pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Cell Structure via Recursive-Recurrent Neural Networks
Authors: Xin Qian, Matthew Kennedy, Diego Klabjan
Date: 2019-05-25 00:00:00
Tasks: Language Modelling, Neural Architecture Search, Sentiment Analysis

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neural networks, our algorithm is able to construct customized cell structures for each data sample and time step, allowing for a more efficient architecture search than existing models. Experiments on three common datasets show that the algorithm discovers high-performance cell architectures and achieves better prediction accuracy compared to the GRU structure for language modelling and sentiment analysis.
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Authors: Shuai Zhang, Yi Tay, Lina Yao, Bin Wu, Aixin Sun
Date: 2019-05-25 00:00:00
Tasks: Deep Learning, Recommendation Systems, Sequential Recommendation

Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for further comparisons. Although a portion of papers provides source code, they adopted different programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: \textbf{DeepRec}. In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow. Three major recommendation scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github: \url{https://github.com/cheungdaven/DeepRec}
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
Authors: Constantin Pape, Alex Matskevych, Adrian Wolny, Julian Hennies, Giula Mizzon, Marion Louveaux, Jacob Musser, Alexis Maizel, Detlev Arendt, Anna Kreshuk
Date: 2019-05-25 00:00:00
Tasks: graph partitioning, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

The throughput of electron microscopes has increased significantly in recent years, enabling detailed analysis of cell morphology and ultrastructure. Analysis of neural circuits at single-synapse resolution remains the flagship target of this technique, but applications to cell and developmental biology are also starting to emerge at scale. The amount of data acquired in such studies makes manual instance segmentation, a fundamental step in many analysis pipelines, impossible. While automatic segmentation approaches have improved significantly thanks to the adoption of convolutional neural networks, their accuracy still lags behind human annotations and requires additional manual proof-reading. A major hindrance to further improvements is the limited field of view of the segmentation networks preventing them from exploiting the expected cell morphology or other prior biological knowledge which humans use to inform their segmentation decisions. In this contribution, we show how such domain-specific information can be leveraged by expressing it as long-range interactions in a graph partitioning problem known as the lifted multicut problem. Using this formulation, we demonstrate significant improvement in segmentation accuracy for three challenging EM segmentation problems from neuroscience and cell biology.
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification
Authors: Yangru Huang, Peixi Peng, Yi Jin, Yidong Li, Junliang Xing, Shiming Ge
Date: 2019-05-25 00:00:00
Tasks: Diversity, Domain Adaptation, General Classification, One-Class Classification, Person Re-Identification, Unsupervised Person Re-Identification

Person re-identification (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-specific part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts.
Soft Contextual Data Augmentation for Neural Machine Translation
Authors: Jinhua Zhu, Fei Gao, Lijun Wu, Yingce Xia, Tao Qin, Wengang Zhou, Xue-Qi Cheng, Tie-Yan Liu
Date: 2019-05-25 00:00:00
Proceeding: ACL 2019 7
Tasks: Data Augmentation, Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

While data augmentation is an important trick to boost the accuracy of deep learning methods in computer vision tasks, its study in natural language tasks is still very limited. In this paper, we present a novel data augmentation method for neural machine translation. Different from previous augmentation methods that randomly drop, swap or replace words with other words in a sentence, we softly augment a randomly chosen word in a sentence by its contextual mixture of multiple related words. More accurately, we replace the one-hot representation of a word by a distribution (provided by a language model) over the vocabulary, i.e., replacing the embedding of this word by a weighted combination of multiple semantically similar words. Since the weights of those words depend on the contextual information of the word to be replaced, the newly generated sentences capture much richer information than previous augmentation methods. Experimental results on both small scale and large scale machine translation datasets demonstrate the superiority of our method over strong baselines.
Bivariate Beta-LSTM
Authors: Kyungwoo Song, JoonHo Jang, Seung jae Shin, Il-Chul Moon
Date: 2019-05-25 00:00:00
Tasks: Caption Generation, Density Estimation, General Classification, image-classification, Image Classification, Inductive Bias, Music Modeling, Sentence, Sentence Classification

Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing multi-modality or skewness. Besides, the previous models lack modeling on the correlation between the gates, which would be a new method to adopt inductive bias for a relationship between previous and current input. This paper proposes a new gate structure with the bivariate Beta distribution. The proposed gate structure enables probabilistic modeling on the gates within the LSTM cell so that the modelers can customize the cell state flow with priors and distributions. Moreover, we theoretically show the higher upper bound of the gradient compared to the sigmoid function, and we empirically observed that the bivariate Beta distribution gate structure provides higher gradient values in training. We demonstrate the effectiveness of bivariate Beta gate structure on the sentence classification, image classification, polyphonic music modeling, and image caption generation.
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Authors: Arsalan Mousavian, Clemens Eppner, Dieter Fox
Date: 2019-05-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: Grasp Generation, Object

Generating grasp poses is a crucial component for any robot object manipulation task. In this work, we formulate the problem of grasp generation as sampling a set of grasps using a variational autoencoder and assess and refine the sampled grasps using a grasp evaluator model. Both Grasp Sampler and Grasp Refinement networks take 3D point clouds observed by a depth camera as input. We evaluate our approach in simulation and real-world robot experiments. Our approach achieves 88\% success rate on various commonly used objects with diverse appearances, scales, and weights. Our model is trained purely in simulation and works in the real world without any extra steps. The video of our experiments can be found at: https://research.nvidia.com/publication/2019-10_6-DOF-GraspNet\%3A-Variational
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning
Authors: Yoni Birman, Shaked Hindi, Gilad Katz, Asaf Shabtai
Date: 2019-05-25 00:00:00
Tasks: Deep Reinforcement Learning, Malware Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Malware detection is an ever-present challenge for all organizational gatekeepers, who must maintain high detection rates while minimizing interruptions to the organization's workflow. To improve detection rates, organizations often deploy an ensemble of detectors. While effective, this approach is computationally expensive, since every file - even clear-cut cases - needs to be analyzed by all detectors. Moreover, with an ever-increasing number of files to process, the use of ensembles may incur unacceptable processing times and costs (e.g., cloud resources). In this study, we propose SPIREL, a reinforcement learning-based method for cost-effective malware detection. Our method enables organizations to directly associate costs to correct/incorrect classification, computing resources and run-time, and then dynamically establishes a security policy. This security policy is then implemented, and for each inspected file, a different set of detectors is assigned and a different detection threshold is set. Our evaluation on two malware domains- Portable Executable (PE) and Android Application Package (APK)files - shows that SPIREL is both accurate and extremely resource-efficient: the proposed method either outperforms the best performing baselines while achieving a modest improvement in efficiency, or reduces the required running time by ~80% while decreasing the accuracy and F1-score by only 0.5%. We also show that our approach is both highly transferable across different datasets and adaptable to changes in individual detector performance.
SuperCaptioning: Image Captioning Using Two-dimensional Word Embedding
Authors: Baohua Sun, Lin Yang, Michael Lin, Charles Young, Patrick Dong, Wenhan Zhang, Jason Dong
Date: 2019-05-25 00:00:00
Tasks: Classification, General Classification, Image Captioning, image-classification, Image Classification, text-classification, Text Classification, Vocal Bursts Valence Prediction

Language and vision are processed as two different modal in current work for image captioning. However, recent work on Super Characters method shows the effectiveness of two-dimensional word embedding, which converts text classification problem into image classification problem. In this paper, we propose the SuperCaptioning method, which borrows the idea of two-dimensional word embedding from Super Characters method, and processes the information of language and vision together in one single CNN model. The experimental results on Flickr30k data shows the proposed method gives high quality image captions. An interactive demo is ready to show at the workshop.
Semi-supervised Learning with Contrastive Predicative Coding
Authors: Jiaxing Wang, Yin Zheng, Xiaoshuang Chen, Junzhou Huang, Jian Cheng
Date: 2019-05-25 00:00:00

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, many of them have thus far been either inflexible, inefficient or non-scalable. This paper explores recently developed contrastive predictive coding technique to improve discriminative power of deep learning models when a large portion of labels are absent. Two models, cpc-SSL and a class conditional variant~(ccpc-SSL) are presented. They effectively exploit the unlabeled data by extracting shared information between different parts of the (high-dimensional) data. The proposed approaches are inductive, and scale well to very large datasets like ImageNet, making them good candidates in real-world large scale applications.
Enhancing Adversarial Defense by k-Winners-Take-All
Authors: Chang Xiao, Peilin Zhong, Changxi Zheng
Date: 2019-05-25 00:00:00
Proceeding: ICLR 2020 1
Tasks: Adversarial Defense, All

We propose a simple change to existing neural network structures for better defending against gradient-based adversarial attacks. Instead of using popular activation functions (such as ReLU), we advocate the use of k-Winners-Take-All (k-WTA) activation, a C0 discontinuous function that purposely invalidates the neural network model's gradient at densely distributed input data points. The proposed k-WTA activation can be readily used in nearly all existing networks and training methods with no significant overhead. Our proposal is theoretically rationalized. We analyze why the discontinuities in k-WTA networks can largely prevent gradient-based search of adversarial examples and why they at the same time remain innocuous to the network training. This understanding is also empirically backed. We test k-WTA activation on various network structures optimized by a training method, be it adversarial training or not. In all cases, the robustness of k-WTA networks outperforms that of traditional networks under white-box attacks.
A Kernel Loss for Solving the Bellman Equation
Authors: Yihao Feng, Lihong Li, Qiang Liu
Date: 2019-05-25 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Value function learning plays a central role in many state-of-the-art reinforcement-learning algorithms. Many popular algorithms like Q-learning do not optimize any objective function, but are fixed-point iterations of some variant of Bellman operator that is not necessarily a contraction. As a result, they may easily lose convergence guarantees, as can be observed in practice. In this paper, we propose a novel loss function, which can be optimized using standard gradient-based methods without risking divergence. The key advantage is that its gradient can be easily approximated using sampled transitions, avoiding the need for double samples required by prior algorithms like residual gradient. Our approach may be combined with general function classes such as neural networks, on either on- or off-policy data, and is shown to work reliably and effectively in several benchmarks.
Learning to Reason in Large Theories without Imitation
Authors: Kshitij Bansal, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Viktor Toman
Date: 2019-05-25 00:00:00
Tasks: Automated Theorem Proving, Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we demonstrate how to do automated theorem proving in the presence of a large knowledge base of potential premises without learning from human proofs. We suggest an exploration mechanism that mixes in additional premises selected by a tf-idf (term frequency-inverse document frequency) based lookup in a deep reinforcement learning scenario. This helps with exploring and learning which premises are relevant for proving a new theorem. Our experiments show that the theorem prover trained with this exploration mechanism outperforms provers that are trained only on human proofs. It approaches the performance of a prover trained by a combination of imitation and reinforcement learning. We perform multiple experiments to understand the importance of the underlying assumptions that make our exploration approach work, thus explaining our design choices.
Cold Case: The Lost MNIST Digits
Authors: Chhavi Yadav, Léon Bottou
Date: 2019-05-25 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Attribute, Image Classification, Model Selection

Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes in accuracy. We trace each MNIST digit to its NIST source and its rich metadata such as writer identifier, partition identifier, etc. We also reconstruct the complete MNIST test set with 60,000 samples instead of the usual 10,000. Since the balance 50,000 were never distributed, they enable us to investigate the impact of twenty-five years of MNIST experiments on the reported testing performances. Our results unambiguously confirm the trends observed by Recht et al. [2018, 2019]: although the misclassification rates are slightly off, classifier ordering and model selection remain broadly reliable. We attribute this phenomenon to the pairing benefits of comparing classifiers on the same digits.
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
Authors: Rui Zhang, Christian Walder, Marian-Andrei Rizoiu
Date: 2019-05-25 00:00:00
Tasks: Model Optimization, Model Selection, Variational Inference

The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.
Finding new routes for integrating Multi-Agent Systems using Apache Camel
Authors: Cleber Jorge Amaral, Sérgio Pereira Bernardes, Mateus Conceição, Jomi Fred Hübner, Luis Pedro Arenhart Lampert, Otávio Arruda Matoso, Maicon Rafael Zatelli
Date: 2019-05-25 00:00:00

In Multi-Agent Systems (MAS) there are two main models of interaction: among agents, and between agents and the environment. Although there are studies considering these models, there is no practical tool to afford the interaction with external entities with both models. This paper presents a proposal for such a tool based on the Apache Camel framework by designing two new components, namely camel-jason and camel-artifact. By means of these components, an external entity is modelled according to its nature, i.e., whether it is autonomous or non-autonomous, interacting with the MAS respectively as an agent or an artifact. It models coherently external entities whereas Camel provides interoperability with several communication protocols.
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
Authors: Sungmin Cha, TaeEon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon
Date: 2019-05-25 00:00:00
Proceeding: ICLR 2021 1
Tasks: Denoising, Image Denoising

We tackle a challenging blind image denoising problem, in which only single distinct noisy images are available for training a denoiser, and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for the given noisy image, and the latter requires two independently realized noisy image pair for a clean image. To that end, we propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise) method that first learns a generative model that can 1) simulate the noise in the given noisy images and 2) generate a rough, noisy estimates of the clean images, then 3) iteratively trains a denoiser with subsequently synthesized noisy image pairs (as in N2N), obtained from the generative model. In results, we show the denoiser trained with our GAN2GAN achieves an impressive denoising performance on both synthetic and real-world datasets for the blind denoising setting; it almost approaches the performance of the standard discriminatively-trained or N2N-trained models that have more information than ours, and it significantly outperforms the recent baseline for the same setting, \textit{e.g.}, Noise2Void, and a more conventional yet strong one, BM3D. The official code of our method is available at https://github.com/csm9493/GAN2GAN.
Designing a Symbolic Intermediate Representation for Neural Surface Realization
Authors: Henry Elder, Jennifer Foster, James Barry, Alexander O'Connor
Date: 2019-05-24 00:00:00
Proceeding: WS 2019 6
Tasks: Hallucination

Generated output from neural NLG systems often contain errors such as hallucination, repetition or contradiction. This work focuses on designing a symbolic intermediate representation to be used in multi-stage neural generation with the intention of reducing the frequency of failed outputs. We show that surface realization from this intermediate representation is of high quality and when the full system is applied to the E2E dataset it outperforms the winner of the E2E challenge. Furthermore, by breaking out the surface realization step from typically end-to-end neural systems, we also provide a framework for non-neural content selection and planning systems to potentially take advantage of semi-supervised pretraining of neural surface realization models.