pwc-archive/papers-with-abstracts huggingface dataset:
Towards Interpretable Ensemble Learning for Image-based Malware Detection
Authors: Yuzhou Lin, Xiaolin Chang
Date: 2021-01-13 00:00:00
Tasks: Ensemble Learning, Malware Detection

Deep learning (DL) models for image-based malware detection have exhibited their capability in producing high prediction accuracy. But model interpretability is posing challenges to their widespread application in security and safety-critical application domains. This paper aims for designing an Interpretable Ensemble learning approach for image-based Malware Detection (IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL) detector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach, which can give the pixel-level explanation to SDEL detector outputs. Furthermore, we develop formulas for calculating fidelity, robustness and expressiveness on pixel-level heatmaps in order to assess the quality of EDTD explanation. With EDTD explanation, we develop a novel Interpretable Dropout approach (IDrop), which establishes IEMD by training SDEL detector. Experiment results exhibit the better explanation of our EDTD than the previous explanation methods for image-based malware detection. Besides, experiment results indicate that IEMD achieves a higher detection accuracy up to 99.87% while exhibiting interpretability with high quality of prediction results. Moreover, experiment results indicate that IEMD interpretability increases with the increasing detection accuracy during the construction of IEMD. This consistency suggests that IDrop can mitigate the tradeoff between model interpretability and detection accuracy.
Neural Sequence-to-grid Module for Learning Symbolic Rules
Authors: Segwang Kim, Hyoungwook Nam, Joonyoung Kim, Kyomin Jung
Date: 2021-01-13 00:00:00
Tasks: Logical Reasoning

Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can easily extend learned symbolic rules. To resolve this difficulty, we propose a neural sequence-to-grid (seq2grid) module, an input preprocessor that automatically segments and aligns an input sequence into a grid. As our module outputs a grid via a novel differentiable mapping, any neural network structure taking a grid input, such as ResNet or TextCNN, can be jointly trained with our module in an end-to-end fashion. Extensive experiments show that neural networks having our module as an input preprocessor achieve OOD generalization on various arithmetic and algorithmic problems including number sequence prediction problems, algebraic word problems, and computer program evaluation problems while other state-of-the-art sequence transduction models cannot. Moreover, we verify that our module enhances TextCNN to solve the bAbI QA tasks without external memory.
A Non-Parametric Subspace Analysis Approach with Application to Anomaly Detection Ensembles
Authors: Marcelo Bacher, Irad Ben-Gal, Erez Shmueli
Date: 2021-01-13 00:00:00
Tasks: Anomaly Detection, Attribute

Identifying anomalies in multi-dimensional datasets is an important task in many real-world applications. A special case arises when anomalies are occluded in a small set of attributes, typically referred to as a subspace, and not necessarily over the entire data space. In this paper, we propose a new subspace analysis approach named Agglomerative Attribute Grouping (AAG) that aims to address this challenge by searching for subspaces that are comprised of highly correlative attributes. Such correlations among attributes represent a systematic interaction among the attributes that can better reflect the behavior of normal observations and hence can be used to improve the identification of two particularly interesting types of abnormal data samples: anomalies that are occluded in relatively small subsets of the attributes and anomalies that represent a new data class. AAG relies on a novel multi-attribute measure, which is derived from information theory measures of partitions, for evaluating the "information distance" between groups of data attributes. To determine the set of subspaces to use, AAG applies a variation of the well-known agglomerative clustering algorithm with the proposed multi-attribute measure as the underlying distance function. Finally, the set of subspaces is used in an ensemble for anomaly detection. Extensive evaluation demonstrates that, in the vast majority of cases, the proposed AAG method (i) outperforms classical and state-of-the-art subspace analysis methods when used in anomaly detection ensembles, and (ii) generates fewer subspaces with a fewer number of attributes each (on average), thus resulting in a faster training time for the anomaly detection ensemble. Furthermore, in contrast to existing methods, the proposed AAG method does not require any tuning of parameters.
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems
Authors: Foozhan Ataiefard, Mohammad Jafar Mashhadi, Hadi Hemmati, Niel Walkinshaw
Date: 2021-01-13 00:00:00
Tasks: Anomaly Detection, Change Point Detection, Time Series, Time Series Analysis, Transfer Learning

Many software engineering tasks, such as testing, and anomaly detection can benefit from the ability to infer a behavioral model of the software.Most existing inference approaches assume access to code to collect execution sequences. In this paper, we investigate a black-box scenario, where the system under analysis cannot be instrumented, in this granular fashion.This scenario is particularly prevalent with control systems' log analysis in the form of continuous signals. In this situation, an execution trace amounts to a multivariate time-series of input and output signals, where different states of the system correspond to different `phases` in the time-series. The main challenge is to detect when these phase changes take place. Unfortunately, most existing solutions are either univariate, make assumptions on the data distribution, or have limited learning power.Therefore, we propose a hybrid deep neural network that accepts as input a multivariate time series and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns over time.We show how this approach can be used to accurately detect state changes, and how the inferred models can be successfully applied to transfer-learning scenarios, to accurately process traces from different products with similar execution characteristics. Our experimental results on two UAV autopilot case studies indicate that our approach is highly accurate (over 90% F1 score for state classification) and significantly improves baselines (by up to 102% for change point detection).Using transfer learning we also show that up to 90% of the maximum achievable F1 scores in the open-source case study can be achieved by reusing the trained models from the industrial case and only fine tuning them using as low as 5 labeled samples, which reduces the manual labeling effort by 98%.
Multi-Source Anomaly Detection in Distributed IT Systems
Authors: Jasmin Bogatinovski, Sasho Nedelkoski
Date: 2021-01-13 00:00:00
Tasks: Anomaly Detection

The multi-source data generated by distributed systems, provide a holistic description of the system. Harnessing the joint distribution of the different modalities by a learning model can be beneficial for critical applications for maintenance of the distributed systems. One such important task is the task of anomaly detection where we are interested in detecting the deviation of the current behaviour of the system from the theoretically expected. In this work, we utilize the joint representation from the distributed traces and system log data for the task of anomaly detection in distributed systems. We demonstrate that the joint utilization of traces and logs produced better results compared to the single modality anomaly detection methods. Furthermore, we formalize a learning task - next template prediction NTP, that is used as a generalization for anomaly detection for both logs and distributed trace. Finally, we demonstrate that this formalization allows for the learning of template embedding for both the traces and logs. The joint embeddings can be reused in other applications as good initialization for spans and logs.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Authors: Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi
Date: 2021-01-13 00:00:00
Proceeding: EACL 2021 2
Tasks: General Classification, Hierarchical Multi-label Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Text Classification

We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known. Encouraged by the recent success of hyperbolic embeddings in capturing hierarchical relations, we propose to jointly learn the classifier parameters as well as the label embeddings. Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy. We propose a novel formulation for the joint learning and empirically evaluate its efficacy. The results show that the joint learning improves over the baseline that employs label co-occurrence based pre-trained hyperbolic embeddings. Moreover, the proposed classifiers achieve state-of-the-art generalization on standard benchmarks. We also present evaluation of the hyperbolic embeddings obtained by joint learning and show that they represent the hierarchy more accurately than the other alternatives.
Sequential IoT Data Augmentation using Generative Adversarial Networks
Authors: Maximilian Ernst Tschuchnig, Cornelia Ferner, Stefan Wegenkittl
Date: 2021-01-13 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation

Sequential data in industrial applications can be used to train and evaluate machine learning models (e.g. classifiers). Since gathering representative amounts of data is difficult and time consuming, there is an incentive to generate it from a small ground truth. Data augmentation is a common method to generate more data through a priori knowledge with one specific method, so called generative adversarial networks (GANs), enabling data generation from noise. This paper investigates the possibility of using GANs in order to augment sequential Internet of Things (IoT) data, with an example implementation that generates household energy consumption data with and without swimming pools. The results of the example implementation seem subjectively similar to the original data. Additionally to this subjective evaluation, the paper also introduces a quantitative evaluation technique for GANs if labels are provided. The positive results from the evaluation support the initial assumption that generating sequential data from a small ground truth is possible. This means that tedious data acquisition of sequential data can be shortened. In the future, the results of this paper may be included as a tool in machine learning, tackling the small data challenge.
Fast convolutional neural networks on FPGAs with hls4ml
Authors: Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Christoffer Petersson, Hampus Linander, Yutaro Iiyama, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Dylan Rankin, Sergo Jindariani, Kevin Pedro, Nhan Tran, Mia Liu, Edward Kreinar, Zhenbin Wu, Duc Hoang
Date: 2021-01-13 00:00:00
Tasks: Model Compression, Quantization

We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of $5\,\mu$s using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels
Authors: Jian Tan, Niv Nayman, Mengchang Wang
Date: 2021-01-13 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Bayesian Optimization, Computational Efficiency, Vocal Bursts Valence Prediction

Bayesian optimization is a popular method for optimizing expensive black-box functions. Yet it oftentimes struggles in high dimensions where the computation could be prohibitively heavy. To alleviate this problem, we introduce Coordinate backoff Bayesian Optimization (CobBO) with two-stage kernels. During each round, the first stage uses a simple coarse kernel that sacrifices the approximation accuracy for computational efficiency. It captures the global landscape by purposely smoothing away local fluctuations. Then, in the second stage of the same round, past observed points in the full space are projected to the selected subspace to form virtual points. These virtual points, along with the means and variances of their unknown function values estimated using the simple kernel of the first stage, are fitted to a more sophisticated kernel model in the second stage. Within the selected low dimensional subspace, the computational cost of conducting Bayesian optimization therein becomes affordable. To further enhance the performance, a sequence of consecutive observations in the same subspace are collected, which can effectively refine the approximation of the function. This refinement lasts until a stopping rule is met determining when to back off from a certain subspace and switch to another. This decoupling significantly reduces the computational burden in high dimensions, which fully leverages the observations in the whole space rather than only relying on observations in each coordinate subspace. Extensive evaluations show that CobBO finds solutions comparable to or better than other state-of-the-art methods for dimensions ranging from tens to hundreds, while reducing both the trial complexity and computational costs.
Temporal Knowledge Graph Forecasting with Neural ODE
Authors: Zhen Han, Zifeng Ding, Yunpu Ma, Yujia Gu, Volker Tresp
Date: 2021-01-13 00:00:00
Tasks: Future prediction, Knowledge Graphs

There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model's superior performance on the future link forecasting task.
MC-LSTM: Mass-Conserving LSTM
Authors: Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer
Date: 2021-01-13 00:00:00
Tasks: Inductive Bias

The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with random weights, meaning without learning. Similarly, Long Short-Term Memory (LSTM) has a strong inductive bias towards storing information over time. However, many real-world systems are governed by conservation laws, which lead to the redistribution of particular quantities -- e.g. in physical and economical systems. Our novel Mass-Conserving LSTM (MC-LSTM) adheres to these conservation laws by extending the inductive bias of LSTM to model the redistribution of those stored quantities. MC-LSTMs set a new state-of-the-art for neural arithmetic units at learning arithmetic operations, such as addition tasks, which have a strong conservation law, as the sum is constant over time. Further, MC-LSTM is applied to traffic forecasting, modelling a pendulum, and a large benchmark dataset in hydrology, where it sets a new state-of-the-art for predicting peak flows. In the hydrology example, we show that MC-LSTM states correlate with real-world processes and are therefore interpretable.
Denoising Score Matching with Random Fourier Features
Authors: Tsimboy Olga, Yermek Kapushev, Evgeny Burnaev, Ivan Oseledets
Date: 2021-01-13 00:00:00
Tasks: Denoising, Density Estimation

The density estimation is one of the core problems in statistics. Despite this, existing techniques like maximum likelihood estimation are computationally inefficient due to the intractability of the normalizing constant. For this reason an interest to score matching has increased being independent on the normalizing constant. However, such estimator is consistent only for distributions with the full space support. One of the approaches to make it consistent is to add noise to the input data which is called Denoising Score Matching. In this work we derive analytical expression for the Denoising Score matching using the Kernel Exponential Family as a model distribution. The usage of the kernel exponential family is motivated by the richness of this class of densities. To tackle the computational complexity we use Random Fourier Features based approximation of the kernel function. The analytical expression allows to drop additional regularization terms based on the higher-order derivatives as they are already implicitly included. Moreover, the obtained expression explicitly depends on the noise variance, so the validation loss can be straightforwardly used to tune the noise level. Along with benchmark experiments, the model was tested on various synthetic distributions to study the behaviour of the model in different cases. The empirical study shows comparable quality to the competing approaches, while the proposed method being computationally faster. The latter one enables scaling up to complex high-dimensional data.
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Authors: Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare
Date: 2021-01-13 00:00:00
Proceeding: ICLR 2021 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite.
Automated Synthesis of Steady-State Continuous Processes using Reinforcement Learning
Authors: Quirin Göttl, Dominik G. Grimm, Jakob Burger
Date: 2021-01-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics of prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially built up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.
Explicit homography estimation improves contrastive self-supervised learning
Authors: David Torpey, Richard Klein
Date: 2021-01-12 00:00:00
Tasks: Contrastive Learning, Homography Estimation, Self-Supervised Learning

The typical contrastive self-supervised algorithm uses a similarity measure in latent space as the supervision signal by contrasting positive and negative images directly or indirectly. Although the utility of self-supervised algorithms has improved recently, there are still bottlenecks hindering their widespread use, such as the compute needed. In this paper, we propose a module that serves as an additional objective in the self-supervised contrastive learning paradigm. We show how the inclusion of this module to regress the parameters of an affine transformation or homography, in addition to the original contrastive objective, improves both performance and learning speed. Importantly, we ensure that this module does not enforce invariance to the various components of the affine transform, as this is not always ideal. We demonstrate the effectiveness of the additional objective on two recent, popular self-supervised algorithms. We perform an extensive experimental analysis of the proposed method and show an improvement in performance for all considered datasets. Further, we find that although both the general homography and affine transformation are sufficient to improve performance and convergence, the affine transformation performs better in all cases.