pwc-archive/papers-with-abstracts huggingface dataset:
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
Authors: Aldo Pacchiano, Peter Bartlett, Michael I. Jordan
Date: 2021-11-08 00:00:00
Tasks: Multi-Armed Bandits

We study the problem of information sharing and cooperation in Multi-Player Multi-Armed bandits. We propose the first algorithm that achieves logarithmic regret for this problem when the collision reward is unknown. Our results are based on two innovations. First, we show that a simple modification to a successive elimination strategy can be used to allow the players to estimate their suboptimality gaps, up to constant factors, in the absence of collisions. Second, we leverage the first result to design a communication protocol that successfully uses the small reward of collisions to coordinate among players, while preserving meaningful instance-dependent logarithmic regret guarantees.
Explaining Face Presentation Attack Detection Using Natural Language
Authors: Hengameh Mirzaalian, Mohamed E. Hussein, Leonidas Spinoulas, Jonathan May, Wael Abd-Almageed
Date: 2021-11-08 00:00:00
Tasks: Face Presentation Attack Detection, Language Modelling, Sentence, Text Generation

A large number of deep neural network based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques' focus has been on improving PAD performance in terms of classification accuracy and robustness against unseen attacks and environmental conditions, there exists little attention on the explainability of PAD predictions. In this paper, we tackle the problem of explaining PAD predictions through natural language. Our approach passes feature representations of a deep layer of the PAD model to a language model to generate text describing the reasoning behind the PAD prediction. Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model. We investigate how the quality of the generated explanations is affected by different loss functions, including the commonly used word-wise cross entropy loss, a sentence discriminative loss, and a sentence semantic loss. We perform our experiments using face images from a dataset consisting of 1,105 bona-fide and 924 presentation attack samples. Our quantitative and qualitative results show the effectiveness of our model for generating proper PAD explanations through text as well as the power of the sentence-wise losses. To the best of our knowledge, this is the first introduction of a joint biometrics-NLP task. Our dataset can be obtained through our GitHub page.
Hybrid BYOL-ViT: Efficient approach to deal with small datasets
Authors: Safwen Naimi, Rien van Leeuwen, Wided Souidene, Slim Ben Saoud
Date: 2021-11-08 00:00:00
Tasks: image-classification, Image Classification, Self-Supervised Learning

Supervised learning can learn large representational spaces, which are crucial for handling difficult learning tasks. However, due to the design of the model, classical image classification approaches struggle to generalize to new problems and new situations when dealing with small datasets. In fact, supervised learning can lose the location of image features which leads to supervision collapse in very deep architectures. In this paper, we investigate how self-supervision with strong and sufficient augmentation of unlabeled data can train effectively the first layers of a neural network even better than supervised learning, with no need for millions of labeled data. The main goal is to disconnect pixel data from annotation by getting generic task-agnostic low-level features. Furthermore, we look into Vision Transformers (ViT) and show that the low-level features derived from a self-supervised architecture can improve the robustness and the overall performance of this emergent architecture. We evaluated our method on one of the smallest open-source datasets STL-10 and we obtained a significant boost of performance from 41.66% to 83.25% when inputting low-level features from a self-supervised learning architecture to the ViT instead of the raw images.
Efficient estimates of optimal transport via low-dimensional embeddings
Authors: Patric M. Fulop, Vincent Danos
Date: 2021-11-08 00:00:00

Optimal transport distances (OT) have been widely used in recent work in Machine Learning as ways to compare probability distributions. These are costly to compute when the data lives in high dimension. Recent work by Paty et al., 2019, aims specifically at reducing this cost by computing OT using low-rank projections of the data (seen as discrete measures). We extend this approach and show that one can approximate OT distances by using more general families of maps provided they are 1-Lipschitz. The best estimate is obtained by maximising OT over the given family. As OT calculations are done after mapping data to a lower dimensional space, our method scales well with the original data dimension. We demonstrate the idea with neural networks.
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
Authors: YooJung Choi, Tal Friedman, Guy Van Den Broeck
Date: 2021-11-08 00:00:00
Tasks: Decision Making

Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient, often linear-time, inference of queries such as marginals and most probable explanations (MPE). However, marginal MAP, which is central to many decision-making problems, remains a hard query for PCs unless they satisfy highly restrictive structural constraints. In this paper, we develop a pruning algorithm that removes parts of the PC that are irrelevant to a marginal MAP query, shrinking the PC while maintaining the correct solution. This pruning technique is so effective that we are able to build a marginal MAP solver based solely on iteratively transforming the circuit -- no search is required. We empirically demonstrate the efficacy of our approach on real-world datasets.
Deep Learning Approach for Aggressive Driving Behaviour Detection
Authors: Farid Talebloo, Emad A. Mohammed, Behrouz Far
Date: 2021-11-08 00:00:00
Tasks: Deep Learning, Position

Driving behaviour is one of the primary causes of road crashes and accidents, and these can be decreased by identifying and minimizing aggressive driving behaviour. This study identifies the timesteps when a driver in different circumstances (rush, mental conflicts, reprisal) begins to drive aggressively. An observer (real or virtual) is needed to examine driving behaviour to discover aggressive driving occasions; we overcome this problem by using a smartphone's GPS sensor to detect locations and classify drivers' driving behaviour every three minutes. To detect timeseries patterns in our dataset, we employ RNN (GRU, LSTM) algorithms to identify patterns during the driving course. The algorithm is independent of road, vehicle, position, or driver characteristics. We conclude that three minutes (or more) of driving (120 seconds of GPS data) is sufficient to identify driver behaviour. The results show high accuracy and a high F1 score.
Visual Question Answering based on Formal Logic
Authors: Muralikrishnna G. Sethuraman, Ali Payani, Faramarz Fekri, J. Clayton Kerce
Date: 2021-11-08 00:00:00
Tasks: Formal Logic, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) has been gaining a lot of traction in the machine learning community in the recent years due to the challenges posed in understanding information coming from multiple modalities (i.e., images, language). In VQA, a series of questions are posed based on a set of images and the task at hand is to arrive at the answer. To achieve this, we take a symbolic reasoning based approach using the framework of formal logic. The image and the questions are converted into symbolic representations on which explicit reasoning is performed. We propose a formal logic framework where (i) images are converted to logical background facts with the help of scene graphs, (ii) the questions are translated to first-order predicate logic clauses using a transformer based deep learning model, and (iii) perform satisfiability checks, by using the background knowledge and the grounding of predicate clauses, to obtain the answer. Our proposed method is highly interpretable and each step in the pipeline can be easily analyzed by a human. We validate our approach on the CLEVR and the GQA dataset. We achieve near perfect accuracy of 99.6% on the CLEVR dataset comparable to the state of art models, showcasing that formal logic is a viable tool to tackle visual question answering. Our model is also data efficient, achieving 99.1% accuracy on CLEVR dataset when trained on just 10% of the training data.
ML-EXray: Visibility into ML Deployment on the Edge
Authors: Hang Qiu, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, Sachin Katti
Date: 2021-11-08 00:00:00
Tasks: Quantization

Benefiting from expanding cloud infrastructure, deep neural networks (DNNs) today have increasingly high performance when trained in the cloud. Researchers spend months of effort competing for an extra few percentage points of model accuracy. However, when these models are actually deployed on edge devices in practice, very often, the performance can abruptly drop over 10% without obvious reasons. The key challenge is that there is not much visibility into ML inference execution on edge devices, and very little awareness of potential issues during the edge deployment process. We present ML-EXray, an end-to-end framework, which provides visibility into layer-level details of the ML execution, and helps developers analyze and debug cloud-to-edge deployment issues. More often than not, the reason for sub-optimal edge performance does not only lie in the model itself, but every operation throughout the data flow and the deployment process. Evaluations show that ML-EXray can effectively catch deployment issues, such as pre-processing bugs, quantization issues, suboptimal kernels, etc. Using ML-EXray, users need to write less than 15 lines of code to fully examine the edge deployment pipeline. Eradicating these issues, ML-EXray can correct model performance by up to 30%, pinpoint error-prone layers, and guide users to optimize kernel execution latency by two orders of magnitude. Code and APIs will be released as an open-source multi-lingual instrumentation library and a Python deployment validation library.
Losses, Dissonances, and Distortions
Authors: Pablo Samuel Castro
Date: 2021-11-08 00:00:00

In this paper I present a study in using the losses and gradients obtained during the training of a simple function approximator as a mechanism for creating musical dissonance and visual distortion in a solo piano performance setting. These dissonances and distortions become part of an artistic performance not just by affecting the visualizations, but also by affecting the artistic musical performance. The system is designed such that the performer can in turn affect the training process itself, thereby creating a closed feedback loop between two processes: the training of a machine learning model and the performance of an improvised piano piece.
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images
Authors: Nadia Brancati, Anna Maria Anniciello, Pushpak Pati, Daniel Riccio, Giosuè Scognamiglio, Guillaume Jaume, Giuseppe De Pietro, Maurizio Di Bonito, Antonio Foncubierta, Gerardo Botti, Maria Gabrani, Florinda Feroce, Maria Frucci
Date: 2021-11-08 00:00:00
Tasks: Diagnostic, whole slide images

Breast cancer is the most commonly diagnosed cancer and registers the highest number of deaths for women with cancer. Recent advancements in diagnostic activities combined with large-scale screening policies have significantly lowered the mortality rates for breast cancer patients. However, the manual inspection of tissue slides by the pathologists is cumbersome, time-consuming, and is subject to significant inter- and intra-observer variability. Recently, the advent of whole-slide scanning systems have empowered the rapid digitization of pathology slides, and enabled to develop digital workflows. These advances further enable to leverage Artificial Intelligence (AI) to assist, automate, and augment pathological diagnosis. But the AI techniques, especially Deep Learning (DL), require a large amount of high-quality annotated data to learn from. Constructing such task-specific datasets poses several challenges, such as, data-acquisition level constrains, time-consuming and expensive annotations, and anonymization of private information. In this paper, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of annotated Hematoxylin & Eosin (H&E)-stained images to facilitate the characterization of breast lesions. BRACS contains 547 Whole-Slide Images (WSIs), and 4539 Regions of Interest (ROIs) extracted from the WSIs. Each WSI, and respective ROIs, are annotated by the consensus of three board-certified pathologists into different lesion categories. Specifically, BRACS includes three lesion types, i.e., benign, malignant and atypical, which are further subtyped into seven categories. It is, to the best of our knowledge, the largest annotated dataset for breast cancer subtyping both at WSI- and ROI-level. Further, by including the understudied atypical lesions, BRACS offers an unique opportunity for leveraging AI to better understand their characteristics.
Mixed Transformer U-Net For Medical Image Segmentation
Authors: Hongyi Wang, Shiao Xie, Lanfen Lin, Yutaro Iwamoto, Xian-Hua Han, Yen-Wei Chen, Ruofeng Tong
Date: 2021-11-08 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA). However, Transformers usually rely on large-scale pre-training and have high computational complexity. Furthermore, SA can only model self-affinities within a single sample, ignoring the potential correlations of the overall dataset. To address these problems, we propose a novel Transformer module named Mixed Transformer Module (MTM) for simultaneous inter- and intra- affinities learning. MTM first calculates self-affinities efficiently through our well-designed Local-Global Gaussian-Weighted Self-Attention (LGG-SA). Then, it mines inter-connections between data samples through External Attention (EA). By using MTM, we construct a U-shaped model named Mixed Transformer U-Net (MT-UNet) for accurate medical image segmentation. We test our method on two different public datasets, and the experimental results show that the proposed method achieves better performance over other state-of-the-art methods. The code is available at: https://github.com/Dootmaan/MT-UNet.
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
Authors: Kei Ishida, Ali Ercan, Takeyoshi Nagasato, Masato Kiyama, Motoki Amagasaki
Date: 2021-11-07 00:00:00
Tasks: Time Series, Time Series Analysis

An architecture consisting of a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory (LSTM) network, which is referred as CNNsLSTM, was proposed for hourly-scale rainfall-runoff modeling in this study. In CNNsLTSM, the CNN component receives the hourly meteorological time series data for a long duration, and then the LSTM component receives the extracted features from 1D-CNN and the hourly meteorological time series data for a short-duration. As a case study, CNNsLSTM was implemented for hourly rainfall-runoff modeling at the Ishikari River watershed, Japan. The meteorological dataset, consists of precipitation, air temperature, evapotranspiration, and long- and short-wave radiation, were utilized as input, and the river flow was used as the target data. To evaluate the performance of proposed CNNsLSTM, results of CNNsLSTM were compared with those of 1D-CNN, LSTM only with hourly inputs (LSTMwHour), parallel architecture of 1D-CNN and LSTM (CNNpLSTM), and the LSTM architecture which uses both daily and hourly input data (LSTMwDpH). CNNsLSTM showed clear improvements on the estimation accuracy compared to the three conventional architectures (1D-CNN, LSTMwHour, and CNNpLSTM), and recently proposed LSTMwDpH. In comparison to observed flows, the median of the NSE values for the test period are 0.455-0.469 for 1D-CNN (based on NCHF=8, 16, and 32, the numbers of the channels of the feature map of the first layer of CNN), 0.639-0.656 for CNNpLSTM (based on NCHF=8, 16, and 32), 0.745 for LSTMwHour, 0.831 for LSTMwDpH, and 0.865-0.873 for CNNsLSTM (based on NCHF=8, 16, and 32). Furthermore, the proposed CNNsLSTM reduces the median RMSE of 1D-CNN by 50.2%-51.4%, CNNpLSTM by 37.4%-40.8%, LSTMwHour by 27.3%-29.5%, and LSTMwDpH by 10.6%-13.4%.
Survey of Deep Learning Methods for Inverse Problems
Authors: Shima Kamyab, Zohreh Azimifar, Rasool Sabzi, Paul Fieguth
Date: 2021-11-07 00:00:00
Tasks: Deep Learning, Denoising, Image Denoising, Inverse Rendering, Object Tracking, Survey

In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer. We choose a sample of each inverse problem type, so as to compare the robustness of the three categories, and report a statistical analysis of their differences. We perform extensive experiments on the classic problem of linear regression and three well-known inverse problems in computer vision, namely image denoising, 3D human face inverse rendering, and object tracking, selected as representative prototypes for each class of inverse problems. The overall results and the statistical analyses show that the solution categories have a robustness behaviour dependent on the type of inverse problem domain, and specifically dependent on whether or not the problem includes measurement outliers. Based on our experimental results, we conclude by proposing the most robust solution category for each inverse problem class.
A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring
Authors: Ronak Aghera, Sahil Chilana, Vishal Garg, Raghunath Reddy
Date: 2021-11-07 00:00:00
Tasks: blind source separation, Non-Intrusive Load Monitoring

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demand-side, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD).
"How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector
Authors: Zhi Lu, Vrizlynn L. L. Thing
Date: 2021-11-06 00:00:00
Tasks: Android Malware Detection, Malware Detection

AI methods have been proven to yield impressive performance on Android malware detection. However, most AI-based methods make predictions of suspicious samples in a black-box manner without transparency on models' inference. The expectation on models' explainability and transparency by cyber security and AI practitioners to assure the trustworthiness increases. In this article, we present a novel model-agnostic explanation method for AI models applied for Android malware detection. Our proposed method identifies and quantifies the data features relevance to the predictions by two steps: i) data perturbation that generates the synthetic data by manipulating features' values; and ii) optimization of features attribution values to seek significant changes of prediction scores on the perturbed data with minimal feature values changes. The proposed method is validated by three experiments. We firstly demonstrate that our proposed model explanation method can aid in discovering how AI models are evaded by adversarial samples quantitatively. In the following experiments, we compare the explainability and fidelity of our proposed method with state-of-the-arts, respectively.
Data Augmentation Can Improve Robustness
Authors: Sylvestre-Alvise Rebuffi, Sven Gowal, Dan A. Calian, Florian Stimberg, Olivia Wiles, Timothy Mann
Date: 2021-11-09 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Data Augmentation

Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust overfitting by using common data augmentation schemes. We demonstrate that, contrary to previous findings, when combined with model weight averaging, data augmentation can significantly boost robust accuracy. Furthermore, we compare various augmentations techniques and observe that spatial composition techniques work the best for adversarial training. Finally, we evaluate our approach on CIFAR-10 against $\ell_\infty$ and $\ell_2$ norm-bounded perturbations of size $\epsilon = 8/255$ and $\epsilon = 128/255$, respectively. We show large absolute improvements of +2.93% and +2.16% in robust accuracy compared to previous state-of-the-art methods. In particular, against $\ell_\infty$ norm-bounded perturbations of size $\epsilon = 8/255$, our model reaches 60.07% robust accuracy without using any external data. We also achieve a significant performance boost with this approach while using other architectures and datasets such as CIFAR-100, SVHN and TinyImageNet.
Monocular Human Shape and Pose with Dense Mesh-borne Local Image Features
Authors: Shubhendu Jena, Franck Multon, Adnane Boukhayma
Date: 2021-11-09 00:00:00
Tasks: Pose Estimation

We propose to improve on graph convolution based approaches for human shape and pose estimation from monocular input, using pixel-aligned local image features. Given a single input color image, existing graph convolutional network (GCN) based techniques for human shape and pose estimation use a single convolutional neural network (CNN) generated global image feature appended to all mesh vertices equally to initialize the GCN stage, which transforms a template T-posed mesh into the target pose. In contrast, we propose for the first time the idea of using local image features per vertex. These features are sampled from the CNN image feature maps by utilizing pixel-to-mesh correspondences generated with DensePose. Our quantitative and qualitative results on standard benchmarks show that using local features improves on global ones and leads to competitive performances with respect to the state-of-the-art.