pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Test Collection Construction via Active Learning
Authors: Md Mustafizur Rahman, Mucahid Kutlu, Tamer Elsayed, Matthew Lease
Date: 2018-01-17 00:00:00
Tasks: Active Learning

To create a new IR test collection at low cost, it is valuable to carefully select which documents merit human relevance judgments. Shared task campaigns such as NIST TREC pool document rankings from many participating systems (and often interactive runs as well) in order to identify the most likely relevant documents for human judging. However, if one's primary goal is merely to build a test collection, it would be useful to be able to do so without needing to run an entire shared task. Toward this end, we investigate multiple active learning strategies which, without reliance on system rankings: 1) select which documents human assessors should judge; and 2) automatically classify the relevance of additional unjudged documents. To assess our approach, we report experiments on five TREC collections with varying scarcity of relevant documents. We report labeling accuracy achieved, as well as rank correlation when evaluating participant systems based upon these labels vs.\ full pool judgments. Results show the effectiveness of our approach, and we further analyze how varying relevance scarcity across collections impacts our findings. To support reproducibility and follow-on work, we have shared our code online: https://github.com/mdmustafizurrahman/ICTIR_AL_TestCollection_2020/.
audioLIME: Listenable Explanations Using Source Separation
Authors: Verena Haunschmid, Ethan Manilow, Gerhard Widmer
Date: 2020-08-02 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Music Tagging, Retrieval

Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks but their predictions are usually not interpretable. We propose audioLIME, a method based on Local Interpretable Model-agnostic Explanations (LIME) extended by a musical definition of locality. The perturbations used in LIME are created by switching on/off components extracted by source separation which makes our explanations listenable. We validate audioLIME on two different music tagging systems and show that it produces sensible explanations in situations where a competing method cannot.
On Frink's type metrization of weighted graphs
Authors: María Florencia Acosta, Hugo Aimar, Ivana Gómez
Date: 2020-08-02 00:00:00
Tasks: Vocal Bursts Type Prediction

Using the technique of the metrization theorem of uniformities with countable bases, in this note we provide, test and compare an explicit algorithm to produce a metric $d(x,y)$ between the vertices $x$ and $y$ of an affinity weighted undirected graph.
Geometrically Enriched Latent Spaces
Authors: Georgios Arvanitidis, Søren Hauberg, Bernhard Schölkopf
Date: 2020-08-02 00:00:00

A common assumption in generative models is that the generator immerses the latent space into a Euclidean ambient space. Instead, we consider the ambient space to be a Riemannian manifold, which allows for encoding domain knowledge through the associated Riemannian metric. Shortest paths can then be defined accordingly in the latent space to both follow the learned manifold and respect the ambient geometry. Through careful design of the ambient metric we can ensure that shortest paths are well-behaved even for deterministic generators that otherwise would exhibit a misleading bias. Experimentally we show that our approach improves interpretability of learned representations both using stochastic and deterministic generators.
SemEval-2020 Task 5: Counterfactual Recognition
Authors: Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin, Xiaodan Zhu
Date: 2020-08-02 00:00:00
Proceeding: SEMEVAL 2020
Tasks: counterfactual, Counterfactual Detection, Counterfactual Inference, Sentence

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequents with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. The data, baseline code, and leaderboard can be found at https://competitions.codalab.org/competitions/21691. The data and baseline code are also available at https://zenodo.org/record/3932442.
Semi-supervised deep learning based on label propagation in a 2D embedded space
Authors: Barbara Caroline Benato, Jancarlo Ferreira Gomes, Alexandru Cristian Telea, Alexandre Xavier Falcão
Date: 2020-08-02 00:00:00

While convolutional neural networks need large labeled sets for training images, expert human supervision of such datasets can be very laborious. Proposed solutions propagate labels from a small set of supervised images to a large set of unsupervised ones to obtain sufficient truly-and-artificially labeled samples to train a deep neural network model. Yet, such solutions need many supervised images for validation. We present a loop in which a deep neural network (VGG-16) is trained from a set with more correctly labeled samples along iterations, created by using t-SNE to project the features of its last max-pooling layer into a 2D embedded space in which labels are propagated using the Optimum-Path Forest semi-supervised classifier. As the labeled set improves along iterations, it improves the features of the neural network. We show that this can significantly improve classification results on test data (using only 1\% to 5\% of supervised samples) of three private challenging datasets and two public ones.
A Foliated View of Transfer Learning
Authors: Janith Petangoda, Nick A. M. Monk, Marc Peter Deisenroth
Date: 2020-08-02 00:00:00
Tasks: Transfer Learning

Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied experimentally, there lacks a foundational description of the transfer learning problem that exposes what related tasks are, and how they can be exploited. In this work, we present a definition for relatedness between tasks and identify foliations as a mathematical framework to represent such relationships.
Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages
Authors: Badr M. Abdullah, Tania Avgustinova, Bernd Möbius, Dietrich Klakow
Date: 2020-08-02 00:00:00
Tasks: Diversity, Domain Adaptation, Language Identification, Spoken language identification, Unsupervised Domain Adaptation

State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.
Video Question Answering on Screencast Tutorials
Authors: Wentian Zhao, Seokhwan Kim, Ning Xu, Hailin Jin
Date: 2020-08-02 00:00:00
Tasks: Question Answering, Video Question Answering

This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software. Unlike other video question answering works, all the answers in our dataset are grounded to the domain knowledge base. An one-shot recognition algorithm is designed to extract the visual cues, which helps enhance the performance of video question answering. We also propose several baseline neural network architectures based on various aspects of video contexts from the dataset. The experimental results demonstrate that our proposed models significantly improve the question answering performances by incorporating multi-modal contexts and domain knowledge.
Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification
Authors: Yu Shen, Sijie Zhu, Chen Chen, Qian Du, Liang Xiao, Jianyu Chen, Delu Pan
Date: 2020-08-02 00:00:00
Tasks: Deep Learning, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

Deep learning based methods, such as Convolution Neural Network (CNN), have demonstrated their efficiency in hyperspectral image (HSI) classification. These methods can automatically learn spectral-spatial discriminative features within local patches. However, for each pixel in an HSI, it is not only related to its nearby pixels but also has connections to pixels far away from itself. Therefore, to incorporate the long-range contextual information, a deep fully convolutional network (FCN) with an efficient non-local module, named ENL-FCN, is proposed for HSI classification. In the proposed framework, a deep FCN considers an entire HSI as input and extracts spectral-spatial information in a local receptive field. The efficient non-local module is embedded in the network as a learning unit to capture the long-range contextual information. Different from the traditional non-local neural networks, the long-range contextual information is extracted in a specially designed criss-cross path for computation efficiency. Furthermore, by using a recurrent operation, each pixel's response is aggregated from all pixels of HSI. The benefits of our proposed ENL-FCN are threefold: 1) the long-range contextual information is incorporated effectively, 2) the efficient module can be freely embedded in a deep neural network in a plug-and-play fashion, and 3) it has much fewer learning parameters and requires less computational resources. The experiments conducted on three popular HSI datasets demonstrate that the proposed method achieves state-of-the-art classification performance with lower computational cost in comparison with several leading deep neural networks for HSI.
An Investigation in Optimal Encoding of Protein Primary Sequence for Structure Prediction by Artificial Neural Networks
Authors: Aaron Hein, Casey Cole, Homayoun Valafar
Date: 2020-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Protein Structure Prediction

Machine learning and the use of neural networks has increased precipitously over the past few years primarily due to the ever-increasing accessibility to data and the growth of computation power. It has become increasingly easy to harness the power of machine learning for predictive tasks. Protein structure prediction is one area where neural networks are becoming increasingly popular and successful. Although very powerful, the use of ANN require selection of most appropriate input/output encoding, architecture, and class to produce the optimal results. In this investigation we have explored and evaluated the effect of several conventional and newly proposed input encodings and selected an optimal architecture. We considered 11 variations of input encoding, 11 alternative window sizes, and 7 different architectures. In total, we evaluated 2,541 permutations in application to the training and testing of more than 10,000 protein structures over the course of 3 months. Our investigations concluded that one-hot encoding, the use of LSTMs, and window sizes of 9, 11, and 15 produce the optimal outcome. Through this optimization, we were able to improve the quality of protein structure prediction by predicting the {\phi} dihedrals to within 14{\deg} - 16{\deg} and {\psi} dihedrals to within 23{\deg}- 25{\deg}. This is a notable improvement compared to previously similar investigations.
Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters
Authors: Yujie He, Changhong Fu, Fuling Lin, Yiming Li, Peng Lu
Date: 2020-08-02 00:00:00
Tasks: Object Tracking, Visual Tracking

Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years. However, existing algorithms still face difficulties such as partial occlusion, clutter background, and other challenging visual factors. Inspired by the cutting-edge attention mechanisms, a novel object tracking framework is proposed to leverage multi-level visual attention. Three primary attention, i.e., contextual attention, dimensional attention, and spatiotemporal attention, are integrated into the training and detection stages of correlation filter-based tracking pipeline. Therefore, the proposed tracker is equipped with robust discriminative power against challenging factors while maintaining high operational efficiency in UAV scenarios. Quantitative and qualitative experiments on two well-known benchmarks with 173 challenging UAV video sequences demonstrate the effectiveness of the proposed framework. The proposed tracking algorithm favorably outperforms 12 state-of-the-art methods, yielding 4.8% relative gain in UAVDT and 8.2% relative gain in UAV123@10fps against the baseline tracker while operating at the speed of $\sim$ 28 frames per second.
Interactive Imitation Learning in State-Space
Authors: Snehal Jauhri, Carlos Celemin, Jens Kober
Date: 2020-08-02 00:00:00
Tasks: Imitation Learning

Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing feedback while the agent executes its task. In this work, we propose a novel Interactive Learning technique that uses human feedback in state-space to train and improve agent behavior (as opposed to alternative methods that use feedback in action-space). Our method titled Teaching Imitative Policies in State-space~(TIPS) enables providing guidance to the agent in terms of `changing its state' which is often more intuitive for a human demonstrator. Through continuous improvement via corrective feedback, agents trained by non-expert demonstrators using TIPS outperformed the demonstrator and conventional Imitation Learning agents.
Bayesian Inference of Minimally Complex Models with Interactions of Arbitrary Order
Authors: Clélia de Mulatier, Matteo Marsili
Date: 2020-08-02 00:00:00
Tasks: Bayesian Inference, Model Selection

Finding the model that best describes a high-dimensional dataset is a daunting task, even more so if one aims to consider all possible high-order patterns of the data, going beyond pairwise models. For binary data, we show that this task becomes feasible when restricting the search to a family of simple models, that we call Minimally Complex Models (MCMs). MCMs are maximum entropy models that have interactions of arbitrarily high order grouped into independent components of minimal complexity. They are simple in information-theoretic terms, which means they can only fit well certain types of data patterns and are therefore easy to falsify. We show that Bayesian model selection restricted to these models is computationally feasible and has many advantages. First, the model evidence, which balances goodness-of-fit against complexity, can be computed efficiently without any parameter fitting, enabling very fast explorations of the space of MCMs. Second, the family of MCMs is invariant under gauge transformations, which can be used to develop a representation-independent approach to statistical modeling. For small systems (up to 15 variables), combining these two results allows us to select the best MCM among all, even though the number of models is already extremely large. For larger systems, we propose simple heuristics to find optimal MCMs in reasonable times. Besides, inference and sampling can be performed without any computational effort. Finally, because MCMs have interactions of any order, they can reveal the presence of important high-order dependencies in the data, providing a new approach to explore high-order dependencies in complex systems. We apply our method to synthetic data and real-world examples, illustrating how MCMs portray the structure of dependencies among variables in a simple manner, extracting falsifiable predictions on symmetries and invariance from the data.
Integrated monitoring of ice in selected Swiss lakes. Final project report
Authors: Manu Tom, Melanie Suetterlin, Damien Bouffard, Mathias Rothermel, Stefan Wunderle, Emmanuel Baltsavias
Date: 2020-08-02 00:00:00

Various lake observables, including lake ice, are related to climate and climate change and provide a good opportunity for long-term monitoring. Lakes (and as part of them lake ice) is therefore considered an Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). Following the need for an integrated multi-temporal monitoring of lake ice in Switzerland, MeteoSwiss in the framework of GCOS Switzerland supported this 2-year project to explore not only the use of satellite images but also the possibilities of Webcams and in-situ measurements. The aim of this project is to monitor some target lakes and detect the extent of ice and especially the ice-on/off dates, with focus on the integration of various input data and processing methods. The target lakes are: St. Moritz, Silvaplana, Sils, Sihl, Greifen and Aegeri, whereby only the first four were mainly frozen during the observation period and thus processed. The observation period was mainly the winter 2016-17. During the project, various approaches were developed, implemented, tested and compared. Firstly, low spatial resolution (250 - 1000 m) but high temporal resolution (1 day) satellite images from the optical sensors MODIS and VIIRS were used. Secondly, and as a pilot project, the use of existing public Webcams was investigated for (a) validation of results from satellite data, and (b) independent estimation of lake ice, especially for small lakes like St. Moritz, that could not be possibly monitored in the satellite images. Thirdly, in-situ measurements were made in order to characterize the development of the temperature profiles and partly pressure before freezing and under the ice-cover until melting. This report presents the results of the project work.
Curriculum Learning with a Progression Function
Authors: Andrea Bassich, Francesco Foglino, Matteo Leonetti, Daniel Kudenko
Date: 2020-08-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Curriculum Learning for Reinforcement Learning is an increasingly popular technique that involves training an agent on a sequence of intermediate tasks, called a Curriculum, to increase the agent's performance and learning speed. This paper introduces a novel paradigm for curriculum generation based on progression and mapping functions. While progression functions specify the complexity of the environment at any given time, mapping functions generate environments of a specific complexity. Different progression functions are introduced, including an autonomous online task progression based on the agent's performance. Our approach's benefits and wide applicability are shown by empirically comparing its performance to two state-of-the-art Curriculum Learning algorithms on six domains.
Differentiable Feature Aggregation Search for Knowledge Distillation
Authors: Yushuo Guan, Pengyu Zhao, Bingxuan Wang, Yuanxing Zhang, Cong Yao, Kaigui Bian, Jian Tang
Date: 2020-08-02 00:00:00
Proceeding: ECCV 2020 8
Tasks: Knowledge Distillation, Model Compression, Neural Architecture Search

Knowledge distillation has become increasingly important in model compression. It boosts the performance of a miniaturized student network with the supervision of the output distribution and feature maps from a sophisticated teacher network. Some recent works introduce multi-teacher distillation to provide more supervision to the student network. However, the effectiveness of multi-teacher distillation methods are accompanied by costly computation resources. To tackle with both the efficiency and the effectiveness of knowledge distillation, we introduce the feature aggregation to imitate the multi-teacher distillation in the single-teacher distillation framework by extracting informative supervision from multiple teacher feature maps. Specifically, we introduce DFA, a two-stage Differentiable Feature Aggregation search method that motivated by DARTS in neural architecture search, to efficiently find the aggregations. In the first stage, DFA formulates the searching problem as a bi-level optimization and leverages a novel bridge loss, which consists of a student-to-teacher path and a teacher-to-student path, to find appropriate feature aggregations. The two paths act as two players against each other, trying to optimize the unified architecture parameters to the opposite directions while guaranteeing both expressivity and learnability of the feature aggregation simultaneously. In the second stage, DFA performs knowledge distillation with the derived feature aggregation. Experimental results show that DFA outperforms existing methods on CIFAR-100 and CINIC-10 datasets under various teacher-student settings, verifying the effectiveness and robustness of the design.
Variational Filtering with Copula Models for SLAM
Authors: John D. Martin, Kevin Doherty, Caralyn Cyr, Brendan Englot, John Leonard
Date: 2020-08-02 00:00:00
Tasks: Simultaneous Localization and Mapping

The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots. In most cases the shared dependency between these variables is modeled through a multivariate Gaussian distribution, but there are many situations where that assumption is unrealistic. Our paper shows how it is possible to relax this assumption and perform simultaneous localization and mapping (SLAM) with a larger class of distributions, whose multivariate dependency is represented with a copula model. We integrate the distribution model with copulas into a Sequential Monte Carlo estimator and show how unknown model parameters can be learned through gradient-based optimization. We demonstrate our approach is effective in settings where Gaussian assumptions are clearly violated, such as environments with uncertain data association and nonlinear transition models.