pwc-archive/papers-with-abstracts huggingface dataset:
Classifying Constructive Comments
Authors: Varada Kolhatkar, Nithum Thain, Jeffrey Sorensen, Lucas Dixon, Maite Taboada
Date: 2020-04-11 00:00:00
Tasks: Domain Adaptation

We introduce the Constructive Comments Corpus (C3), comprised of 12,000 annotated news comments, intended to help build new tools for online communities to improve the quality of their discussions. We define constructive comments as high-quality comments that make a contribution to the conversation. We explain the crowd worker annotation scheme and define a taxonomy of sub-characteristics of constructiveness. The quality of the annotation scheme and the resulting dataset is evaluated using measurements of inter-annotator agreement, expert assessment of a sample, and by the constructiveness sub-characteristics, which we show provide a proxy for the general constructiveness concept. We provide models for constructiveness trained on C3 using both feature-based and a variety of deep learning approaches and demonstrate that these models capture general rather than topic- or domain-specific characteristics of constructiveness, through domain adaptation experiments. We examine the role that length plays in our models, as comment length could be easily gamed if models depend heavily upon this feature. By examining the errors made by each model and their distribution by length, we show that the best performing models are less correlated with comment length.The constructiveness corpus and our experiments pave the way for a moderation tool focused on promoting comments that make a contribution, rather than only filtering out undesirable content.
Robot self/other distinction: active inference meets neural networks learning in a mirror
Authors: Pablo Lanillos, Jordi Pages, Gordon Cheng
Date: 2020-04-11 00:00:00

Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware. However, only a selected group of animals, mainly high order mammals such as humans, has passed the mirror test, a behavioural experiment proposed to assess self-recognition abilities. In this paper, we describe self-recognition as a process that is built on top of body perception unconscious mechanisms. We present an algorithm that enables a robot to perform non-appearance self-recognition on a mirror and distinguish its simple actions from other entities, by answering the following question: am I generating these sensations? The algorithm combines active inference, a theoretical model of perception and action in the brain, with neural network learning. The robot learns the relation between its actions and its body with the effect produced in the visual field and its body sensors. The prediction error generated between the models and the real observations during the interaction is used to infer the body configuration through free energy minimization and to accumulate evidence for recognizing its body. Experimental results on a humanoid robot show the reliability of the algorithm for different initial conditions, such as mirror recognition in any perspective, robot-robot distinction and human-robot differentiation.
Autoencoding Generative Adversarial Networks
Authors: Conor Lazarou
Date: 2020-04-11 00:00:00
Tasks: Generative Adversarial Network

In the years since Goodfellow et al. introduced Generative Adversarial Networks (GANs), there has been an explosion in the breadth and quality of generative model applications. Despite this work, GANs still have a long way to go before they see mainstream adoption, owing largely to their infamous training instability. Here I propose the Autoencoding Generative Adversarial Network (AEGAN), a four-network model which learns a bijective mapping between a specified latent space and a given sample space by applying an adversarial loss and a reconstruction loss to both the generated images and the generated latent vectors. The AEGAN technique offers several improvements to typical GAN training, including training stabilization, mode-collapse prevention, and permitting the direct interpolation between real samples. The effectiveness of the technique is illustrated using an anime face dataset.
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
Authors: Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
Date: 2020-04-11 00:00:00
Tasks: Segmentation

Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, fertilization, green-house gases (GHG's), etc. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events - a growing trend related to climate change. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data. In this paper, we break down this task using satellite imaging into two approaches: 1) Segmentation of parcel boundaries, and 2) Segmentation of parcel areas. We implemented variations of UNets, one of which takes into account temporal information, which achieved the best results on our dataset on farmland parcels in France in 2017.
Robust Large-Margin Learning in Hyperbolic Space
Authors: Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar
Date: 2020-04-11 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Representation Learning

Recently, there has been a surge of interest in representation learning in hyperbolic spaces, driven by their ability to represent hierarchical data with significantly fewer dimensions than standard Euclidean spaces. However, the viability and benefits of hyperbolic spaces for downstream machine learning tasks have received less attention. In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space. Specifically, we consider the problem of learning a large-margin classifier for data possessing a hierarchical structure. We provide an algorithm to efficiently learn a large-margin hyperplane, relying on the careful injection of adversarial examples. Finally, we prove that for hierarchical data that embeds well into hyperbolic space, the low embedding dimension ensures superior guarantees when learning the classifier directly in hyperbolic space.
Depthwise Discrete Representation Learning
Authors: Iordanis Fostiropoulos
Date: 2020-04-11 00:00:00
Tasks: Quantization, Representation Learning

Recent advancements in learning Discrete Representations as opposed to continuous ones have led to state of art results in tasks that involve Language, Audio and Vision. Some latent factors such as words, phonemes and shapes are better represented by discrete latent variables as opposed to continuous. Vector Quantized Variational Autoencoders (VQVAE) have produced remarkable results in multiple domains. VQVAE learns a prior distribution $z_e$ along with its mapping to a discrete number of $K$ vectors (Vector Quantization). We propose applying VQ along the feature axis. We hypothesize that by doing so, we are learning a mapping between the codebook vectors and the marginal distribution of the prior feature space. Our approach leads to 33\% improvement as compared to prevous discrete models and has similar performance to state of the art auto-regressive models (e.g. PixelSNAIL). We evaluate our approach on a static prior using an artificial toy dataset (blobs). We further evaluate our approach on benchmarks for CIFAR-10 and ImageNet.
Deep learning-based topological optimization for representing a user-specified design area
Authors: Keigo Nakamura, Yoshiro Suzuki
Date: 2020-04-11 00:00:00
Tasks: Decoder

Presently, topology optimization requires multiple iterations to create an optimized structure for given conditions. Among the conditions for topology optimization,the design area is one of the most important for structural design. In this study, we propose a new deep learning model to generate an optimized structure for a given design domain and other boundary conditions without iteration. For this purpose, we used open-source topology optimization MATLAB code to generate a pair of optimized structures under various design conditions. The resolution of the optimized structure is 32 * 32 pixels, and the design conditions are design area, volume fraction, distribution of external forces, and load value. Our deep learning model is primarily composed of a convolutional neural network (CNN)-based encoder and decoder, trained with datasets generated with MATLAB code. In the encoder, we use batch normalization (BN) to increase the stability of the CNN model. In the decoder, we use SPADE (spatially adaptive denormalization) to reinforce the design area information. Comparing the performance of our proposed model with a CNN model that does not use BN and SPADE, values for mean absolute error (MAE), mean compliance error, and volume error with the optimized topology structure generated in MAT-LAB code were smaller, and the proposed model was able to represent the design area more precisely. The proposed method generates near-optimal structures reflecting the design area in less computational time, compared with the open-source topology optimization MATLAB code.
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
Authors: Yijiang Liu, Meishan Zhang, Donghong Ji
Date: 2020-04-11 00:00:00
Proceeding: COLING 2020 8
Tasks: Graph Attention

In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end joint model for the task, which exploits the state-of-the-art BERT representations as encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our joint model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model.
Bio-inspired Gait Imitation of Hexapod Robot Using Event-Based Vision Sensor and Spiking Neural Network
Authors: Justin Ting, Yan Fang, Ashwin Sanjay Lele, Arijit Raychowdhury
Date: 2020-04-11 00:00:00
Tasks: Event-based vision

Learning how to walk is a sophisticated neurological task for most animals. In order to walk, the brain must synthesize multiple cortices, neural circuits, and diverse sensory inputs. Some animals, like humans, imitate surrounding individuals to speed up their learning. When humans watch their peers, visual data is processed through a visual cortex in the brain. This complex problem of imitation-based learning forms associations between visual data and muscle actuation through Central Pattern Generation (CPG). Reproducing this imitation phenomenon on low power, energy-constrained robots that are learning to walk remains challenging and unexplored. We propose a bio-inspired feed-forward approach based on neuromorphic computing and event-based vision to address the gait imitation problem. The proposed method trains a "student" hexapod to walk by watching an "expert" hexapod moving its legs. The student processes the flow of Dynamic Vision Sensor (DVS) data with a one-layer Spiking Neural Network (SNN). The SNN of the student successfully imitates the expert within a small convergence time of ten iterations and exhibits energy efficiency at the sub-microjoule level.
Discriminative Learning via Adaptive Questioning
Authors: Achal Bassamboo, Vikas Deep, Sandeep Juneja, Assaf Zeevi
Date: 2020-04-11 00:00:00

We consider the problem of designing an adaptive sequence of questions that optimally classify a candidate's ability into one of several categories or discriminative grades. A candidate's ability is modeled as an unknown parameter, which, together with the difficulty of the question asked, determines the likelihood with which s/he is able to answer a question correctly. The learning algorithm is only able to observe these noisy responses to its queries. We consider this problem from a fixed confidence-based $\delta$-correct framework, that in our setting seeks to arrive at the correct ability discrimination at the fastest possible rate while guaranteeing that the probability of error is less than a pre-specified and small $\delta$. In this setting we develop lower bounds on any sequential questioning strategy and develop geometrical insights into the problem structure both from primal and dual formulation. In addition, we arrive at algorithms that essentially match these lower bounds. Our key conclusions are that, asymptotically, any candidate needs to be asked questions at most at two (candidate ability-specific) levels, although, in a reasonably general framework, questions need to be asked only at a single level. Further, and interestingly, the problem structure facilitates endogenous exploration, so there is no need for a separately designed exploration stage in the algorithm.
Meta-Learning in Neural Networks: A Survey
Authors: Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey
Date: 2020-04-11 00:00:00
Tasks: Few-Shot Learning, Hyperparameter Optimization, Meta-Learning, Multi-Task Learning, Reinforcement Learning, Survey, Transfer Learning

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction
Authors: Kevin Lybarger, Mari Ostendorf, Meliha Yetisgen
Date: 2020-04-11 00:00:00
Tasks: Active Learning, Decision Making, Event Extraction, text-classification, Text Classification

Social determinants of health (SDOH) affect health outcomes, and knowledge of SDOH can inform clinical decision-making. Automatically extracting SDOH information from clinical text requires data-driven information extraction models trained on annotated corpora that are heterogeneous and frequently include critical SDOH. This work presents a new corpus with SDOH annotations, a novel active learning framework, and the first extraction results on the new corpus. The Social History Annotation Corpus (SHAC) includes 4,480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events. We introduce a novel active learning framework that selects samples for annotation using a surrogate text classification task as a proxy for a more complex event extraction task. The active learning framework successfully increases the frequency of health risk factors and improves automatic extraction of these events over undirected annotation. An event extraction model trained on SHAC achieves high extraction performance for substance use status (0.82-0.93 F1), employment status (0.81-0.86 F1), and living status type (0.81-0.93 F1) on data from three institutions.
Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review
Authors: Muhammad Ilyas, Hina Rehman, Amine Nait-ali
Date: 2020-04-11 00:00:00

In 2019, the entire world is facing a situation of health emergency due to a newly emerged coronavirus (COVID-19). Almost 196 countries are affected by covid-19, while USA, Italy, China, Spain, Iran, and France have the maximum active cases of COVID-19. The issues, medical and healthcare departments are facing in delay of detecting the COVID-19. Several artificial intelligence based system are designed for the automatic detection of COVID-19 using chest x-rays. In this article we will discuss the different approaches used for the detection of COVID-19 and the challenges we are facing. It is mandatory to develop an automatic detection system to prevent the transfer of the virus through contact. Several deep learning architecture are deployed for the detection of COVID-19 such as ResNet, Inception, Googlenet etc. All these approaches are detecting the subjects suffering with pneumonia while its hard to decide whether the pneumonia is caused by COVID-19 or due to any other bacterial or fungal attack.
Underwater Image Enhancement Based on Structure-Texture Reconstruction
Authors: Sen Lin, Kaichen Chi
Date: 2020-04-11 00:00:00
Tasks: Image Enhancement

Aiming at the problems of color distortion, blur and excessive noise of underwater image, an underwater image enhancement algorithm based on structure-texture reconstruction is proposed. Firstly, the color equalization of the degraded image is realized by the automatic color enhancement algorithm; Secondly, the relative total variation is introduced to decompose the image into the structure layer and texture layer; Then, the best background light point is selected based on brightness, gradient discrimination, and hue judgment, the transmittance of the backscatter component is obtained by the red dark channel prior, which is substituted into the imaging model to remove the fogging phenomenon in the structure layer. Enhancement of effective details in the texture layer by multi scale detail enhancement algorithm and binary mask; Finally, the structure layer and texture layer are reconstructed to get the final image. The experimental results show that the algorithm can effectively balance the hue, saturation, and clarity of underwater image, and has good performance in different underwater environments.
Scaling Bayesian inference of mixed multinomial logit models to very large datasets
Authors: Filipe Rodrigues
Date: 2020-04-11 00:00:00
Tasks: Bayesian Inference, Computational Efficiency, GPU, Variational Inference

Variational inference methods have been shown to lead to significant improvements in the computational efficiency of approximate Bayesian inference in mixed multinomial logit models when compared to standard Markov-chain Monte Carlo (MCMC) methods without compromising accuracy. However, despite their demonstrated efficiency gains, existing methods still suffer from important limitations that prevent them to scale to very large datasets, while providing the flexibility to allow for rich prior distributions and to capture complex posterior distributions. In this paper, we propose an Amortized Variational Inference approach that leverages stochastic backpropagation, automatic differentiation and GPU-accelerated computation, for effectively scaling Bayesian inference in Mixed Multinomial Logit models to very large datasets. Moreover, we show how normalizing flows can be used to increase the flexibility of the variational posterior approximations. Through an extensive simulation study, we empirically show that the proposed approach is able to achieve computational speedups of multiple orders of magnitude over traditional MSLE and MCMC approaches for large datasets without compromising estimation accuracy.
The Role of Stem Noise in Visual Perception and Image Quality Measurement
Authors: Arash Ashtari
Date: 2020-04-11 00:00:00
Tasks: Image Quality Assessment, Image Reconstruction

This paper considers reference free quality assessment of distorted and noisy images. Specifically, it considers the first and second order statistics of stem noise that can be evaluated given any image. In the research field of Image quality Assessment (IQA), the stem noise is defined as the input of an Auto Regressive (AR) process, from which a low-energy and de-correlated version of the image can be recovered. To estimate the AR model parameters and associated stem noise energy, the Yule-walker equations are used such that the accompanying Auto Correlation Function (ACF) coefficients can be treated as model parameters for image reconstruction. To characterize systematic signal dependent and signal independent distortions, the mean and variance of stem noise can be evaluated over the image. Crucially, this paper shows that these statistics have a predictive validity in relation to human ratings of image quality. Furthermore, under certain kinds of image distortion, stem noise statistics show very significant correlations with established measures of image quality.
Optimal Learning for Sequential Decisions in Laboratory Experimentation
Authors: Kristopher Reyes, Warren B. Powell
Date: 2020-04-11 00:00:00

The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new advance reaches commercial production can span 20 years. This tutorial is aimed to provide experimental scientists with a foundation in the science of making decisions. Using numerical examples drawn from the experiences of the authors, the article describes the fundamental elements of any experimental learning problem. It emphasizes the important role of belief models, which include not only the best estimate of relationships provided by prior research, previous experiments and scientific expertise, but also the uncertainty in these relationships. We introduce the concept of a learning policy, and review the major categories of policies. We then introduce a policy, known as the knowledge gradient, that maximizes the value of information from each experiment. We bring out the importance of reducing uncertainty, and illustrate this process for different belief models.
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
Authors: Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri, Marco Grangetto
Date: 2020-04-11 00:00:00
Tasks: Small Data Image Classification, Transfer Learning

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.