pwc-archive/papers-with-abstracts huggingface dataset:
Effect of The Latent Structure on Clustering with GANs
Authors: Deepak Mishra, Aravind Jayendran, Prathosh A. P
Date: 2020-05-05 00:00:00
Tasks: Clustering

Generative adversarial networks (GANs) have shown remarkable success in generation of data from natural data manifolds such as images. In several scenarios, it is desirable that generated data is well-clustered, especially when there is severe class imbalance. In this paper, we focus on the problem of clustering in generated space of GANs and uncover its relationship with the characteristics of the latent space. We derive from first principles, the necessary and sufficient conditions needed to achieve faithful clustering in the GAN framework: (i) presence of a multimodal latent space with adjustable priors, (ii) existence of a latent space inversion mechanism and (iii) imposition of the desired cluster priors on the latent space. We also identify the GAN models in the literature that partially satisfy these conditions and demonstrate the importance of all the components required, through ablative studies on multiple real world image datasets. Additionally, we describe a procedure to construct a multimodal latent space which facilitates learning of cluster priors with sparse supervision.
Stolen Probability: A Structural Weakness of Neural Language Models
Authors: David Demeter, Gregory Kimmel, Doug Downey
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Inductive Bias

Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space. The dot-product distance metric forms part of the inductive bias of NNLMs. Although NNLMs optimize well with this inductive bias, we show that this results in a sub-optimal ordering of the embedding space that structurally impoverishes some words at the expense of others when assigning probability. We present numerical, theoretical and empirical analyses showing that words on the interior of the convex hull in the embedding space have their probability bounded by the probabilities of the words on the hull.
Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
Authors: Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, Joelle Pineau
Date: 2020-05-05 00:00:00
Tasks: BIG-bench Machine Learning

We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes. We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account. We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints. Our model is used in Korbit, a large-scale dialogue-based ITS with thousands of students launched in 2019, and we demonstrate that the personalized feedback leads to considerable improvement in student learning outcomes and in the subjective evaluation of the feedback.
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Authors: Zhishuai Guo, Mingrui Liu, Zhuoning Yuan, Li Shen, Wei Liu, Tianbao Yang
Date: 2020-05-05 00:00:00
Proceeding: ICML 2020 1
Tasks: Distributed Optimization

In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been investigated extensively in deep learning, they are not directly applicable to stochastic AUC maximization with deep neural networks due to its striking differences from standard loss minimization problems (e.g., cross-entropy). Towards addressing this challenge, we propose and analyze a communication-efficient distributed optimization algorithm based on a {\it non-convex concave} reformulation of the AUC maximization, in which the communication of both the primal variable and the dual variable between each worker and the parameter server only occurs after multiple steps of gradient-based updates in each worker. Compared with the naive parallel version of an existing algorithm that computes stochastic gradients at individual machines and averages them for updating the model parameters, our algorithm requires a much less number of communication rounds and still achieves a linear speedup in theory. To the best of our knowledge, this is the \textbf{first} work that solves the {\it non-convex concave min-max} problem for AUC maximization with deep neural networks in a communication-efficient distributed manner while still maintaining the linear speedup property in theory. Our experiments on several benchmark datasets show the effectiveness of our algorithm and also confirm our theory.
Community Detection Clustering via Gumbel Softmax
Authors: Deepak Bhaskar Acharya, Huaming Zhang
Date: 2020-05-05 00:00:00
Tasks: Clustering, Community Detection, Deep Learning, Graph Clustering, Miscellaneous, Recommendation Systems, speech-recognition, Speech Recognition

Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets. Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences. In this paper, we propose a method of community detection clustering the nodes of various graph datasets. We cluster different category datasets that belong to Affiliation networks, Animal networks, Human contact networks, Human social networks, Miscellaneous networks. The deep learning role in modeling the interaction between nodes in a network allows a revolution in the field of science relevant to graph network analysis. In this paper, we extend the gumbel softmax approach to graph network clustering. The experimental findings on specific graph datasets reveal that the new approach outperforms traditional clustering significantly, which strongly shows the efficacy of deep learning in graph community detection clustering. We do a series of experiments on our graph clustering algorithm, using various datasets: Zachary karate club, Highland Tribe, Train bombing, American Revolution, Dolphins, Zebra, Windsurfers, Les Mis\'erables, Political books.
Classification-Based Anomaly Detection for General Data
Authors: Liron Bergman, Yedid Hoshen
Date: 2020-05-05 00:00:00
Proceeding: ICLR 2020 1
Tasks: Anomaly Detection, Classification, General Classification

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The strong performance of our method is extensively validated on multiple datasets from different domains.
Sub-Image Anomaly Detection with Deep Pyramid Correspondences
Authors: Niv Cohen, Yedid Hoshen
Date: 2020-05-05 00:00:00
Tasks: Anomaly Classification, Anomaly Detection, Anomaly Segmentation, Segmentation, Unsupervised Anomaly Detection

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong anomaly detection performance when applied to entire images. A limitation of kNN methods is the lack of segmentation map describing where the anomaly lies inside the image. In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images. Our method, Semantic Pyramid Anomaly Detection (SPADE) uses correspondences based on a multi-resolution feature pyramid. SPADE is shown to achieve state-of-the-art performance on unsupervised anomaly detection and localization while requiring virtually no training time.
Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
Authors: Shixiang Chen, Zengde Deng, Shiqian Ma, Anthony Man-Cho So
Date: 2020-05-05 00:00:00
Tasks: Dictionary Learning

We consider the problem of maximizing the $\ell_1$ norm of a linear map over the sphere, which arises in various machine learning applications such as orthogonal dictionary learning (ODL) and robust subspace recovery (RSR). The problem is numerically challenging due to its nonsmooth objective and nonconvex constraint, and its algorithmic aspects have not been well explored. In this paper, we show how the manifold structure of the sphere can be exploited to design fast algorithms for tackling this problem. Specifically, our contribution is threefold. First, we present a manifold proximal point algorithm (ManPPA) for the problem and show that it converges at a sublinear rate. Furthermore, we show that ManPPA can achieve a quadratic convergence rate when applied to the ODL and RSR problems. Second, we propose a stochastic variant of ManPPA called StManPPA, which is well suited for large-scale computation, and establish its sublinear convergence rate. Both ManPPA and StManPPA have provably faster convergence rates than existing subgradient-type methods. Third, using ManPPA as a building block, we propose a new approach to solving a matrix analog of the problem, in which the sphere is replaced by the Stiefel manifold. The results from our extensive numerical experiments on the ODL and RSR problems demonstrate the efficiency and efficacy of our proposed methods.
It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
Authors: Emanuele Bugliarello, Sabrina J. Mielke, Antonios Anastasopoulos, Ryan Cotterell, Naoaki Okazaki
Date: 2020-05-05 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Translation

The performance of neural machine translation systems is commonly evaluated in terms of BLEU. However, due to its reliance on target language properties and generation, the BLEU metric does not allow an assessment of which translation directions are more difficult to model. In this paper, we propose cross-mutual information (XMI): an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models. XMI allows us to better evaluate the difficulty of translating text into the target language while controlling for the difficulty of the target-side generation component independent of the translation task. We then present the first systematic and controlled study of cross-lingual translation difficulties using modern neural translation systems. Code for replicating our experiments is available online at https://github.com/e-bug/nmt-difficulty.
Differential Machine Learning
Authors: Brian Huge, Antoine Savine
Date: 2020-05-05 00:00:00
Proceeding: arXiv 2020 5
Tasks: BIG-bench Machine Learning, Management, Mathematical Proofs

Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations. Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on https://github.com/differential-machine-learning
Heuristic-Based Weak Learning for Automated Decision-Making
Authors: Ryan Steed, Benjamin Williams
Date: 2020-05-05 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Machine learning systems impact many stakeholders and groups of users, often disparately. Prior studies have reconciled conflicting user preferences by aggregating a high volume of manually labeled pairwise comparisons, but this technique may be costly or impractical. How can we lower the barrier to participation in algorithm design? Instead of creating a simplified labeling task for a crowd, we suggest collecting ranked decision-making heuristics from a focused sample of affected users. With empirical data from two use cases, we show that our weak learning approach, which requires little to no manual labeling, agrees with participants' pairwise choices nearly as often as fully supervised approaches.
A Hierarchical Approach to Multi-Energy Demand Response: From Electricity to Multi-Energy Applications
Authors: Ali Hassan, Samrat Acharya, Michael Chertkov, Deepjyoti Deka, Yury Dvorkin
Date: 2020-05-05 00:00:00

Due to proliferation of energy efficiency measures and availability of the renewable energy resources, traditional energy infrastructure systems (electricity, heat, gas) can no longer be operated in a centralized manner under the assumption that consumer behavior is inflexible, i.e. cannot be adjusted in return for an adequate incentive. To allow for a less centralized operating paradigm, consumer-end perspective and abilities should be integrated in current dispatch practices and accounted for in switching between different energy sources not only at the system but also at the individual consumer level. Since consumers are confined within different built environments, this paper looks into an opportunity to control energy consumption of an aggregation of many residential, commercial and industrial consumers, into an ensemble. This ensemble control becomes a modern demand response contributor to the set of modeling tools for multi-energy infrastructure systems.
The computerization of archaeology: survey on AI techniques
Authors: Lorenzo Mantovan, Loris Nanni
Date: 2020-05-05 00:00:00
Tasks: Survey

This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions; the use of humanoid robots and holographic displays as guides that interact and involve museum visitors; b) The analysis of methods for the classification of fragments found in archaeological excavations and for the reconstruction of ceramics, with the recomposition of the parts of text missing from historical documents and epigraphs; c) The cataloguing and study of human remains to understand the social and historical context of belonging with the demonstration of the effectiveness of the AI techniques used; d) The detection of particularly difficult terrestrial archaeological sites with the analysis of the architectures of the Artificial Neural Networks most suitable for solving the problems presented by the site; the design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version.
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
Authors: Mahsan Nourani, Chiradeep Roy, Tahrima Rahman, Eric D. Ragan, Nicholas Ruozzi, Vibhav Gogate
Date: 2020-05-05 00:00:00
Tasks: Activity Recognition, Decision Making

Explainable machine learning and artificial intelligence models have been used to justify a model's decision-making process. This added transparency aims to help improve user performance and understanding of the underlying model. However, in practice, explainable systems face many open questions and challenges. Specifically, designers might reduce the complexity of deep learning models in order to provide interpretability. The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model. This can further add confusion to the users as they might not find the explanations meaningful with respect to the model predictions. Understanding how these explanations affect user behavior is an ongoing challenge. In this paper, we explore how explanation veracity affects user performance and agreement in intelligent systems. Through a controlled user study with an explainable activity recognition system, we compare variations in explanation veracity for a video review and querying task. The results suggest that low veracity explanations significantly decrease user performance and agreement compared to both accurate explanations and a system without explanations. These findings demonstrate the importance of accurate and understandable explanations and caution that poor explanations can sometimes be worse than no explanations with respect to their effect on user performance and reliance on an AI system.
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Authors: Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian Spears
Date: 2020-05-05 00:00:00
Tasks: Small Data Image Classification

Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning (ML) methods, such as deep neural networks, to build data-driven emulators. While the majority of existing efforts has focused on tailoring off-the-shelf ML solutions to better suit the scientific problem at hand, we study an often overlooked, yet important, problem of choosing loss functions to measure the discrepancy between observed data and the predictions from a model. Due to lack of better priors on the expected residual structure, in practice, simple choices such as the mean squared error and the mean absolute error are made. However, the inherent symmetric noise assumption made by these loss functions makes them inappropriate in cases where the data is heterogeneous or when the noise distribution is asymmetric. We propose Learn-by-Calibrating (LbC), a novel deep learning approach based on interval calibration for designing emulators in scientific applications, that are effective even with heterogeneous data and are robust to outliers. Using a large suite of use-cases, we show that LbC provides significant improvements in generalization error over widely-adopted loss function choices, achieves high-quality emulators even in small data regimes and more importantly, recovers the inherent noise structure without any explicit priors.
Digraphie des langues ouest africaines : Latin2Ajami : un algorithme de translitteration automatique
Authors: El hadji M. Fall, El hadji M. Nguer, Bao Diop Sokhna, Mouhamadou Khoule, Mathieu Mangeot, Mame T. Cisse
Date: 2020-05-05 00:00:00
Tasks: Transliteration

The national languages of Senegal, like those of West Africa country in general, are written with two alphabets : the Latin alphabet that draws its strength from official decreesm and the completed Arabic script (Ajami), widespread and well integrated, that has little institutional support. This digraph created two worlds ignoring each other. Indeed, Ajami writing is generally used daily by populations from Koranic schools, while writing with the Latin alphabet is used by people from the public school. To solve this problem, it is useful to establish transliteration tools between these two scriptures. Preliminary work (Nguer, Bao-Diop, Fall, khoule, 2015) was performed to locate the problems, challenges and prospects. This present work, making it subsequently fell into this. Its objective is the study and creation of a transliteration algorithm from latin towards Ajami.
Deep Constraint-based Propagation in Graph Neural Networks
Authors: Matteo Tiezzi, Giuseppe Marra, Stefano Melacci, Marco Maggini
Date: 2020-05-05 00:00:00

The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the nodes of the graph by means of an iterative diffusion procedure that, during the learning stage, must be computed at every epoch, until the fixed point of a learnable state transition function is reached, propagating the information among the neighbouring nodes. We propose a novel approach to learning in GNNs, based on constrained optimization in the Lagrangian framework. Learning both the transition function and the node states is the outcome of a joint process, in which the state convergence procedure is implicitly expressed by a constraint satisfaction mechanism, avoiding iterative epoch-wise procedures and the network unfolding. Our computational structure searches for saddle points of the Lagrangian in the adjoint space composed of weights, nodes state variables and Lagrange multipliers. This process is further enhanced by multiple layers of constraints that accelerate the diffusion process. An experimental analysis shows that the proposed approach compares favourably with popular models on several benchmarks.
Digraph of Senegal s local languages: issues, challenges and prospects of their transliteration
Authors: Elhadji Mamadou Nguer, Diop Sokhna Bao, Yacoub Ahmed Fall, Mouhamadou Khoule
Date: 2020-05-05 00:00:00
Tasks: Translation, Transliteration

The local languages in Senegal, like those of West African countries in general, are written based on two alphabets: supplemented Arabic alphabet (called Ajami) and Latin alphabet. Each writing has its own applications. Ajami writing is generally used by people educated in Koranic schools for communication, business, literature (religious texts, poetry, etc.), traditional religious medicine, etc. Writing with Latin characters is used for localization of ICT (Web, dictionaries, Windows and Google tools translated in Wolof, etc.), the translation of legal texts (commercial code and constitution translated in Wolof) and religious ones (Quran and Bible in Wolof), book edition, etc. To facilitate both populations general access to knowledge, it is useful to set up transliteration tools between these two scriptures. This work falls within the framework of the implementation of project for a collaborative online dictionary Wolof (Nguer E. M., Khoule M, Thiam M. N., Mbaye B. T., Thiare O., Cisse M. T., Mangeot M. 2014), which will involve people using Ajami writing. Our goal will consist, on the one hand in raising the issues related to the transliteration and the challenges that this will raise, and on the other one, presenting the perspectives.