pwc-archive/papers-with-abstracts huggingface dataset:
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Authors: Jiancheng Yang, Jiajun Chen, Kaiming Kuang, Tiancheng Lin, Junjun He, Bingbing Ni
Date: 2020-10-08 00:00:00
Tasks: Deep Learning, Prognosis, Text-To-Speech Synthesis, Time Series, Time Series Analysis, Time Series Classification

Predicting clinical outcome is remarkably important but challenging. Research efforts have been paid on seeking significant biomarkers associated with the therapy response or/and patient survival. However, these biomarkers are generally costly and invasive, and possibly dissatifactory for novel therapy. On the other hand, multi-modal, heterogeneous, unaligned temporal data is continuously generated in clinical practice. This paper aims at a unified deep learning approach to predict patient prognosis and therapy response, with easily accessible data, e.g., radiographics, laboratory and clinical information. Prior arts focus on modeling single data modality, or ignore the temporal changes. Importantly, the clinical time series is asynchronous in practice, i.e., recorded with irregular intervals. In this study, we formalize the prognosis modeling as a multi-modal asynchronous time series classification task, and propose a MIA-Prognosis framework with Measurement, Intervention and Assessment (MIA) information to predict therapy response, where a Simple Temporal Attention (SimTA) module is developed to process the asynchronous time series. Experiments on synthetic dataset validate the superiory of SimTA over standard RNN-based approaches. Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy. The proposed method achieves promising performance on predicting the immunotherapy response. Notably, our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition
Authors: Bagus Tris Atmaja, Masato Akagi
Date: 2020-04-06 00:00:00
Tasks: Deep Learning, Emotion Recognition, Speech Emotion Recognition

Modern deep learning architectures are ordinarily performed on high-performance computing facilities due to the large size of the input features and complexity of its model. This paper proposes traditional multilayer perceptrons (MLP) with deep layers and small input size to tackle that computation requirement limitation. The result shows that our proposed deep MLP outperformed modern deep learning architectures, i.e., LSTM and CNN, on the same number of layers and value of parameters. The deep MLP exhibited the highest performance on both speaker-dependent and speaker-independent scenarios on IEMOCAP and MSP-IMPROV corpus.
Simplifying the explanation of deep neural networks with sufficient and necessary feature-sets: case of text classification
Authors: Jiechieu Kameni Florentin Flambeau, Tsopze Norbert
Date: 2020-10-08 00:00:00
Tasks: text-classification, Text Classification

During the last decade, deep neural networks (DNN) have demonstrated impressive performances solving a wide range of problems in various domains such as medicine, finance, law, etc. Despite their great performances, they have long been considered as black-box systems, providing good results without being able to explain them. However, the inability to explain a system decision presents a serious risk in critical domains such as medicine where people's lives are at stake. Several works have been done to uncover the inner reasoning of deep neural networks. Saliency methods explain model decisions by assigning weights to input features that reflect their contribution to the classifier decision. However, not all features are necessary to explain a model decision. In practice, classifiers might strongly rely on a subset of features that might be sufficient to explain a particular decision. The aim of this article is to propose a method to simplify the prediction explanation of One-Dimensional (1D) Convolutional Neural Networks (CNN) by identifying sufficient and necessary features-sets. We also propose an adaptation of Layer-wise Relevance Propagation for 1D-CNN. Experiments carried out on multiple datasets show that the distribution of relevance among features is similar to that obtained with a well known state of the art model. Moreover, the sufficient and necessary features extracted perceptually appear convincing to humans.
Dense Relational Image Captioning via Multi-task Triple-Stream Networks
Authors: Dong-Jin Kim, Tae-Hyun Oh, Jinsoo Choi, In So Kweon
Date: 2020-10-08 00:00:00
Tasks: Graph Generation, Image Captioning, Object, POS, Relational Captioning, Retrieval, Scene Graph Generation

We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS; i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to learn not only to generate captions but also to understand the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. Then, we present applications of our framework to holistic image captioning, scene graph generation, and retrieval tasks.
Automatic generation of reviews of scientific papers
Authors: Anna Nikiforovskaya, Nikolai Kapralov, Anna Vlasova, Oleg Shpynov, Aleksei Shpilman
Date: 2020-10-08 00:00:00
Tasks: Extractive Summarization

With an ever-increasing number of scientific papers published each year, it becomes more difficult for researchers to explore a field that they are not closely familiar with already. This greatly inhibits the potential for cross-disciplinary research. A traditional introduction into an area may come in the form of a review paper. However, not all areas and sub-areas have a current review. In this paper, we present a method for the automatic generation of a review paper corresponding to a user-defined query. This method consists of two main parts. The first part identifies key papers in the area by their bibliometric parameters, such as a graph of co-citations. The second stage uses a BERT based architecture that we train on existing reviews for extractive summarization of these key papers. We describe the general pipeline of our method and some implementation details and present both automatic and expert evaluations on the PubMed dataset.
Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
Authors: Gareth P. Jones, James M. Hickey, Pietro G. Di Stefano, Charanpal Dhanjal, Laura C. Stoddart, Vlasios Vasileiou
Date: 2020-10-08 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Understanding and removing bias from the decisions made by machine learning models is essential to avoid discrimination against unprivileged groups. Despite recent progress in algorithmic fairness, there is still no clear answer as to which bias-mitigation approaches are most effective. Evaluation strategies are typically use-case specific, rely on data with unclear bias, and employ a fixed policy to convert model outputs to decision outcomes. To address these problems, we performed a systematic comparison of a number of popular fairness algorithms applicable to supervised classification. Our study is the most comprehensive of its kind. It utilizes three real and four synthetic datasets, and two different ways of converting model outputs to decisions. It considers fairness, predictive-performance, calibration quality, and speed of 28 different modelling pipelines, corresponding to both fairness-unaware and fairness-aware algorithms. We found that fairness-unaware algorithms typically fail to produce adequately fair models and that the simplest algorithms are not necessarily the fairest ones. We also found that fairness-aware algorithms can induce fairness without material drops in predictive power. Finally, we found that dataset idiosyncracies (e.g., degree of intrinsic unfairness, nature of correlations) do affect the performance of fairness-aware approaches. Our results allow the practitioner to narrow down the approach(es) they would like to adopt without having to know in advance their fairness requirements.
Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
Authors: Mona Fuhrländer, Sebastian Schöps
Date: 2020-10-08 00:00:00
Tasks: regression

Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.
Information Theory Measures via Multidimensional Gaussianization
Authors: Valero Laparra, J. Emmanuel Johnson, Gustau Camps-Valls, Raul Santos-Rodríguez, Jesus Malo
Date: 2020-10-08 00:00:00
Tasks: Density Estimation

Information theory is an outstanding framework to measure uncertainty, dependence and relevance in data and systems. It has several desirable properties for real world applications: it naturally deals with multivariate data, it can handle heterogeneous data types, and the measures can be interpreted in physical units. However, it has not been adopted by a wider audience because obtaining information from multidimensional data is a challenging problem due to the curse of dimensionality. Here we propose an indirect way of computing information based on a multivariate Gaussianization transform. Our proposal mitigates the difficulty of multivariate density estimation by reducing it to a composition of tractable (marginal) operations and simple linear transformations, which can be interpreted as a particular deep neural network. We introduce specific Gaussianization-based methodologies to estimate total correlation, entropy, mutual information and Kullback-Leibler divergence. We compare them to recent estimators showing the accuracy on synthetic data generated from different multivariate distributions. We made the tools and datasets publicly available to provide a test-bed to analyze future methodologies. Results show that our proposal is superior to previous estimators particularly in high-dimensional scenarios; and that it leads to interesting insights in neuroscience, geoscience, computer vision, and machine learning.
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild
Authors: Denis Dresvyanskiy, Elena Ryumina, Heysem Kaya, Maxim Markitantov, Alexey Karpov, Wolfgang Minker
Date: 2020-10-07 00:00:00
Tasks: Deep Learning, Emotion Recognition, Multimodal Emotion Recognition, Transfer Learning

In this paper, we present our contribution to ABAW facial expression challenge. We report the proposed system and the official challenge results adhering to the challenge protocol. Using end-to-end deep learning and benefiting from transfer learning approaches, we reached a test set challenge performance measure of 42.10%.
On the importance of pre-training data volume for compact language models
Authors: Vincent Micheli, Martin d'Hoffschmidt, François Fleuret
Date: 2020-10-08 00:00:00
Proceeding: EMNLP 2020 11
Tasks: FQuAD, Language Modeling, Language Modelling, Question Answering

Recent advances in language modeling have led to computationally intensive and resource-demanding state-of-the-art models. In an effort towards sustainable practices, we study the impact of pre-training data volume on compact language models. Multiple BERT-based models are trained on gradually increasing amounts of French text. Through fine-tuning on the French Question Answering Dataset (FQuAD), we observe that well-performing models are obtained with as little as 100 MB of text. In addition, we show that past critically low amounts of pre-training data, an intermediate pre-training step on the task-specific corpus does not yield substantial improvements.
Cohesive Networks using Delayed Self Reinforcement
Authors: Santosh Devasia
Date: 2020-03-14 00:00:00

How a network gets to the goal (a consensus value) can be as important as reaching the consensus value. While prior methods focus on rapidly getting to a new consensus value, maintaining cohesion, during the transition between consensus values or during tracking, remains challenging and has not been addressed. The main contributions of this work are to address the problem of maintaining cohesion by: (i) proposing a new delayed self-reinforcement (DSR) approach; (ii) extending it for use with agents that have higher-order, heterogeneous dynamics, and (iii) developing stability conditions for the DSR-based method. With DSR, each agent uses current and past information from neighbors to infer the overall goal and modifies the update law to improve cohesion. The advantages of the proposed DSR approach are that it only requires already-available information from a given network to improve the cohesion and does not require network-connectivity modifications (which might not be always feasible) nor increases in the system's overall response speed (which can require larger input). Moreover, illustrative simulation examples are used to comparatively evaluate the performance with and without DSR. The simulation results show substantial improvement in cohesion with DSR.
Quantum-enhanced barcode decoding and pattern recognition
Authors: Leonardo Banchi, Quntao Zhuang, Stefano Pirandola
Date: 2020-10-07 00:00:00
Tasks: Two-sample testing

Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.
Host Load Prediction with Bi-directional Long Short-Term Memory in Cloud Computing
Authors: Hengheng Shen, Xuehai Hong
Date: 2020-07-23 00:00:00
Tasks: Cloud Computing, Decoder

Host load prediction is the basic decision information for managing the computing resources usage on the cloud platform, its accuracy is critical for achieving the servicelevel agreement. Host load data in cloud environment is more high volatility and noise compared to that of grid computing, traditional data-driven methods tend to have low predictive accuracy when dealing with host load of cloud computing, Thus, we have proposed a host load prediction method based on Bidirectional Long Short-Term Memory (BiLSTM) in this paper. Our BiLSTM-based apporach improve the memory capbility and nonlinear modeling ability of LSTM and LSTM Encoder-Decoder (LSTM-ED), which is used in the recent previous work, In order to evaluate our approach, we have conducted experiments using a 1-month trace of a Google data centre with more than twelve thousand machines. our BiLSTM-based approach successfully achieves higher accuracy than other previous models, including the recent LSTM one and LSTM-ED one.
Simplicial Neural Networks
Authors: Stefania Ebli, Michaël Defferrard, Gard Spreemann
Date: 2020-10-07 00:00:00
Proceeding: NeurIPS Workshop TDA_and_Beyond 2020 12

We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are natural multi-dimensional extensions of graphs that encode not only pairwise relationships but also higher-order interactions between vertices - allowing us to consider richer data, including vector fields and $n$-fold collaboration networks. We define an appropriate notion of convolution that we leverage to construct the desired convolutional neural networks. We test the SNNs on the task of imputing missing data on coauthorship complexes.
Size-Invariant Visual Cryptography With Improved Perceptual Quality for Grayscale Image
Authors: 孙锐
Date: 2020-06-01 00:00:00

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9186114
Parkland Trauma Index of Mortality (PTIM): Real-time Predictive Model for PolyTrauma Patients
Authors: Adam J. Starr, Manjula Julka, Arun Nethi, John D. Watkins, Ryan W. Fairchild, Michael W. Cripps, Dustin Rinehart, Hayden N. Box
Date: 2020-10-07 00:00:00
Tasks: Decision Making, Mortality Prediction, Specificity

Vital signs and laboratory values are routinely used to guide clinical decision-making for polytrauma patients, such as the decision to use damage control techniques versus early definitive fracture fixation. Prior multivariate models have tried to predict mortality risk, but due to several limitations like one-time prediction at the time of admission, they have not proven clinically useful. There is a need for a dynamic model that captures evolving physiologic changes during patient's hospital course to trauma and resuscitation for mortality prediction. The Parkland Trauma Index of Mortality (PTIM) is a machine learning algorithm that uses electronic medical record (EMR) data to predict $48-$hour mortality during the first $72$ hours of hospitalization. The model updates every hour, evolving with the patient's physiologic response to trauma. Area under (AUC) the receiver-operator characteristic curve (ROC), sensitivity, specificity, positive (PPV) and negative predictive value (NPV), and positive and negative likelihood ratios (LR) were used to evaluate model performance. By evolving with the patient's physiologic response to trauma and relying only on EMR data, the PTIM overcomes many of the limitations of prior mortality risk models. It may be a useful tool to inform clinical decision-making for polytrauma patients early in their hospitalization.
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Authors: Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai
Date: 2020-10-08 00:00:00
Proceeding: ICLR 2021 1
Tasks: 2D Object Detection, Object Detection, Real-Time Object Detection

DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. Code is released at https://github.com/fundamentalvision/Deformable-DETR.
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination
Authors: Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Date: 2020-10-08 00:00:00
Tasks: Adversarial Robustness, Multi-Armed Bandits, regression

In this work we revisit two classic high-dimensional online learning problems, namely linear regression and contextual bandits, from the perspective of adversarial robustness. Existing works in algorithmic robust statistics make strong distributional assumptions that ensure that the input data is evenly spread out or comes from a nice generative model. Is it possible to achieve strong robustness guarantees even without distributional assumptions altogether, where the sequence of tasks we are asked to solve is adaptively and adversarially chosen? We answer this question in the affirmative for both linear regression and contextual bandits. In fact our algorithms succeed where conventional methods fail. In particular we show strong lower bounds against Huber regression and more generally any convex M-estimator. Our approach is based on a novel alternating minimization scheme that interleaves ordinary least-squares with a simple convex program that finds the optimal reweighting of the distribution under a spectral constraint. Our results obtain essentially optimal dependence on the contamination level $\eta$, reach the optimal breakdown point, and naturally apply to infinite dimensional settings where the feature vectors are represented implicitly via a kernel map.
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Authors: Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, Francois Lanusse, Casey Lam, Jessica Lu
Date: 2020-10-08 00:00:00
Tasks: Density Estimation

Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface. Current analysis of such events requires both expert knowledge and large-scale grid searches to locate the approximate solution as a prerequisite to MCMC posterior sampling. As the next generation, space-based microlensing survey with the Roman Space Observatory is expected to yield thousands of binary microlensing events, a new scalable and automated approach is desired. Here, we present an automated inference method based on neural density estimation (NDE). We show that the NDE trained on simulated Roman data not only produces fast, accurate, and precise posteriors but also captures expected posterior degeneracies. A hybrid NDE-MCMC framework can further be applied to produce the exact posterior.