pwc-archive/papers-with-abstracts huggingface dataset:
Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
Authors: Ahmad Sakor, Kuldeep Singh, Anery Patel, Maria-Esther Vidal
Date: 2019-12-24 00:00:00
Tasks: Knowledge Base Question Answering, Knowledge Graphs, Language Modelling, Relation, Relation Linking

The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, still, there are limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, first joint entity, and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is public and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository. We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
Open-domain Event Extraction and Embedding for Natural Gas Market Prediction
Authors: Minh Triet Chau, Diego Esteves, Jens Lehmann
Date: 2019-12-08 00:00:00
Tasks: Event Extraction, Sentence, Sentence Embedding, Sentence-Embedding, Time Series, Time Series Analysis

We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines. Most previous methods treats price as an extrapolatable time series, those analyze the relation between prices and news either trim their price data correspondingly to a public news dataset, manually annotate headlines or use off-the-shelf tools. In comparison to off-the-shelf tools, our event extraction method detects not only the occurrence of phenomena but also the changes in attribution and characteristics from public sources. Instead of using sentence embedding as a feature, we use every word of the extracted events, encode and organize them before feeding to the learning models. Empirical results show favorable results, in terms of prediction performance, money saved and scalability.
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications
Authors: Sri Harsha Dumpala, Imran Sheikh, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2019-12-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Generative Adversarial Network, speech-recognition, Speech Recognition

Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems. In this paper, we propose a front-end based on Cycle-Consistent Generative Adversarial Network (CycleGAN) which transforms naturally perturbed speech into normal speech, and hence improves the robustness of an ASR system. The CycleGAN model is trained on non-parallel examples of perturbed and normal speech. Experiments on spontaneous laughter-speech and creaky-speech datasets show that the performance of four different ASR systems improve by using speech obtained from CycleGAN based front-end, as compared to directly using the original perturbed speech. Visualization of the features of the laughter perturbed speech and those generated by the proposed front-end further demonstrates the effectiveness of our approach.
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics
Authors: Sungyong Seo*, Chuizheng Meng*, Yan Liu
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems. The discretization error becomes even larger when the sparse data are irregularly distributed so that the data defined on an unstructured grid, making it hard to build deep learning models to handle physics-governing observations on the unstructured grid. In this paper, we propose a novel architecture named Physics-aware Difference Graph Networks (PA-DGN) that exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN further leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given observations. We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from weather stations.
dAIrector: Automatic Story Beat Generation through Knowledge Synthesis
Authors: Markus Eger, Kory W. Mathewson
Date: 2018-10-31 00:00:00

dAIrector is an automated director which collaborates with humans storytellers for live improvisational performances and writing assistance. dAIrector can be used to create short narrative arcs through contextual plot generation. In this work, we present the system architecture, a quantitative evaluation of design choices, and a case-study usage of the system which provides qualitative feedback from a professional improvisational performer. We present relevant metrics for the understudied domain of human-machine creative generation, specifically long-form narrative creation. We include, alongside publication, open-source code so that others may test, evaluate, and run the dAIrector.
A Multimodal Target-Source Classifier with Attention Branches to Understand Ambiguous Instructions for Fetching Daily Objects
Authors: Aly Magassouba, Komei Sugiura, Hisashi Kawai
Date: 2019-12-23 00:00:00
Tasks: Sentence

In this study, we focus on multimodal language understanding for fetching instructions in the domestic service robots context. This task consists of predicting a target object, as instructed by the user, given an image and an unstructured sentence, such as "Bring me the yellow box (from the wooden cabinet)." This is challenging because of the ambiguity of natural language, i.e., the relevant information may be missing or there might be several candidates. To solve such a task, we propose the multimodal target-source classifier model with attention branches (MTCM-AB), which is an extension of the MTCM. Our methodology uses the attention branch network (ABN) to develop a multimodal attention mechanism based on linguistic and visual inputs. Experimental validation using a standard dataset showed that the MTCM-AB outperformed both state-of-the-art methods and the MTCM. In particular the MTCM-AB accuracy on average was 90.1% while human performance was 90.3% on the PFN-PIC dataset.
Recurrent Feedback Improves Feedforward Representations in Deep Neural Networks
Authors: Siming Yan, Xuyang Fang, Bowen Xiao, Harold Rockwell, Yimeng Zhang, Tai Sing Lee
Date: 2019-12-22 00:00:00

The abundant recurrent horizontal and feedback connections in the primate visual cortex are thought to play an important role in bringing global and semantic contextual information to early visual areas during perceptual inference, helping to resolve local ambiguity and fill in missing details. In this study, we find that introducing feedback loops and horizontal recurrent connections to a deep convolution neural network (VGG16) allows the network to become more robust against noise and occlusion during inference, even in the initial feedforward pass. This suggests that recurrent feedback and contextual modulation transform the feedforward representations of the network in a meaningful and interesting way. We study the population codes of neurons in the network, before and after learning with feedback, and find that learning with feedback yielded an increase in discriminability (measured by d-prime) between the different object classes in the population codes of the neurons in the feedforward path, even at the earliest layer that receives feedback. We find that recurrent feedback, by injecting top-down semantic meaning to the population activities, helps the network learn better feedforward paths to robustly map noisy image patches to the latent representations corresponding to important visual concepts of each object class, resulting in greater robustness of the network against noises and occlusion as well as better fine-grained recognition.
Machine Learning and Blockchain for Fraud Detection: Employing Artificial Intelligence in the Banking Sector
Authors: Vinita Silaparasetty
Date: 2018-06-18 00:00:00
Proceeding: GCU INTERNATIONAL KNOWLEDGE TRANSFER CONCLAVE 2018 6
Tasks: BIG-bench Machine Learning, Fraud Detection

Fraudulent banking operations can cause huge losses to the bank and further affect the economy negatively. What if Blockchain Technology and Machine Learning could be combined to detect suspicious banking activity and stop transactions at the source? That is what this paper aims to do.
In this paper, a system is created which consists of a the following components:
1) Blockchain: To securely store transaction history.
2) XGBoosted KMenas algorithm: For quick and efficient detection of outliers, which indicate suspicious
activity.
3) Apache Ignite: This is an open source platform that provides powerful computing for real-time Machine
Learning.
Key words: Artificial Intelligence, Machine Learning, Data Science, Algorithm, Blockchain, Means, Apache.
Machine Learning of Accurate Energy-conserving Molecular Force Fields
Authors: Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R.
Date: 2017-05-05 00:00:00
Proceeding: Science Advances 2017 5
Tasks: Atomic Forces, BIG-bench Machine Learning

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Authors: Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A.
Date: 2018-08-24 00:00:00
Proceeding: Nature Communications 2018 8

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Authors: Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A.
Date: 2019-07-01 00:00:00
Proceeding: Computer Physics Communications 2019 7
Tasks: Atomic Forces, BIG-bench Machine Learning

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.
Boltzmann Exploration Expectation–Maximisation
Authors: Mathias Edman, Neil Dhir
Date: 2019-12-18 00:00:00
Proceeding: arXiv 2019 12
Tasks: Iris Segmentation

We present a general method for fitting finite mixture models (FMM). Learning
in a mixture model consists of finding the most likely cluster assignment for each
data-point, as well as finding the parameters of the clusters themselves. In many
mixture models, this is difficult with current learning methods, where the most common approach is to employ monotone learning algorithms e.g. the conventional expectation-maximisation algorithm. While effective, the success of any
monotone algorithm is crucially dependant on good parameter initialisation, where
a common choice is K-means initialisation, commonly employed for Gaussian
mixture models.
For other types of mixture models, the path to good initialisation parameters is often
unclear and may require a problem-specific solution. To this end, we propose a
general heuristic learning algorithm that utilises Boltzmann exploration to assign
each observation to a specific base distribution within the mixture model, which we
call Boltzmann exploration expectation-maximisation (BEEM). With BEEM, hard
assignments allow straight forward parameter learning for each base distribution
by conditioning only on its assigned observations. Consequently, it can be applied
to mixtures of any base distribution where single component parameter learning is
tractable. The stochastic learning procedure is able to escape local optima and is
thus insensitive to parameter initialisation. We show competitive performance on a
number of synthetic benchmark cases as well as on real-world datasets.
Neural Separation of Observed and Unobserved Distribution
Authors: Tavi Halperin, Ariel Ephrat, Yedid Hoshen
Date: 2018-11-30 00:00:00
Proceeding: ICML'19 2018 11

Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution. Our method iteratively learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce Latent Mixture Masking which ensures a good initialization. Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision.
Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach
Authors: Jaewon Yang and Jure Leskovec
Date: 2013-02-04 00:00:00
Proceeding: WSDM 2013 2
Tasks: Community Detection

Network communities represent basic structures for understanding the organization of real-world networks. A community (also referred to as a module or a cluster) is typically thought of as a group of nodes with more connections amongst its members than between its members and the remainder of the network. Communities in networks also overlap as nodes belong to multiple clusters at once. Due to the difficulties in evaluating the detected communities and the lack of scalable algorithms, the task of overlapping community detection in large networks largely remains an open problem.

In this paper we present BIGCLAM (Cluster Affiliation Model for Big Networks), an overlapping community detection method that scales to large networks of millions of nodes and edges. We build on a novel observation that overlaps between communities are densely connected. This is in sharp contrast with present community detection methods which implicitly assume that overlaps between communities are sparsely connected and thus cannot properly extract overlapping communities in networks. In this paper, we develop a model-based community detection algorithm that can detect densely overlapping, hierarchically nested as well as non-overlapping communities in massive networks. We evaluate our algorithm on 6 large social, collaboration and information networks with ground-truth community information. Experiments show state of the art performance both in terms of the quality of detected communities as well as in speed and scalability of our algorithm.
Using Data Mining to Predict Hospital Admissions From the Emergency Department
Authors: BYRON GRAHAM 1, RAYMOND BOND2, MICHAEL QUINN3, AND MAURICE MULVENNA2, (Senior Member, IEEE)
Date: 2018-02-22 00:00:00
Proceeding: journal 2018 2
Tasks: BIG-bench Machine Learning, regression

Crowding within emergency departments (EDs) can have signicant negative consequences
for patients. EDs therefore need to explore the use of innovative methods to improve patient ow and prevent
overcrowding. One potential method is the use of data mining using machine learning techniques to predict
ED admissions. This paper uses routinely collected administrative data (120 600 records) from two major
acute hospitals in Northern Ireland to compare contrasting machine learning algorithms in predicting the risk
of admission from the ED. We use three algorithms to build the predictive models: 1) logistic regression;
2) decision trees; and 3) gradient boosted machines (GBM). The GBM performed better (accuracy D
80:31%, AUC-ROC D 0:859) than the decision tree (accuracy D 80:06%, AUC-ROC D 0:824) and the
logistic regression model (accuracy D 79:94%, AUC-ROC D 0:849). Drawing on logistic regression,
we identify several factors related to hospital admissions, including hospital site, age, arrival mode, triage
category, care group, previous admission in the past month, and previous admission in the past year. This
paper highlights the potential utility of three common machine learning algorithms in predicting patient
admissions. Practical implementation of the models developed in this paper in decision support tools
would provide a snapshot of predicted admissions from the ED at a given time, allowing for advance
resource planning and the avoidance bottlenecks in patient ow, as well as comparison of predicted and
actual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic
regression models, although GBM's will be useful where accuracy is paramount.
Learning by Cheating
Authors: Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
Date: 2019-12-27 00:00:00
Tasks: Autonomous Driving

Vision-based urban driving is hard. The autonomous system needs to learn to perceive the world and act in it. We show that this challenging learning problem can be simplified by decomposing it into two stages. We first train an agent that has access to privileged information. This privileged agent cheats by observing the ground-truth layout of the environment and the positions of all traffic participants. In the second stage, the privileged agent acts as a teacher that trains a purely vision-based sensorimotor agent. The resulting sensorimotor agent does not have access to any privileged information and does not cheat. This two-stage training procedure is counter-intuitive at first, but has a number of important advantages that we analyze and empirically demonstrate. We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art on the CARLA benchmark and the recent NoCrash benchmark. Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art. For the video that summarizes this work, see https://youtu.be/u9ZCxxD-UUw
Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization
Authors: Lourenço V. Pato, Renato Negrinho, Pedro M. Q. Aguiar
Date: 2019-12-27 00:00:00
Tasks: object-detection, Object Detection

The majority of current object detectors lack context: class predictions are made independently from other detections. We propose to incorporate context in object detection by post-processing the output of an arbitrary detector to rescore the confidences of its detections. Rescoring is done by conditioning on contextual information from the entire set of detections: their confidences, predicted classes, and positions. We show that AP can be improved by simply reassigning the detection confidence values such that true positives that survive longer (i.e., those with the correct class and large IoU) are scored higher than false positives or detections with small IoU. In this setting, we use a bidirectional RNN with attention for contextual rescoring and introduce a training target that uses the IoU with ground truth to maximize AP for the given set of detections. The fact that our approach does not require access to visual features makes it computationally inexpensive and agnostic to the detection architecture. In spite of this simplicity, our model consistently improves AP over strong pre-trained baselines (Cascade R-CNN and Faster R-CNN with several backbones), particularly by reducing the confidence of duplicate detections (a learned form of non-maximum suppression) and removing out-of-context objects by conditioning on the confidences, classes, positions, and sizes of the co-occurrent detections. Code is available at https://github.com/LourencoVazPato/seeing-without-looking/