pwc-archive/papers-with-abstracts huggingface dataset:
Neural Maximum Common Subgraph Detection with Guided Subgraph Extraction
Authors: Yunsheng Bai, Derek Xu, Ken Gu, Xueqing Wu, Agustin Marinovic, Christopher Ro, Yizhou Sun, Wei Wang
Date: 2019-09-25 00:00:00
Tasks: Heuristic Search

Maximum Common Subgraph (MCS) is defined as the largest subgraph that is commonly present in both graphs of a graph pair. Exact MCS detection is NP-hard, and its state-of-the-art exact solver based on heuristic search is slow in practice without any time complexity guarantee. Given the huge importance of this task yet the lack of fast solver, we propose an efficient MCS detection algorithm, NeuralMCS, consisting of a novel neural network model that learns the node-node correspondence from the ground-truth MCS result, and a subgraph extraction procedure that uses the neural network output as guidance for final MCS prediction. The whole model guarantees polynomial time complexity with respect to the number of the nodes of the larger of the two input graphs. Experiments on four real graph datasets show that the proposed model is 48.1x faster than the exact solver and more accurate than all the existing competitive approximate approaches to MCS detection.
GroSS Decomposition: Group-Size Series Decomposition for Whole Search-Space Training
Authors: Henry Howard-Jenkins, Yiwen Li, Victor Adrian Prisacariu
Date: 2019-09-25 00:00:00

We present Group-size Series (GroSS) decomposition, a mathematical formulation of tensor factorisation into a series of approximations of increasing rank terms. GroSS allows for dynamic and differentiable selection of factorisation rank, which is analogous to a grouped convolution. Therefore, to the best of our knowledge, GroSS is the first method to simultaneously train differing numbers of groups within a single layer, as well as all possible combinations between layers. In doing so, GroSS trains an entire grouped convolution architecture search-space concurrently. We demonstrate this with a proof-of-concept exhaustive architecure search with a performance objective. GroSS represents a significant step towards liberating network architecture search from the burden of training and finetuning.
Emergence of Collective Policies Inside Simulations with Biased Representations
Authors: Jooyeon Kim, Alice Oh
Date: 2019-09-25 00:00:00

We consider a setting where biases are involved when agents internalise an environment.  Agents have different biases, all of which resulting in imperfect evidence collected for taking optimal actions.  Throughout the interactions, each agent asynchronously internalises their own predictive model of the environment and forms a virtual simulation within which the agent plays trials of the episodes in entirety. In this research, we focus on developing a collective policy trained solely inside agents' simulations, which can then be transferred to the real-world environment. The key idea is to let agents imagine together; make them take turns to host virtual episodes within which all agents participate and interact with their own biased representations. Since agents' biases vary, the collective policy developed while sequentially visiting the internal simulations complement one another's shortcomings. In our experiment, the collective policies consistently achieve significantly higher returns than the best individually trained policies.
On unsupervised-supervised risk and one-class neural networks
Authors: Christophe Cerisara
Date: 2019-09-25 00:00:00
Tasks: Anomaly Detection, Clustering, Deep Clustering, Representation Learning

Most unsupervised neural networks training methods concern generative models, deep clustering, pretraining or some form of representation learning. We rather deal in this work with unsupervised training of the final classification stage of a standard deep learning stack, with a focus on two types of methods: unsupervised-supervised risk approximations and one-class models. We derive a new analytical solution for the former and identify and analyze its similarity with the latter.
We apply and validate the proposed approach on multiple experimental conditions, in particular on four challenging recent Natural Language Processing tasks as well as on an anomaly detection task, where it improves over state-of-the-art models.
Adaptive Adversarial Imitation Learning
Authors: Yiren Lu, Jonathan Tompson, Sergey Levine
Date: 2019-09-25 00:00:00
Tasks: Imitation Learning

We present the ADaptive Adversarial Imitation Learning (ADAIL) algorithm for learning adaptive policies that can be transferred between environments of varying dynamics, by imitating a small number of demonstrations collected from a single source domain. This problem is important in robotic learning because in real world scenarios 1) reward functions are hard to obtain, 2) learned policies from one domain are difficult to deploy in another due to varying source to target domain statistics, 3) collecting expert demonstrations in multiple environments where the dynamics are known and controlled is often infeasible. We address these constraints by building upon recent advances in adversarial imitation learning; we condition our policy on a learned dynamics embedding and we employ a domain-adversarial loss to learn a dynamics-invariant discriminator. The effectiveness of our method is demonstrated on simulated control tasks with varying environment dynamics and the learned adaptive agent outperforms several recent baselines.
Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness
Authors: Haichao Zhang, Wei Xu
Date: 2019-09-25 00:00:00

We propose a simple approach for adversarial training. The proposed approach utilizes an adversarial interpolation scheme for generating adversarial images and accompanying adversarial labels, which are then used in place of the original data for model training. The proposed approach is intuitive to understand, simple to implement and achieves state-of-the-art performance. We evaluate the proposed approach on a number of datasets including CIFAR10, CIFAR100 and SVHN. Extensive empirical results compared with several state-of-the-art methods against different attacks verify the effectiveness of the proposed approach.
Manifold Learning and Alignment with Generative Adversarial Networks
Authors: Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang
Date: 2019-09-25 00:00:00
Tasks: Disentanglement, Generative Adversarial Network

We present a generative adversarial network (GAN) that conducts manifold learning and alignment (MLA): A task to learn the multi-manifold structure underlying data and to align those manifolds without any correspondence information. Our main idea is to exploit the powerful abstraction ability of encoder architecture. Specifically, we define multiple generators to model multiple manifolds, but in a particular way that their inverse maps can be commonly represented by a single smooth encoder. Then, the abstraction ability of the encoder enforces semantic similarities between the generators and gives a plausibly aligned embedding in the latent space. In experiments with MNIST, 3D-Chair, and UT-Zap50k datasets, we demonstrate the superiority of our model in learning the manifolds by FID scores and in aligning the manifolds by disentanglement scores. Furthermore, by virtue of the abstractive modeling, we show that our model can generate data from an untrained manifold, which is unique to our model.
Count-guided Weakly Supervised Localization Based on Density Map
Authors: Ming Ma, Stephan Chalup, Fayeem Aziz, Yang Liu, Defu Cheng, Zhijian Zhou
Date: 2019-09-25 00:00:00
Tasks: Crowd Counting

Weakly supervised localization (WSL) aims at training a model to find the positions of objects by providing it with only abstract labels. For most of the existing WSL methods, the labels are the class of the main object in an image. In this paper, we generalize WSL to counting machines that apply convolutional neural networks (CNN) and density maps for counting. We show that given only ground-truth count numbers, the density map as a hidden layer can be trained for localizing objects and detecting features. Convolution and pooling are the two major building blocks of CNNs. This paper discusses their impacts on an end-to-end WSL network. The learned features in a density map present in the form of dots. In order to make these features interpretable for human beings, this paper proposes a Gini impurity penalty to regularize the density map. Furthermore, it will be shown that this regularization is similar to the variational term of the $\beta$-variational autoencoder. The details of this algorithm are demonstrated through a simple bubble counting task. Finally, the proposed methods are applied to the widely used crowd counting dataset the Mall to learn discriminative features of human figures.
LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks
Authors: Mingxi Cheng, Yizhi Li, Shahin Nazarian, Paul Bogdan
Date: 2019-09-25 00:00:00
Tasks: Decision Making, Fake News Detection, feature selection, Sentence

Social media have emerged to be increasingly popular and have been used as tools for gathering and propagating information. However, the vigorous growth of social media contributes to the fast-spreading and far-reaching rumors. Rumor detection has become a necessary defense. Traditional rumor detection methods based on hand-crafted feature selection are replaced by automatic approaches that are based on Artificial Intelligence (AI). AI decision making systems need to have the necessary means, such as explainability to assure users their trustworthiness. Inspired by the thriving development of Generative Adversarial Networks (GANs) on text applications, we propose LEX-GAN, a GAN-based layered explainable rumor detector to improve the detection quality and provide explainability. Unlike fake news detection that needs a previously collected verified news database, LEX-GAN realizes explainable rumor detection based on only tweet-level text. LEX-GAN is trained with generated non-rumor-looking rumors. The generators produce rumors by intelligently inserting controversial information in non-rumors, and force the discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. The layered structures in both generative and discriminative model contributes to the high performance. We show LEX-GAN's mutation detection ability in textural sequences by performing a gene classification and mutation detection task.
A New Multi-input Model with the Attention Mechanism for Text Classification
Authors: Junhao Qiu, Ronghua Shi, Fangfang Li (the corresponding author), Jinjing Shi, Wangmin Liao
Date: 2019-09-25 00:00:00
Tasks: Classification, text-classification, Text Classification

Recently, deep learning has made extraordinary achievements in text classification. However, most of present models, especially convolutional neural network (CNN), do not extract long-range associations, global representations, and hierarchical features well due to their relatively shallow and simple structures. This causes a negative effect on text classification. Moreover, we find that there are many express methods of texts. It is appropriate to design the multi-input model to improve the classification effect. But most of models of text classification only use words or characters and do not use the multi-input model. Inspired by the above points and Densenet (Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017.), we propose a new text classification model, which uses words, characters, and labels as input. The model, which is a deep CNN with a novel attention mechanism, can effectively leverage the input information and solve the above issues of the shallow model. We conduct experiments on six large text classification datasets. Our model achieves the state of the art results on all datasets compared to multiple baseline models.
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs
Authors: Kaushalya Madhawa, katsuhiko Ishiguro, Kosuke Nakago, Motoki Abe
Date: 2019-09-25 00:00:00
Tasks: Graph Generation, Molecular Graph Generation, valid

We propose GraphNVP, an invertible flow-based molecular graph generation model. Existing flow-based models only handle node attributes of a graph with invertible maps. In contrast, our model is the first invertible model for the whole graph components: both of dequantized node attributes and adjacency tensor are converted into latent vectors through two novel invertible flows. This decomposition yields the exact likelihood maximization on graph-structured data. We decompose the generation of a graph into two steps: generation of (i) an adjacency tensor and(ii) node attributes. We empirically demonstrate that our model and the two-step generation efficiently generates valid molecular graphs with almost no duplicated molecules, although there are no domain-specific heuristics ingrained in the model. We also confirm that the sampling (generation) of graphs is faster in magnitude than other models in our implementation. In addition, we observe that the learned latent space can be used to generate molecules with desired chemical properties
RGTI:Response generation via templates integration for End to End dialog
Authors: Yuxin Zhang, Songyan Liu
Date: 2019-09-25 00:00:00
Tasks: Decoder, Response Generation, Retrieval

End-to-end models have achieved considerable success in task-oriented dialogue area, but suffer from the challenges of (a) poor semantic control, and (b) little interaction with auxiliary information. In this paper, we propose a novel yet simple end-to-end model for response generation via mixed templates, which can address above challenges. 
In our model, we retrieval candidate responses which contain abundant syntactic and sequence information by dialogue semantic information related to dialogue history. Then, we exploit candidate response attention to get templates which should be mentioned in response. Our model can integrate multi template information to guide the decoder module how to generate response better. We show that our proposed model learns useful templates information, which improves the performance of "how to say" and "what to say" in response generation. Experiments on the large-scale Multiwoz dataset demonstrate the effectiveness of our proposed model, which attain the state-of-the-art performance.
Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching
Authors: Peng Zhang, Xiaoliu Mao, Xindian Ma, Benyou Wang, Jing Zhang, Jun Wang, Dawei Song
Date: 2019-09-25 00:00:00
Tasks: Inductive Bias, Question Answering, Sentence, Text Matching

The formal understanding of deep learning has made great progress based on quantum many-body physics.  For example, the entanglement entropy in quantum many-body systems can interpret the inductive bias of neural network and then guide the design of network structure and parameters for certain tasks. However, there are two unsolved problems in the current study of entanglement entropy, which limits its application potential. First, the theoretical benefits of entanglement entropy was only investigated in the representation of a single object (e.g., an image or a sentence), but has not been well studied in the matching of two objects (e.g., question-answering pairs). Second,  the entanglement entropy can not be qualitatively calculated since  the exponentially increasing dimension of the matching matrix. In this paper, we are trying to address these two problem by investigating the fundamental connections between the entanglement entropy and the attention matrix. We prove that by a mapping (via the trace operator) on the high-dimensional matching matrix,  a low-dimensional attention matrix can be derived. Based on such a attention matrix, we can provide a feasible solution to the entanglement entropy that describes the correlation between the two objects in matching tasks. Inspired by the theoretical property of the entanglement entropy, we can design the network architecture adaptively in a typical text matching task, i.e., question-answering task.
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model
Authors: Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh.
Date: 2019-09-25 00:00:00
Tasks: Distributed Optimization, Scheduling

The population model is a standard way to represent large-scale decentralized
distributed systems, in which agents with limited computational power interact
in randomly chosen pairs, in order to collectively solve global computational
tasks. In contrast with synchronous gossip models, nodes are anonymous, lack a
common notion of time, and have no control over their scheduling. In this paper,
we examine whether large-scale distributed optimization can be performed in this
extremely restrictive setting. 

We introduce and analyze a natural decentralized variant of stochastic gradient
descent (SGD), called PopSGD, in which every node maintains a local parameter,
and is able to compute stochastic gradients with respect to this parameter. 
Every pair-wise node interaction performs a stochastic gradient step at each
agent, followed by averaging of the two models. We prove that, under standard
assumptions, SGD can converge even in this extremely loose, decentralized
setting, for both convex and non-convex objectives.  Moreover, surprisingly, in
the former case, the algorithm can achieve linear speedup in the number of nodes
n. Our analysis leverages a new technical connection between decentralized SGD
and randomized load balancing, which enables us to tightly bound the
concentration of node parameters. We validate our analysis through experiments,
showing that PopSGD can achieve convergence and speedup for large-scale
distributed learning tasks in a supercomputing environment.
Wide Neural Networks are Interpolating Kernel Methods: Impact of Initialization on Generalization
Authors: Manuel Nonnenmacher, David Reeb, Ingo Steinwart
Date: 2019-09-25 00:00:00
Tasks: Generalization Bounds

The recently developed link between strongly overparametrized neural networks (NNs) and kernel methods has opened a new way to understand puzzling features of NNs, such as their convergence and generalization behaviors. In this paper, we make the bias of initialization on strongly overparametrized NNs under gradient descent explicit. We prove that fully-connected wide ReLU-NNs trained with squared loss are essentially a sum of two parts: The first is the minimum complexity solution of an interpolating kernel method, while the second contributes to the test error only and depends heavily on the initialization. This decomposition has two consequences: (a) the second part becomes negligible in the regime of small initialization variance, which allows us to transfer generalization bounds from minimum complexity interpolating kernel methods to NNs; (b) in the opposite regime, the test error of wide NNs increases significantly with the initialization variance, while still interpolating the training data perfectly. Our work shows that -- contrary to common belief -- the initialization scheme has a strong effect on generalization performance, providing a novel criterion to identify good initialization strategies.
Improved Mutual Information Estimation
Authors: Youssef Mroueh*, Igor Melnyk*, Pierre Dognin*, Jerret Ross*, Tom Sercu*
Date: 2019-09-25 00:00:00
Tasks: Mutual Information Estimation, Representation Learning

We propose a new variational lower bound on the KL divergence and show that the Mutual Information (MI) can be estimated by maximizing this bound using a witness function on a hypothesis function class and an auxiliary scalar variable. If the function class is in a Reproducing Kernel Hilbert Space (RKHS), this leads to a jointly convex problem. We analyze the bound by deriving its dual formulation and show its connection to a likelihood ratio estimation problem. We show that the auxiliary variable introduced in our variational form plays the role of a Lagrange multiplier that enforces a normalization constraint on the likelihood ratio. By extending the function space to neural networks, we propose an efficient neural MI estimator, and validate its performance on synthetic examples, showing advantage over the existing baselines. We then demonstrate the strength of our estimator in large-scale self-supervised representation learning through MI maximization.
Learning Key Steps to Attack Deep Reinforcement Learning Agents
Authors: Chien-Min Yu, Hsuan-Tien Lin
Date: 2019-09-25 00:00:00
Tasks: Adversarial Attack, Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning agents are known to be vulnerable to adversarial attacks. In particular, recent studies have shown that attacking a few key steps is effective for decreasing the agent's cumulative reward. However, all existing attacking methods find those key steps with human-designed heuristics, and it is not clear how more effective key steps can be identified. This paper introduces a novel reinforcement learning framework that learns more effective key steps through interacting with the agent. The proposed framework does not require any human heuristics nor knowledge, and can be flexibly coupled with any white-box or black-box adversarial attack scenarios. Experiments on benchmark Atari games across different scenarios demonstrate that the proposed framework is superior to existing methods for identifying more effective key steps.
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling
Authors: Robin Vogel, Mastane Achab, Charles Tillier, Stéphan Clémençon
Date: 2019-09-25 00:00:00
Tasks: Transfer Learning

We consider statistical learning problems, when the distribution $P'$ of the training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution $P$ involved in the risk one seeks to minimize (referred to as the \textit{test distribution}) but is still defined on the same measurable space as $P$ and dominates it. In the unrealistic case where the likelihood ratio $\Phi(z)=dP/dP'(z)$ is known, one may straightforwardly extends the Empirical Risk Minimization (ERM) approach to this specific \textit{transfer learning} setup using the same idea as that behind Importance Sampling, by minimizing a weighted version of the empirical risk functional computed from the 'biased' training data $Z'_i$ with weights $\Phi(Z'_i)$. Although the \textit{importance function} $\Phi(z)$ is generally unknown in practice, we show that, in various situations frequently encountered in practice, it takes a simple form and can be directly estimated from the $Z'_i$'s and some auxiliary information on the statistical population $P$. By means of linearization techniques, we then prove that the generalization capacity of the approach aforementioned is preserved when plugging the resulting estimates of the $\Phi(Z'_i)$'s into the  weighted empirical risk. Beyond these theoretical guarantees, numerical results provide strong empirical evidence of the relevance of the approach promoted in this article.