pwc-archive/papers-with-abstracts huggingface dataset:
Human-Machine Collaboration for Fast Land Cover Mapping
Authors: Caleb Robinson, Anthony Ortiz, Kolya Malkin, Blake Elias, Andi Peng, Dan Morris, Bistra Dilkina, Nebojsa Jojic
Date: 2019-06-10 00:00:00
Tasks: Active Learning

We propose incorporating human labelers in a model fine-tuning system that provides immediate user feedback. In our framework, human labelers can interactively query model predictions on unlabeled data, choose which data to label, and see the resulting effect on the model's predictions. This bi-directional feedback loop allows humans to learn how the model responds to new data. Our hypothesis is that this rich feedback allows human labelers to create mental models that enable them to better choose which biases to introduce to the model. We compare human-selected points to points selected using standard active learning methods. We further investigate how the fine-tuning methodology impacts the human labelers' performance. We implement this framework for fine-tuning high-resolution land cover segmentation models. Specifically, we fine-tune a deep neural network -- trained to segment high-resolution aerial imagery into different land cover classes in Maryland, USA -- to a new spatial area in New York, USA. The tight loop turns the algorithm and the human operator into a hybrid system that can produce land cover maps of a large area much more efficiently than the traditional workflows. Our framework has applications in geospatial machine learning settings where there is a practically limitless supply of unlabeled data, of which only a small fraction can feasibly be labeled through human efforts.
Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
Authors: Mariusz Kubkowski, Jan Mielniczuk
Date: 2019-06-10 00:00:00
Tasks: regression

We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model is semiparametric and the response function is misspecified under parametric model fit. Selection for such a scenario aims at recovering the support of the minimizer of the associated risk with large probability. We propose a two-step selection procedure which consists of screening and ordering predictors by Lasso method and then selecting a subset of predictors which minimizes Generalized Information Criterion on the corresponding nested family of models. We prove consistency of the selection method under conditions which allow for much larger number of predictors than number of observations. For the semiparametric case when distribution of random predictors satisfies linear regression conditions the true and the estimated parameters are collinear and their common support can be consistently identified.
Differentiable Surface Splatting for Point-based Geometry Processing
Authors: Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, Olga Sorkine-Hornung
Date: 2019-06-10 00:00:00
Tasks: Denoising, Inverse Rendering

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to handle discontinuities of the rendering function. Regularization terms are introduced to ensure uniform distribution of the points on the underlying surface. We demonstrate applications of DSS to inverse rendering for geometry synthesis and denoising, where large scale topological changes, as well as small scale detail modifications, are accurately and robustly handled without requiring explicit connectivity, outperforming state-of-the-art techniques. The data and code are at https://github.com/yifita/DSS.
Introducing the Hearthstone-AI Competition
Authors: Alexander Dockhorn, Sanaz Mostaghim
Date: 2019-05-06 00:00:00
Tasks: Card Games

The Hearthstone AI framework and competition motivates the development of artificial intelligence agents that can play collectible card games. A special feature of those games is the high variety of cards, which can be chosen by the players to create their own decks. In contrast to simpler card games, the value of many cards is determined by their possible synergies. The vast amount of possible decks, the randomness of the game, as well as the restricted information during the player's turn offer quite a hard challenge for the development of game-playing agents. This short paper introduces the competition framework and goes into more detail on the problems and challenges that need to be faced during the development process.
Pykg2vec: A Python Library for Knowledge Graph Embedding
Authors: Shih Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque
Date: 2019-06-04 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Representation Learning

Python library for knowledge graph embedding and representation learning.
Interpreting OWL Complex Classes in AutomationML based on Bidirectional Translation
Authors: Yingbing Hua, Björn Hein
Date: 2019-06-04 00:00:00
Tasks: Code Generation, Translation

The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering tools stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by engineers such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from/to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for nonexperts to visualize, modify, and create OWL complex classes.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
Authors: Aryan Mobiny, Hien V. Nguyen, Supratik Moulik, Naveen Garg, Carol C. Wu
Date: 2019-06-07 00:00:00
Tasks: Autonomous Driving, Bayesian Inference, Medical Diagnosis, Semantic Segmentation

Deep neural networks (DNNs) have achieved state-of-the-art performances in many important domains, including medical diagnosis, security, and autonomous driving. In these domains where safety is highly critical, an erroneous decision can result in serious consequences. While a perfect prediction accuracy is not always achievable, recent work on Bayesian deep networks shows that it is possible to know when DNNs are more likely to make mistakes. Knowing what DNNs do not know is desirable to increase the safety of deep learning technology in sensitive applications. Bayesian neural networks attempt to address this challenge. However, traditional approaches are computationally intractable and do not scale well to large, complex neural network architectures. In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights. This method, called MC-DropConnect, gives us a tool to represent the model uncertainty with little change in the overall model structure or computational cost. We extensively validate the proposed algorithm on multiple network architectures and datasets for classification and semantic segmentation tasks. We also propose new metrics to quantify the uncertainty estimates. This enables an objective comparison between MC-DropConnect and prior approaches. Our empirical results demonstrate that the proposed framework yields significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
Authors: Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Michael Rabbat
Date: 2019-06-09 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Deep Reinforcement Learning, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning by stabilizing learning and allowing for higher training throughputs. We propose Gossip-based Actor-Learner Architectures (GALA) where several actor-learners (such as A2C agents) are organized in a peer-to-peer communication topology, and exchange information through asynchronous gossip in order to take advantage of a large number of distributed simulators. We prove that GALA agents remain within an epsilon-ball of one-another during training when using loosely coupled asynchronous communication. By reducing the amount of synchronization between agents, GALA is more computationally efficient and scalable compared to A2C, its fully-synchronous counterpart. GALA also outperforms A2C, being more robust and sample efficient. We show that we can run several loosely coupled GALA agents in parallel on a single GPU and achieve significantly higher hardware utilization and frame-rates than vanilla A2C at comparable power draws.
Evaluating aleatoric and epistemic uncertainties of time series deep learning models for soil moisture predictions
Authors: Kuai Fang, Chaopeng Shen, Daniel Kifer
Date: 2019-06-10 00:00:00
Tasks: Time Series, Time Series Analysis

Soil moisture is an important variable that determines floods, vegetation health, agriculture productivity, and land surface feedbacks to the atmosphere, etc. Accurately modeling soil moisture has important implications in both weather and climate models. The recently available satellite-based observations give us a unique opportunity to build data-driven models to predict soil moisture instead of using land surface models, but previously there was no uncertainty estimate. We tested Monte Carlo dropout (MCD) with an aleatoric term for our long short-term memory models for this problem, and asked if the uncertainty terms behave as they were argued to. We show that the method successfully captures the predictive error after tuning a hyperparameter on a representative training dataset. We show the MCD uncertainty estimate, as previously argued, does detect dissimilarity.
Write, Execute, Assess: Program Synthesis with a REPL
Authors: Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, Armando Solar-Lezama
Date: 2019-06-09 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Navigate, Program Synthesis

We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search process with an interpreter or a read-eval-print-loop (REPL), which immediately executes partially written programs, exposing their semantics. The REPL addresses a basic challenge of program synthesis: tiny changes in syntax can lead to huge changes in semantics. We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far. At test time we can combine these models with a Sequential Monte Carlo algorithm. We apply our approach to two domains: synthesizing text editing programs and inferring 2D and 3D graphics programs.
FAKTA: An Automatic End-to-End Fact Checking System
Authors: Moin Nadeem, Wei Fang, Brian Xu, Mitra Mohtarami, James Glass
Date: 2019-06-07 00:00:00
Proceeding: NAACL 2019 6
Tasks: Fact Checking, Retrieval, Sentence, Stance Detection

We present FAKTA which is a unified framework that integrates various components of a fact checking process: document retrieval from media sources with various types of reliability, stance detection of documents with respect to given claims, evidence extraction, and linguistic analysis. FAKTA predicts the factuality of given claims and provides evidence at the document and sentence level to explain its predictions
Leveraging BERT for Extractive Text Summarization on Lectures
Authors: Derek Miller
Date: 2019-06-07 00:00:00
Tasks: Clustering, Extractive Summarization, Extractive Text Summarization, Management, Text Summarization

In the last two decades, automatic extractive text summarization on lectures has demonstrated to be a useful tool for collecting key phrases and sentences that best represent the content. However, many current approaches utilize dated approaches, producing sub-par outputs or requiring several hours of manual tuning to produce meaningful results. Recently, new machine learning architectures have provided mechanisms for extractive summarization through the clustering of output embeddings from deep learning models. This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid for summary selection. The purpose of the service was to provide students a utility that could summarize lecture content, based on their desired number of sentences. On top of the summary work, the service also includes lecture and summary management, storing content on the cloud which can be used for collaboration. While the results of utilizing BERT for extractive summarization were promising, there were still areas where the model struggled, providing feature research opportunities for further improvement.
Analyzing the Structure of Attention in a Transformer Language Model
Authors: Jesse Vig, Yonatan Belinkov
Date: 2019-06-07 00:00:00
Proceeding: WS 2019 8
Tasks: Language Modeling, Language Modelling

The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP tasks. In this paper, we analyze the structure of attention in a Transformer language model, the GPT-2 small pretrained model. We visualize attention for individual instances and analyze the interaction between attention and syntax over a large corpus. We find that attention targets different parts of speech at different layer depths within the model, and that attention aligns with dependency relations most strongly in the middle layers. We also find that the deepest layers of the model capture the most distant relationships. Finally, we extract exemplar sentences that reveal highly specific patterns targeted by particular attention heads.
Towards Run Time Estimation of the Gaussian Chemistry Code for SEAGrid Science Gateway
Authors: Angel Beltre, Shehtab Zaman, Kenneth Chiu, Sudhakar Pamidighantam, Xingye Qiao, Madhusudhan Govindaraju
Date: 2019-06-07 00:00:00
Tasks: Computational chemistry

Accurate estimation of the run time of computational codes has a number of significant advantages for scientific computing. It is required information for optimal resource allocation, improving turnaround times and utilization of science gateways. Furthermore, it allows users to better plan and schedule their research, streamlining workflows and improving the overall productivity of cyberinfrastructure. Predicting run time is challenging, however. The inputs to scientific codes can be complex and high dimensional. Their relationship to the run time may be highly non-linear, and, in the most general case is completely arbitrary and thus unpredictable (i.e., simply a random mapping from inputs to run time). Most codes are not so arbitrary, however, and there has been significant prior research on predicting the run time of applications and workloads. Such predictions are generally application-specific, however. In this paper, we focus on the Gaussian computational chemistry code. We characterize a data set of runs from the SEAGrid science gateway with a number of different studies. We also explore a number of different potential regression methods and present promising future directions.
Spring-Electrical Models For Link Prediction
Authors: Yana Kashinskaya, Egor Samosvat, Akmal Artikov
Date: 2019-05-24 00:00:00
Tasks: Link Prediction, Prediction

We propose a link prediction algorithm that is based on spring-electrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g., connected and/or belonging to one cluster, tend to be visualized close to each other. Therefore, we assumed that the Euclidean distance between nodes in the obtained network layout correlates with a probability of a link between them. We evaluate the proposed method against several popular baselines and demonstrate its flexibility by applying it to undirected, directed and bipartite networks.
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains
Authors: Matthieu Zimmer, Paul Weng
Date: 2019-06-10 00:00:00

In the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic (NFAC). This approach is based on a policy update different from that of deterministic policy gradient (DPG). Previous work has observed its excellent performance empirically, but a theoretical justification is lacking. To fill this gap, we provide a theoretical explanation to motivate this unorthodox policy update by relating it to another update and making explicit the objective function of the latter. We furthermore discuss in depth the properties of these updates to get a deeper understanding of the overall approach. In addition, we extend it and propose a new trust region algorithm, Penalized NFAC (PeNFAC). Finally, we experimentally demonstrate in several classic control problems that it surpasses the state-of-the-art algorithms to learn deterministic policies.
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Authors: Hao Peng, Jian-Xin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, Philip S. Yu
Date: 2019-06-09 00:00:00
Tasks: Clustering, Event Detection

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques.
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Authors: Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, huan zhang, Ilya Razenshteyn, Sebastien Bubeck
Date: 2019-06-09 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Adversarial Attack, Adversarial Defense, Deep Learning

Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to $\ell_2$-norm adversarial perturbations. In this paper, we employ adversarial training to improve the performance of randomized smoothing. We design an adapted attack for smoothed classifiers, and we show how this attack can be used in an adversarial training setting to boost the provable robustness of smoothed classifiers. We demonstrate through extensive experimentation that our method consistently outperforms all existing provably $\ell_2$-robust classifiers by a significant margin on ImageNet and CIFAR-10, establishing the state-of-the-art for provable $\ell_2$-defenses. Moreover, we find that pre-training and semi-supervised learning boost adversarially trained smoothed classifiers even further. Our code and trained models are available at http://github.com/Hadisalman/smoothing-adversarial .
Proposition d'une nouvelle approche d'extraction des motifs fermés fréquents
Authors: Ons Khemiri
Date: 2019-06-09 00:00:00

This work is done as part of a master's thesis project. The increase in the volume of data has given rise to various issues related to the collection, storage, analysis and exploitation of these data in order to create an added value. In this master, we are interested in the search of frequent closed patterns in the transaction bases. One way to process data is to partition the search space into subcontexts, and then explore the subcontexts simultaneously. In this context, we have proposed a new approach for extracting frequent closed itemsets. The main idea is to update frequent closed patterns with their minimal generators by applying a strategy of partitioning of the initial extraction context. Our new approach called UFCIGs-DAC was designed and implemented to perform a search in the test bases. The main originality of this approach is the simultaneous exploration of the research space by the update of the frequent closed patterns and the minimal generators. Moreover, our approach can be adapted to any algorithm of extraction of the frequent closed patterns with their minimal generators.
Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
Authors: Chen Qi, Yuxiu Hua, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
Date: 2019-06-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$-learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the varying demands and the allocated bandwidth as the environment state and the action, respectively. However, allocating bandwidth in a finer resolution usually implies larger action space, and unfortunately DQL fails to quickly converge in this case. In this paper, we introduce discrete normalized advantage functions (DNAF) into DQL, by separating the $\mathcal{Q}$-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of $\mathcal{Q}$-value for every state-action pair. Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output. Finally, we verify the faster convergence of the DNAF-based DQL through extensive simulations.