pwc-archive/papers-with-abstracts huggingface dataset:
The relational processing limits of classic and contemporary neural network models of language processing
Authors: Guillermo Puebla, Andrea E. Martin, Leonidas A. A. Doumas
Date: 2019-05-12 00:00:00
Tasks: Reading Comprehension, Relational Reasoning

The ability of neural networks to capture relational knowledge is a matter of long-standing controversy. Recently, some researchers in the PDP side of the debate have argued that (1) classic PDP models can handle relational structure (Rogers & McClelland, 2008, 2014) and (2) the success of deep learning approaches to text processing suggests that structured representations are unnecessary to capture the gist of human language (Rabovsky et al., 2018). In the present study we tested the Story Gestalt model (St. John, 1992), a classic PDP model of text comprehension, and a Sequence-to-Sequence with Attention model (Bahdanau et al., 2015), a contemporary deep learning architecture for text processing. Both models were trained to answer questions about stories based on the thematic roles that several concepts played on the stories. In three critical test we varied the statistical structure of new stories while keeping their relational structure constant with respect to the training data. Each model was susceptible to each statistical structure manipulation to a different degree, with their performance failing below chance at least under one manipulation. We argue that the failures of both models are due to the fact that they cannotperform dynamic binding of independent roles and fillers. Ultimately, these results cast doubts onthe suitability of traditional neural networks models for explaining phenomena based on relational reasoning, including language processing.
Adaptive surrogate models for parametric studies
Authors: Jan N. Fuhg
Date: 2019-05-12 00:00:00
Tasks: Binary Classification, Contact mechanics

The computational effort for the evaluation of numerical simulations based on e.g. the finite-element method is high. Metamodels can be utilized to create a low-cost alternative. However the number of required samples for the creation of a sufficient metamodel should be kept low, which can be achieved by using adaptive sampling techniques. In this Master thesis adaptive sampling techniques are investigated for their use in creating metamodels with the Kriging technique, which interpolates values by a Gaussian process governed by prior covariances. The Kriging framework with extension to multifidelity problems is presented and utilized to compare adaptive sampling techniques found in the literature for benchmark problems as well as applications for contact mechanics. This thesis offers the first comprehensive comparison of a large spectrum of adaptive techniques for the Kriging framework. Furthermore a multitude of adaptive techniques is introduced to multifidelity Kriging as well as well as to a Kriging model with reduced hyperparameter dimension called partial least squares Kriging. In addition, an innovative adaptive scheme for binary classification is presented and tested for identifying chaotic motion of a Duffing's type oscillator.
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Authors: Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz
Date: 2019-05-12 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing reinforcement learning to guide the configuration of the set of interacting modules that comprise the system. The challenge of doing system-wide optimization is a combinatorial problem. Local attempts to boost the performance of a specific module by modifying its configuration often leads to losses in overall utility of the system's performance as the distribution of inputs to downstream modules changes drastically. We present metareasoning techniques which consider a rich representation of the input, monitor the state of the entire pipeline, and adjust the configuration of modules on-the-fly so as to maximize the utility of a system's operation. We show significant improvement in both real-world and synthetic pipelines across a variety of reinforcement learning techniques.
Linear Range in Gradient Descent
Authors: Angxiu Ni, Chaitanya Talnikar
Date: 2019-05-11 00:00:00

This paper defines linear range as the range of parameter perturbations which lead to approximately linear perturbations in the states of a network. We compute linear range from the difference between actual perturbations in states and the tangent solution. Linear range is a new criterion for estimating the effectivenss of gradients and thus having many possible applications. In particular, we propose that the optimal learning rate at the initial stages of training is such that parameter changes on all minibatches are within linear range. We demonstrate our algorithm on two shallow neural networks and a ResNet.
CoLight: Learning Network-level Cooperation for Traffic Signal Control
Authors: Hua Wei, Nan Xu, Huichu Zhang, Guanjie Zheng, Xinshi Zang, Chacha Chen, Wei-Nan Zhang, Yanmin Zhu, Kai Xu, Zhenhui Li
Date: 2019-05-11 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning, Traffic Signal Control

Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.
Graph U-Nets
Authors: Hongyang Gao, Shuiwang Ji
Date: 2019-05-11 00:00:00
Tasks: Decoder, General Classification, Graph Classification, Graph Embedding, Node Classification, Representation Learning

We consider the problem of representation learning for graph data. Convolutional neural networks can naturally operate on images, but have significant challenges in dealing with graph data. Given images are special cases of graphs with nodes lie on 2D lattices, graph embedding tasks have a natural correspondence with image pixel-wise prediction tasks such as segmentation. While encoder-decoder architectures like U-Nets have been successfully applied on many image pixel-wise prediction tasks, similar methods are lacking for graph data. This is due to the fact that pooling and up-sampling operations are not natural on graph data. To address these challenges, we propose novel graph pooling (gPool) and unpooling (gUnpool) operations in this work. The gPool layer adaptively selects some nodes to form a smaller graph based on their scalar projection values on a trainable projection vector. We further propose the gUnpool layer as the inverse operation of the gPool layer. The gUnpool layer restores the graph into its original structure using the position information of nodes selected in the corresponding gPool layer. Based on our proposed gPool and gUnpool layers, we develop an encoder-decoder model on graph, known as the graph U-Nets. Our experimental results on node classification and graph classification tasks demonstrate that our methods achieve consistently better performance than previous models.
A Distributed Approach towards Discriminative Distance Metric Learning
Authors: Jun Li, Xun Lin, Xiaoguang Rui, Yong Rui, DaCheng Tao
Date: 2019-05-11 00:00:00
Tasks: Metric Learning

Distance metric learning is successful in discovering intrinsic relations in data. However, most algorithms are computationally demanding when the problem size becomes large. In this paper, we propose a discriminative metric learning algorithm, and develop a distributed scheme learning metrics on moderate-sized subsets of data, and aggregating the results into a global solution. The technique leverages the power of parallel computation. The algorithm of the aggregated distance metric learning (ADML) scales well with the data size and can be controlled by the partition. We theoretically analyse and provide bounds for the error induced by the distributed treatment. We have conducted experimental evaluation of ADML, both on specially designed tests and on practical image annotation tasks. Those tests have shown that ADML achieves the state-of-the-art performance at only a fraction of the cost incurred by most existing methods.
Towards a Quantum-Like Cognitive Architecture for Decision-Making
Authors: Catarina Moreira, Lauren Fell, Shahram Dehdashti, Peter Bruza, Andreas Wichert
Date: 2019-05-11 00:00:00
Tasks: Decision Making

We propose an alternative and unifying framework for decision-making that, by using quantum mechanics, provides more generalised cognitive and decision models with the ability to represent more information than classical models. This framework can accommodate and predict several cognitive biases reported in Lieder & Griffiths without heavy reliance on heuristics nor on assumptions of the computational resources of the mind.
Encrypted Speech Recognition using Deep Polynomial Networks
Authors: Shi-Xiong Zhang, Yifan Gong, Dong Yu
Date: 2019-05-11 00:00:00
Tasks: speech-recognition, Speech Recognition

The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in cloud may also reveal some sensitive information. This paper proposes a deep polynomial network (DPN) that can be applied to the encrypted speech as an acoustic model. It allows clients to send their data in an encrypted form to the cloud to ensure that their data remains confidential, at mean while the DPN can still make frame-level predictions over the encrypted speech and return them in encrypted form. One good property of the DPN is that it can be trained on unencrypted speech features in the traditional way. To keep the cloud away from the raw audio and recognition results, a cloud-local joint decoding framework is also proposed. We demonstrate the effectiveness of model and framework on the Switchboard and Cortana voice assistant tasks with small performance degradation and latency increased comparing with the traditional cloud-based DNNs.
Embeddings of Persistence Diagrams into Hilbert Spaces
Authors: Peter Bubenik, Alexander Wagner
Date: 2019-05-11 00:00:00

Since persistence diagrams do not admit an inner product structure, a map into a Hilbert space is needed in order to use kernel methods. It is natural to ask if such maps necessarily distort the metric on persistence diagrams. We show that persistence diagrams with the bottleneck distance do not even admit a coarse embedding into a Hilbert space. As part of our proof, we show that any separable, bounded metric space isometrically embeds into the space of persistence diagrams with the bottleneck distance. As corollaries, we obtain the generalized roundness, negative type, and asymptotic dimension of this space.
A logical-based corpus for cross-lingual evaluation
Authors: Felipe Salvatore, Marcelo Finger, Roberto Hirata Jr
Date: 2019-05-10 00:00:00
Proceeding: WS 2019 11
Tasks: Cross-Lingual Transfer, Natural Language Inference, Transfer Learning

At present, different deep learning models are presenting high accuracy on popular inference datasets such as SNLI, MNLI, and SciTail. However, there are different indicators that those datasets can be exploited by using some simple linguistic patterns. This fact poses difficulties to our understanding of the actual capacity of machine learning models to solve the complex task of textual inference. We propose a new set of syntactic tasks focused on contradiction detection that require specific capacities over linguistic logical forms such as: Boolean coordination, quantifiers, definite description, and counting operators. We evaluate two kinds of deep learning models that implicitly exploit language structure: recurrent models and the Transformer network BERT. We show that although BERT is clearly more efficient to generalize over most logical forms, there is space for improvement when dealing with counting operators. Since the syntactic tasks can be implemented in different languages, we show a successful case of cross-lingual transfer learning between English and Portuguese.
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization
Authors: Hao Yu, Rong Jin
Date: 2019-05-10 00:00:00
Tasks: Stochastic Optimization

For SGD based distributed stochastic optimization, computation complexity, measured by the convergence rate in terms of the number of stochastic gradient calls, and communication complexity, measured by the number of inter-node communication rounds, are two most important performance metrics. The classical data-parallel implementation of SGD over $N$ workers can achieve linear speedup of its convergence rate but incurs an inter-node communication round at each batch. We study the benefit of using dynamically increasing batch sizes in parallel SGD for stochastic non-convex optimization by charactering the attained convergence rate and the required number of communication rounds. We show that for stochastic non-convex optimization under the P-L condition, the classical data-parallel SGD with exponentially increasing batch sizes can achieve the fastest known $O(1/(NT))$ convergence with linear speedup using only $\log(T)$ communication rounds. For general stochastic non-convex optimization, we propose a Catalyst-like algorithm to achieve the fastest known $O(1/\sqrt{NT})$ convergence with only $O(\sqrt{NT}\log(\frac{T}{N}))$ communication rounds.
Markov versus quantum dynamic models of belief change during evidence monitoring
Authors: Jerome R. Busemeyer, Peter D. Kvam, Timothy J. Pleskac
Date: 2019-05-10 00:00:00

Two different dynamic models for belief change during evidence monitoring were evaluated: Markov and quantum. They were empirically tested with an experiment in which participants monitored evidence for an initial period of time, made a probability rating, then monitored more evidence, before making a second rating. The models were qualitatively tested by manipulating the time intervals in a manner that provided a test for interference effects of the first rating on the second. The Markov model predicted no interference whereas the quantum model predicted interference. A quantitative comparison of the two models was also carried out using a generalization criterion method: the parameters were fit to data from one set of time intervals, and then these same parameters were used to predict data from another set of time intervals. The results indicated that some features of both Markov and quantum models are needed to accurately account for the results.
Modeling user context for valence prediction from narratives
Authors: Aniruddha Tammewar, Alessandra Cervone, Eva-Maria Messner, Giuseppe Riccardi
Date: 2019-05-09 00:00:00
Tasks: Prediction

Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the task of valence prediction was framed as a three-class classification problem using 8 seconds fragments from individuals' narratives. As such, the task did not allow for exploring contextual information of the narratives. In this work, we investigate the intrinsic information from multiple narratives recounted by the same individual in order to predict their current state-of-mind. Furthermore, with generalizability in mind, we decided to focus our experiments exclusively on textual information as the public availability of audio narratives is limited compared to text. Our hypothesis is, that context modeling might provide insights about emotion triggering concepts (e.g. events, people, places) mentioned in the narratives that are linked to an individual's state of mind. We explore multiple machine learning techniques to model narratives. We find that the models are able to capture inter-individual differences, leading to more accurate predictions of an individual's emotional state, as compared to single narratives.
Graph Width Measures for CNF-Encodings with Auxiliary Variables
Authors: Stefan Mengel, Romain Wallon
Date: 2019-05-09 00:00:00

We consider bounded width CNF-formulas where the width is measured by popular graph width measures on graphs associated to CNF-formulas. Such restricted graph classes, in particular those of bounded treewidth, have been extensively studied for their uses in the design of algorithms for various computational problems on CNF-formulas. Here we consider the expressivity of these formulas in the model of clausal encodings with auxiliary variables. We first show that bounding the width for many of the measures from the literature leads to a dramatic loss of expressivity, restricting the formulas to such of low communication complexity. We then show that the width of optimal encodings with respect to different measures is strongly linked: there are two classes of width measures, one containing primal treewidth and the other incidence cliquewidth, such that in each class the width of optimal encodings only differs by constant factors. Moreover, between the two classes the width differs at most by a factor logarithmic in the number of variables. Both these results are in stark contrast to the setting without auxiliary variables where all width measures we consider here differ by more than constant factors and in many cases even by linear factors.
SuperChat: Dialogue Generation by Transfer Learning from Vision to Language using Two-dimensional Word Embedding and Pretrained ImageNet CNN Models
Authors: Baohua Sun, Lin Yang, Michael Lin, Charles Young, Jason Dong, Wenhan Zhang, Patrick Dong
Date: 2019-05-07 00:00:00
Tasks: Dialogue Generation, General Classification, text-classification, Text Classification, Transfer Learning

The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational response for open domain dialogues. The experimental results on a public dataset shows that the proposed SuperChat method generates high quality responses. An interactive demo is ready to show at the workshop.
Development of Deep Learning Based Natural Language Processing Model for Turkish
Authors: Baris Baburoglu, Adem Tekerek, Mehmet Tekerek
Date: 2019-05-07 00:00:00
Tasks: Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging

Natural language is one of the most fundamental features that distinguish people from other living things and enable people to communicate each other. Language is a tool that enables people to express their feelings and thoughts and to transfers cultures through generations. Texts and audio are examples of natural language in daily life. In the natural language, many words disappear in time, on the other hand new words are derived. Therefore, while the process of natural language processing (NLP) is complex even for human, it is difficult to process in computer system. The area of linguistics examines how people use language. NLP, which requires the collaboration of linguists and computer scientists, plays an important role in human computer interaction. Studies in NLP have increased with the use of artificial intelligence technologies in the field of linguistics. With the deep learning methods which are one of the artificial intelligence study areas, platforms close to natural language are being developed. Developed platforms for language comprehension, machine translation and part of speech (POS) tagging benefit from deep learning methods. Recurrent Neural Network (RNN), one of the deep learning architectures, is preferred for processing sequential data such as text or audio data. In this study, Turkish POS tagging model has been proposed by using Bidirectional Long-Short Term Memory (BLSTM) which is an RNN type. The proposed POS tagging model is provided to natural language researchers with a platform that allows them to perform and use their own analysis. In the development phase of the platform developed by using BLSTM, the error rate of the POS tagger has been reduced by taking feedback with expert opinion.
Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis
Authors: Waleed A. Yousef, Omar M. Ibrahime, Taha M. Madbouly, Moustafa A. Mahmoud
Date: 2019-05-07 00:00:00
Tasks: Poem meters classification

Recognizing a piece of writing as a poem or prose is usually easy for the majority of people; however, only specialists can determine which meter a poem belongs to. In this paper, we build Recurrent Neural Network (RNN) models that can classify poems according to their meters from plain text. The input text is encoded at the character level and directly fed to the models without feature handcrafting. This is a step forward for machine understanding and synthesis of languages in general, and Arabic language in particular. Among the 16 poem meters of Arabic and the 4 meters of English the networks were able to correctly classify poem with an overall accuracy of 96.38\% and 82.31\% respectively. The poem datasets used to conduct this research were massive, over 1.5 million of verses, and were crawled from different nontechnical sources, almost Arabic and English literature sites, and in different heterogeneous and unstructured formats. These datasets are now made publicly available in clean, structured, and documented format for other future research. To the best of the authors' knowledge, this research is the first to address classifying poem meters in a machine learning approach, in general, and in RNN featureless based approach, in particular. In addition, the dataset is the first publicly available dataset ready for the purpose of future computational research.