we propose an End-to-End Knowledge-routed
Relational Dialogue System (KR-DS) that seamlessly incorporates rich medical
knowledge graph into the topic transition in dialogue management, and makes it
cooperative with natural language understanding and natural language
generation. A novel Knowledge-routed Deep Q-network (KR-DQN) is introduced to
manage topic transitions, which integrates a relational refinement branch for
encoding relations among different symptoms and symptom-disease pairs, and a
knowledge-routed graph branch for topic decision-making. Extensive experiments
on a public medical dialogue dataset show our KR-DS significantly beats
state-of-the-art methods (by more than 8% in diagnosis accuracy). We further
show the superiority of our KR-DS on a newly collected medical dialogue system
dataset, which is more challenging retaining original self-reports and
conversational data between patients and doctors.
Reliable Smart Road Signs
Authors: Muhammed O. Sayin, Chung-Wei Lin, Eunsuk Kang, Shinichi Shiraishi, Tamer Basar
Date: 2019-01-30 00:00:00
Tasks: Classification, General Classification

In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is ``smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios.
Enhanced Variational Inference with Dyadic Transformation
Authors: Sarin Chandy, Amin Rasekh
Date: 2019-01-30 00:00:00
Tasks: Variational Inference

Variational autoencoder is a powerful deep generative model with variational
inference. The practice of modeling latent variables in the VAE's original
formulation as normal distributions with a diagonal covariance matrix limits
the flexibility to match the true posterior distribution. We propose a new
transformation, dyadic transformation (DT), that can model a multivariate
normal distribution. DT is a single-stage transformation with low computational
requirements. We demonstrate empirically on MNIST dataset that DT enhances the
posterior flexibility and attains competitive results compared to other VAE
enhancements.
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop
Authors: Tong Liu, Christopher M. Homan
Date: 2019-01-30 00:00:00

We present the Twitter Job/Employment Corpus, a collection of tweets
annotated by a humans-in-the-loop supervised learning framework that integrates
crowdsourcing contributions and expertise on the local community and employment
environment. Previous computational studies of job-related phenomena have used
corpora collected from workplace social media that are hosted internally by the
employers, and so lacks independence from latent job-related coercion and the
broader context that an open domain, general-purpose medium such as Twitter
provides. Our new corpus promises to be a benchmark for the extraction of
job-related topics and advanced analysis and modeling, and can potentially
benefit a wide range of research communities in the future.
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Authors: Myung Seok Shim, Chenye Zhao, Yang Li, Xuchong Zhang, Wenrui Zhang, Peng Li
Date: 2019-01-29 00:00:00
Tasks: Deep Learning, Sensor Fusion

Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architectures are not always resilient, leading to the issue of fusion weight inconsistency. We propose deep multi-modal sensor fusion architectures with enhanced robustness particularly under the presence of sensor failures. At the core of our gating architectures are fusion weight regularization and fusion target learning operating on auxiliary unimodal sensing networks appended to the main fusion model. The proposed regularized gating architectures outperform the existing deep learning architectures with and without gating under both clean and corrupted sensory inputs resulted from sensor failures. The demonstrated improvements are particularly pronounced when one or more multiple sensory modalities are corrupted.
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Authors: Di Feng, Xiao Wei, Lars Rosenbaum, Atsuto Maki, Klaus Dietmayer
Date: 2019-01-29 00:00:00
Tasks: Active Learning, Autonomous Driving, Object

Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy, annotating data is very tedious and time-consuming, especially when dealing with 3D LiDAR points or radar data. Active learning has the potential to minimize human annotation efforts while maximizing the object detector's performance. In this work, we propose an active learning method to train a LiDAR 3D object detector with the least amount of labeled training data necessary. The detector leverages 2D region proposals generated from the RGB images to reduce the search space of objects and speed up the learning process. Experiments show that our proposed method works under different uncertainty estimations and query functions, and can save up to 60% of the labeling efforts while reaching the same network performance.
Improved Path-length Regret Bounds for Bandits
Authors: Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei
Date: 2019-01-29 00:00:00

We study adaptive regret bounds in terms of the variation of the losses (the so-called path-length bounds) for both multi-armed bandit and more generally linear bandit. We first show that the seemingly suboptimal path-length bound of (Wei and Luo, 2018) is in fact not improvable for adaptive adversary. Despite this negative result, we then develop two new algorithms, one that strictly improves over (Wei and Luo, 2018) with a smaller path-length measure, and the other which improves over (Wei and Luo, 2018) for oblivious adversary when the path-length is large. Our algorithms are based on the well-studied optimistic mirror descent framework, but importantly with several novel techniques, including new optimistic predictions, a slight bias towards recently selected arms, and the use of a hybrid regularizer similar to that of (Bubeck et al., 2018). Furthermore, we extend our results to linear bandit by showing a reduction to obtaining dynamic regret for a full-information problem, followed by a further reduction to convex body chasing. We propose a simple greedy chasing algorithm for squared 2-norm, leading to new dynamic regret results and as a consequence the first path-length regret for general linear bandit as well.