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Title: Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs Abstract: Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched represen... |
Title: muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem Abstract: Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment p... |
Title: Interpretable Deep Causal Learning for Moderation Effects Abstract: In this extended abstract paper, we address the problem of interpretability and targeted regularization in causal machine learning models. In particular, we focus on the problem of estimating individual causal/treatment effects under observed co... |
Title: R2-AD2: Detecting Anomalies by Analysing the Raw Gradient Abstract: Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a different gradient distribution. Based on this intuition, we des... |
Title: Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars Abstract: This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches f... |
Title: World of Bugs: A Platform for Automated Bug Detection in 3D Video Games Abstract: We present World of Bugs (WOB), an open platform that aims to support Automated Bug Detection (ABD) research in video games. We discuss some open problems in ABD and how they relate to the platform's design, arguing that learning-b... |
Title: Riemannian data-dependent randomized smoothing for neural networks certification Abstract: Certification of neural networks is an important and challenging problem that has been attracting the attention of the machine learning community since few years. In this paper, we focus on randomized smoothing (RS) which ... |
Title: Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering Abstract: Recently, deep learning-based image compression has made signifcant progresses, and has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC, i... |
Title: The Integration of Machine Learning into Automated Test Generation: A Systematic Literature Review Abstract: Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluatio... |
Title: Supermodular $\mf$-divergences and bounds on lossy compression and generalization error with mutual $\mf$-information Abstract: In this paper, we introduce super-modular $\mf$-divergences and provide three applications for them: (i) we introduce Sanov's upper bound on the tail probability of sum of independent r... |
Title: Personalized Subgraph Federated Learning Abstract: In real-world scenarios, subgraphs of a larger global graph may be distributed across multiple devices or institutions, and only locally accessible due to privacy restrictions, although there may be links between them. Recently proposed subgraph Federated Learni... |
Title: Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations Abstract: Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge devices, we propose to leverage app... |
Title: A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Abstract: We propose a novel framework to study asynchronous federated learning optimization with delays in gradient updates. Our theoretical framework extends the standard FedAvg aggregation scheme by introducing sto... |
Title: Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition Abstract: When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and ... |
Title: Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling Abstract: Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling observations from the environment is usually split across multiple agents. However, transferring these observations from the agents to a... |
Title: Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander Abstract: Nowadays, so as to improve services and urban areas livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project in Santander, Spain, which ... |
Title: Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems Abstract: Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world... |
Title: Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations Abstract: Over the last years, supervised learning (SL) has established itself as the state-of-the-art for data-driven turbulence modeling. In the SL paradigm, models are trained based on a dataset, which is typically computed a priori... |
Title: Open-Source Framework for Encrypted Internet and Malicious Traffic Classification Abstract: Internet traffic classification plays a key role in network visibility, Quality of Services (QoS), intrusion detection, Quality of Experience (QoE) and traffic-trend analyses. In order to improve privacy, integrity, confi... |
Title: A Contrastive Approach to Online Change Point Detection Abstract: We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to a flexible procedure suitable for both par... |
Title: Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks Abstract: Node classification on attributed networks is a semi-supervised task that is crucial for network analysis. By decoupling two critical operations in Graph Convolutional Networks (GCNs), namely feature transformat... |
Title: Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation Learning Abstract: Negative sampling (NS) loss plays an important role in learning knowledge graph embedding (KGE) to handle a huge number of entities. However, the performance of KGE degrades without hyperparameters such as the margin... |
Title: Insights into Pre-training via Simpler Synthetic Tasks Abstract: Pre-training produces representations that are effective for a wide range of downstream tasks, but it is still unclear what properties of pre-training are necessary for effective gains. Notably, recent work shows that even pre-training on synthetic... |
Title: Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning Abstract: Contrastive self-supervised learning methods learn to map data points such as images into non-parametric representation space without requiring labels. While highly successful, current methods require a large amoun... |
Title: Automatic Concept Extraction for Concept Bottleneck-based Video Classification Abstract: Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-l... |
Title: Safe and Psychologically Pleasant Traffic Signal Control with Reinforcement Learning using Action Masking Abstract: Reinforcement learning (RL) for traffic signal control (TSC) has shown better performance in simulation for controlling the traffic flow of intersections than conventional approaches. However, due ... |
Title: Finite Expression Method for Solving High-Dimensional Partial Differential Equations Abstract: Designing efficient and accurate numerical solvers for high-dimensional partial differential equations (PDEs) remains a challenging and important topic in computational science and engineering, mainly due to the ``curs... |
Title: Automatic Controllable Product Copywriting for E-Commerce Abstract: Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copywriting aims to attract users' interest and improve user experience by highlighting product characteristics with textua... |
Title: Model-Based Imitation Learning Using Entropy Regularization of Model and Policy Abstract: Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with the... |
Title: Reconstruct from Top View: A 3D Lane Detection Approach based on Geometry Structure Prior Abstract: In this paper, we propose an advanced approach in targeting the problem of monocular 3D lane detection by leveraging geometry structure underneath the process of 2D to 3D lane reconstruction. Inspired by previous ... |
Title: DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials Abstract: Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. Howeve... |
Title: Renormalized Sparse Neural Network Pruning Abstract: Large neural networks are heavily over-parameterized. This is done because it improves training to optimality. However once the network is trained, this means many parameters can be zeroed, or pruned, leaving an equivalent sparse neural network. We propose ren... |
Title: The Manifold Scattering Transform for High-Dimensional Point Cloud Data Abstract: The manifold scattering transform is a deep feature extractor for data defined on a Riemannian manifold. It is one of the first examples of extending convolutional neural network-like operators to general manifolds. The initial wor... |
Title: Finding Optimal Policy for Queueing Models: New Parameterization Abstract: Queueing systems appear in many important real-life applications including communication networks, transportation and manufacturing systems. Reinforcement learning (RL) framework is a suitable model for the queueing control problem where ... |
Title: Benchmarking Node Outlier Detection on Graphs Abstract: Graph outlier detection is an emerging but crucial machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years, the lack of a standard and unified setting for performance evaluation limits their advanc... |
Title: Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum Abstract: Despite considerable advances in deep reinforcement learning, it has been shown to be highly vulnerable to adversarial perturbations to state observations. Recent efforts that have attempted to improve adversarial robustne... |
Title: A Novel Three-Dimensional Navigation Method for the Visually Impaired Abstract: According to the World Health Organization, visual impairment is estimated to affect approximately 2.2 billion people worldwide. The visually impaired must currently rely on navigational aids to replace their sense of sight, like a w... |
Title: Decentralized Distributed Learning with Privacy-Preserving Data Synthesis Abstract: In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to s... |
Title: Identifiability of deep generative models under mixture priors without auxiliary information Abstract: We prove identifiability of a broad class of deep latent variable models that (a) have universal approximation capabilities and (b) are the decoders of variational autoencoders that are commonly used in practic... |
Title: Achieving Utility, Fairness, and Compactness via Tunable Information Bottleneck Measures Abstract: Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for critical applications. In this article,... |
Title: Deep Learning Models on CPUs: A Methodology for Efficient Training Abstract: GPUs have been favored for training deep learning models due to their highly parallelized architecture. As a result, most studies on training optimization focus on GPUs. There is often a trade-off, however, between cost and efficiency w... |
Title: QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient Abstract: Federated Learning (FL) is an emerging paradigm to enable the large-scale distributed training of machine learning models, while still providing privacy guarantees. In this work, we jointly address two of the main practical... |
Title: DNA: Proximal Policy Optimization with a Dual Network Architecture Abstract: This paper explores the problem of simultaneously learning a value function and policy in deep actor-critic reinforcement learning models. We find that the common practice of learning these functions jointly is sub-optimal, due to an or... |
Title: Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality Abstract: We revisit the problem of stochastic online learning with feedback graphs, with the goal of devising algorithms that are optimal, up to constants, both asymptotically and in finite time. We show that, surprisingly, t... |
Title: flow-based clustering and spectral clustering: a comparison Abstract: We propose and study a novel graph clustering method for data with an intrinsic network structure. Similar to spectral clustering, we exploit an intrinsic network structure of data to construct Euclidean feature vectors. These feature vectors ... |
Title: Deep Partial Least Squares for Empirical Asset Pricing Abstract: We use deep partial least squares (DPLS) to estimate an asset pricing model for individual stock returns that exploits conditioning information in a flexible and dynamic way while attributing excess returns to a small set of statistical risk factor... |
Title: Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments Abstract: We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes. Our algorithm estimates the AME,... |
Title: Limitations of the NTK for Understanding Generalization in Deep Learning Abstract: The ``Neural Tangent Kernel'' (NTK) (Jacot et al 2018), and its empirical variants have been proposed as a proxy to capture certain behaviors of real neural networks. In this work, we study NTKs through the lens of scaling laws, a... |
Title: When Does Re-initialization Work? Abstract: Re-initializing a neural network during training has been observed to improve generalization in recent works. Yet it is neither widely adopted in deep learning practice nor is it often used in state-of-the-art training protocols. This raises the question of when re-ini... |
Title: Hyperparameter Importance of Quantum Neural Networks Across Small Datasets Abstract: As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is the use of a special type of quantum circuit - a... |
Title: Model Optimization in Imbalanced Regression Abstract: Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of stu... |
Title: Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics Abstract: The class-imbalance issue is intrinsic to many real-world machine learning tasks, particularly to the rare-event classification problems. Although the impact and treatment of imbalanced data is widely known, the magni... |
Title: Mitigating Data Heterogeneity in Federated Learning with Data Augmentation Abstract: Federated Learning (FL) is a prominent framework that enables training a centralized model while securing user privacy by fusing local, decentralized models. In this setting, one major obstacle is data heterogeneity, i.e., each ... |
Title: Thompson Sampling Efficiently Learns to Control Diffusion Processes Abstract: Diffusion processes that evolve according to linear stochastic differential equations are an important family of continuous-time dynamic decision-making models. Optimal policies are well-studied for them, under full certainty about the... |
Title: Noise Estimation in Gaussian Process Regression Abstract: We develop a computational procedure to estimate the covariance hyperparameters for semiparametric Gaussian process regression models with additive noise. Namely, the presented method can be used to efficiently estimate the variance of the correlated erro... |
Title: Critical Investigation of Failure Modes in Physics-informed Neural Networks Abstract: Several recent works in scientific machine learning have revived interest in the application of neural networks to partial differential equations (PDEs). A popular approach is to aggregate the residual form of the governing PDE... |
Title: Global Context Vision Transformers Abstract: We propose global context vision transformer (GC ViT), a novel architecture that enhances parameter and compute utilization. Our method leverages global context self-attention modules, joint with local self-attention, to effectively yet efficiently model both long and... |
Title: Quantum machine learning channel discrimination Abstract: In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measuring the output state. This work studies applications of variational quantum... |
Title: Inference-Based Quantum Sensing Abstract: In a standard Quantum Sensing (QS) task one aims at estimating an unknown parameter $\theta$, encoded into an $n$-qubit probe state, via measurements of the system. The success of this task hinges on the ability to correlate changes in the parameter to changes in the sys... |
Title: A Langevin-like Sampler for Discrete Distributions Abstract: We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based proposal for sampling complex high-dimensional discrete distributions. In contrast to Gibbs sampling-based methods, DLP is able to update all coordinates in parallel in a... |
Title: Low-Precision Stochastic Gradient Langevin Dynamics Abstract: While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored. As a consequence, sampling is simply infeasible in many large-scale scenarios, despite providing remarkable benefits ... |
Title: Only Tails Matter: Average-Case Universality and Robustness in the Convex Regime Abstract: The recently developed average-case analysis of optimization methods allows a more fine-grained and representative convergence analysis than usual worst-case results. In exchange, this analysis requires a more precise hypo... |
Title: Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem Abstract: Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences. The grouping should reflect the students' aspirations as much as possibl... |
Title: Latent Variable Modelling Using Variational Autoencoders: A survey Abstract: A probability distribution allows practitioners to uncover hidden structure in the data and build models to solve supervised learning problems using limited data. The focus of this report is on Variational autoencoders, a method to lear... |
Title: On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games Abstract: Learning to cooperate with other agents is challenging when those agents also possess the ability to adapt to our own behavior. Practical and theoretical approaches to learning in cooperative settings typic... |
Title: Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world Abstract: We introduce \textit{Nocturne}, a new 2D driving simulator for investigating multi-agent coordination under partial observability. The focus of Nocturne is to enable research into inference and th... |
Title: SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression Abstract: To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms ... |
Title: Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities Abstract: It is an important problem in trustworthy machine learning to recognize out-of-distribution (OOD) inputs which are inputs unrelated to the in-distribution task. Many o... |
Title: Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models Abstract: This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the United States Internal Revenue Service (IRS). While the field of algorithmic fairness has developed pri... |
Title: COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection Abstract: More than two years after its outbreak, the COVID-19 pandemic continues to plague medical systems around the world, putting a strain on scarce resources, and claiming human lives.... |
Title: Regression of high dimensional angular momentum states of light Abstract: The Orbital Angular Momentum (OAM) of light is an infinite-dimensional degree of freedom of light with several applications in both classical and quantum optics. However, to fully take advantage of the potential of OAM states, reliable det... |
Title: A Neural Network Based Method with Transfer Learning for Genetic Data Analysis Abstract: Transfer learning has emerged as a powerful technique in many application problems, such as computer vision and natural language processing. However, this technique is largely ignored in application to genetic data analysis.... |
Title: Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review Abstract: Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repet... |
Title: Understanding Robust Learning through the Lens of Representation Similarities Abstract: Representation learning, i.e. the generation of representations useful for downstream applications, is a task of fundamental importance that underlies much of the success of deep neural networks (DNNs). Recently, robustness t... |
Title: Additive Gaussian Processes Revisited Abstract: Gaussian Process (GP) models are a class of flexible non-parametric models that have rich representational power. By using a Gaussian process with additive structure, complex responses can be modelled whilst retaining interpretability. Previous work showed that add... |
Title: Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification Abstract: Recent years have seen a growth in user-centric applications that require effective knowledge transfer across tasks in the low-data regime. An example is personalization, where a pretrained system is adapted by learning on sm... |
Title: Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation Abstract: Understanding the implicit bias of training algorithms is of crucial importance in order to explain the success of overparametrised neural networks. In this paper, we study the role of the label noise in... |
Title: Business Document Information Extraction: Towards Practical Benchmarks Abstract: Information extraction from semi-structured documents is crucial for frictionless business-to-business (B2B) communication. While machine learning problems related to Document Information Extraction (IE) have been studied for decade... |
Title: The Right Tool for the Job: Open-Source Auditing Tools in Machine Learning Abstract: In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. Howe... |
Title: A Distributional Approach for Soft Clustering Comparison and Evaluation Abstract: The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the unce... |
Title: Exceedance Probability Forecasting via Regression for Significant Wave Height Forecasting Abstract: Significant wave height forecasting is a key problem in ocean data analytics. Predicting the significant wave height is crucial for estimating the energy production from waves. Moreover, the timely prediction of l... |
Title: SMT-DTA: Improving Drug-Target Affinity Prediction with Semi-supervised Multi-task Training Abstract: Drug-Target Affinity (DTA) prediction is an essential task for drug discovery and pharmaceutical research. Accurate predictions of DTA can greatly benefit the design of new drug. As wet experiments are costly an... |
Title: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets Abstract: Data is commonly stored in tabular format. Several fields of research (e.g., biomedical, fault/fraud detection), are prone to small imbalanced tabular data. Supervised Machine Learning ... |
Title: Shapley-NAS: Discovering Operation Contribution for Neural Architecture Search Abstract: In this paper, we propose a Shapley value based method to evaluate operation contribution (Shapley-NAS) for neural architecture search. Differentiable architecture search (DARTS) acquires the optimal architectures by optimiz... |
Title: Actively Learning Deep Neural Networks with Uncertainty Sampling Based on Sum-Product Networks Abstract: Active learning is popular approach for reducing the amount of data in training deep neural network model. Its success hinges on the choice of an effective acquisition function, which ranks not yet labeled da... |
Title: Actively learning to learn causal relationships Abstract: How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose ... |
Title: Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns Abstract: Purpose: To utilize high-resolution quantitative CT (QCT) imaging features for prediction of diagnosis and prognosis in fibrosing interstitial lung diseases (ILD). Approach: 40 ILD pa... |
Title: Towards Perspective-Based Specification of Machine Learning-Enabled Systems Abstract: Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into... |
Title: Time Gated Convolutional Neural Networks for Crop Classification Abstract: This paper presented a state-of-the-art framework, Time Gated Convolutional Neural Network (TGCNN) that takes advantage of temporal information and gating mechanisms for the crop classification problem. Besides, several vegetation indices... |
Title: Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold Abstract: The prototypical NLP experiment trains a standard architecture on labeled English data and optimizes for accuracy, without accounting for other dimensions such as fairness, interpretability, or computational effici... |
Title: A Comparative Study on Application of Class-Imbalance Learning for Severity Prediction of Adverse Events Following Immunization Abstract: In collaboration with the Liaoning CDC, China, we propose a prediction system to predict the subsequent hospitalization of children with adverse reactions based on data on adv... |
Title: Guided Safe Shooting: model based reinforcement learning with safety constraints Abstract: In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game. Yet, there are few success stories when it comes to deploying those algorithms to r... |
Title: Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy Abstract: Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging,... |
Title: A Note on the Convergence of Mirrored Stein Variational Gradient Descent under $(L_0,L_1)-$Smoothness Condition Abstract: In this note, we establish a descent lemma for the population limit Mirrored Stein Variational Gradient Method~(MSVGD). This descent lemma does not rely on the path information of MSVGD but r... |
Title: Great Expectations: Unsupervised Inference of Suspense, Surprise and Salience in Storytelling Abstract: Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. The t... |
Title: The Role of Machine Learning in Cybersecurity Abstract: Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discre... |
Title: Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets Abstract: In this report, we combine the idea of Wide ResNets and transfer learning to optimize the architecture of deep neural networks. The first improvement of the architecture is the use of all layers as information... |
Title: GiDR-DUN; Gradient Dimensionality Reduction -- Differences and Unification Abstract: TSNE and UMAP are two of the most popular dimensionality reduction algorithms due to their speed and interpretable low-dimensional embeddings. However, while attempts have been made to improve on TSNE's computational complexity,... |
Title: GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks Abstract: As one of the most popular machine learning models today, graph neural networks (GNNs) have attracted intense interest recently, and so does their explainability. Users are increasingly interested in a better... |
Title: EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL Abstract: Reinforcement learning (RL) in long horizon and sparse reward tasks is notoriously difficult and requires a lot of training steps. A standard solution to speed up the process is to leverage additional reward signal... |
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