896 research outputs found

    On the Significance of Distance in Machine Learning

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    Avstandsbegrepet er grunnleggende i maskinlæring. Hvordan vi velger å måle avstand har betydning, men det er ofte utfordrende å finne et passende avstandsmål. Metrisk læring kan brukes til å lære funksjoner som implementerer avstand eller avstandslignende mål. Vanlige dyplæringsmodeller er sårbare for modifikasjoner av input som har til hensikt å lure modellen (adversarial examples, motstridende eksempler). Konstruksjon av modeller som er robuste mot denne typen angrep er av stor betydning for å kunne utnytte maskinlæringsmodeller i større skala, og et passende avstandsmål kan brukes til å studere slik motstandsdyktighet. Ofte eksisterer det hierarkiske relasjoner blant klasser, og disse relasjonene kan da representeres av den hierarkiske avstanden til klasser. I klassifiseringsproblemer som må ta i betraktning disse klasserelasjonene, kan hierarkiinformert klassifisering brukes. Jeg har utviklet en metode kalt /distance-ratio/-basert (DR) metrisk læring. I motsetning til den formuleringen som normalt anvendes har DR-formuleringen to gunstige egenskaper. For det første er det skala-invariant med hensyn til rommet det projiseres til. For det andre har optimale klassekonfidensverdier på klasserepresentantene. Dersom rommet for å konstruere modifikasjoner er tilstrekklig stort, vil man med standard adversarial accuracy (SAA, standard motstridende nøyaktighet) risikere at naturlige datapunkter blir betraktet som motstridende eksempler. Dette kan være en årsak til SAA ofte går på bekostning av nøyaktighet. For å løse dette problemet har jeg utviklet en ny definisjon på motstridende nøyaktighet kalt Voronoi-epsilon adversarial accuracy (VAA, Voronoi-epsilon motstridende nøyaktighet). VAA utvider studiet av lokal robusthet til global robusthet. Klassehierarkisk informasjon er ikke tilgjengelig for alle datasett. For å håndtere denne utfordringen har jeg undersøkt om klassifikasjonsbaserte metriske læringsmodeller kan brukes til å utlede klassehierarkiet. Videre har jeg undersøkt de mulige effektene av robusthet på feature space (egenskapsrom). Jeg fant da at avstandsstrukturen til et egenskapsrom trent for robusthet har større likhet med avstandsstrukturen i rådata enn et egenskapsrom trent uten robusthet.The notion of distance is fundamental in machine learning. The choice of distance matters, but it is often challenging to find an appropriate distance. Metric learning can be used for learning distance(-like) functions. Common deep learning models are vulnerable to the adversarial modification of inputs. Devising adversarially robust models is of immense importance for the wide deployment of machine learning models, and distance can be used for the study of adversarial robustness. Often, hierarchical relationships exist among classes, and these relationships can be represented by the hierarchical distance of classes. For classification problems that must take these class relationships into account, hierarchy-informed classification can be used. I propose distance-ratio-based (DR) formulation for metric learning. In contrast to the commonly used formulation, DR formulation has two favorable properties. First, it is invariant of the scale of an embedding. Secondly, it has optimal class confidence values on class representatives. For a large perturbation budget, standard adversarial accuracy (SAA) allows natural data points to be considered as adversarial examples. This could be a reason for the tradeoff between accuracy and SAA. To resolve the issue, I proposed a new definition of adversarial accuracy named Voronoi-epsilon adversarial accuracy (VAA). VAA extends the study of local robustness to global robustness. Class hierarchical information is not available for all datasets. To handle this challenge, I investigated whether classification-based metric learning models can be used to infer class hierarchy. Furthermore, I explored the possible effects of adversarial robustness on feature space. I found that the distance structure of robustly trained feature space resembles that of input space to a greater extent than does standard trained feature space.Doktorgradsavhandlin

    Geometric Learning on Graph Structured Data

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    Graphs provide a ubiquitous and universal data structure that can be applied in many domains such as social networks, biology, chemistry, physics, and computer science. In this thesis we focus on two fundamental paradigms in graph learning: representation learning and similarity learning over graph-structured data. Graph representation learning aims to learn embeddings for nodes by integrating topological and feature information of a graph. Graph similarity learning brings into play with similarity functions that allow to compute similarity between pairs of graphs in a vector space. We address several challenging issues in these two paradigms, designing powerful, yet efficient and theoretical guaranteed machine learning models that can leverage rich topological structural properties of real-world graphs. This thesis is structured into two parts. In the first part of the thesis, we will present how to develop powerful Graph Neural Networks (GNNs) for graph representation learning from three different perspectives: (1) spatial GNNs, (2) spectral GNNs, and (3) diffusion GNNs. We will discuss the model architecture, representational power, and convergence properties of these GNN models. Specifically, we first study how to develop expressive, yet efficient and simple message-passing aggregation schemes that can go beyond the Weisfeiler-Leman test (1-WL). We propose a generalized message-passing framework by incorporating graph structural properties into an aggregation scheme. Then, we introduce a new local isomorphism hierarchy on neighborhood subgraphs. We further develop a novel neural model, namely GraphSNN, and theoretically prove that this model is more expressive than the 1-WL test. After that, we study how to build an effective and efficient graph convolution model with spectral graph filters. In this study, we propose a spectral GNN model, called DFNets, which incorporates a novel spectral graph filter, namely feedback-looped filters. As a result, this model can provide better localization on neighborhood while achieving fast convergence and linear memory requirements. Finally, we study how to capture the rich topological information of a graph using graph diffusion. We propose a novel GNN architecture with dynamic PageRank, based on a learnable transition matrix. We explore two variants of this GNN architecture: forward-euler solution and invariable feature solution, and theoretically prove that our forward-euler GNN architecture is guaranteed with the convergence to a stationary distribution. In the second part of this thesis, we will introduce a new optimal transport distance metric on graphs in a regularized learning framework for graph kernels. This optimal transport distance metric can preserve both local and global structures between graphs during the transport, in addition to preserving features and their local variations. Furthermore, we propose two strongly convex regularization terms to theoretically guarantee the convergence and numerical stability in finding an optimal assignment between graphs. One regularization term is used to regularize a Wasserstein distance between graphs in the same ground space. This helps to preserve the local clustering structure on graphs by relaxing the optimal transport problem to be a cluster-to-cluster assignment between locally connected vertices. The other regularization term is used to regularize a Gromov-Wasserstein distance between graphs across different ground spaces based on degree-entropy KL divergence. This helps to improve the matching robustness of an optimal alignment to preserve the global connectivity structure of graphs. We have evaluated our optimal transport-based graph kernel using different benchmark tasks. The experimental results show that our models considerably outperform all the state-of-the-art methods in all benchmark tasks

    "Private Prediction Strikes Back!'' Private Kernelized Nearest Neighbors with Individual Renyi Filter

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    Most existing approaches of differentially private (DP) machine learning focus on private training. Despite its many advantages, private training lacks the flexibility in adapting to incremental changes to the training dataset such as deletion requests from exercising GDPR's right to be forgotten. We revisit a long-forgotten alternative, known as private prediction, and propose a new algorithm named Individual Kernelized Nearest Neighbor (Ind-KNN). Ind-KNN is easily updatable over dataset changes and it allows precise control of the R\'{e}nyi DP at an individual user level -- a user's privacy loss is measured by the exact amount of her contribution to predictions; and a user is removed if her prescribed privacy budget runs out. Our results show that Ind-KNN consistently improves the accuracy over existing private prediction methods for a wide range of ϵ\epsilon on four vision and language tasks. We also illustrate several cases under which Ind-KNN is preferable over private training with NoisySGD

    Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study

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    Our research investigates the recommendation of code examples to aid software developers, a practice that saves developers significant time by providing ready-to-use code snippets. The focus of our study is Stack Overflow, a commonly used resource for coding discussions and solutions, particularly in the context of the Java programming language. We applied BERT, a powerful Large Language Model (LLM) that enables us to transform code examples into numerical vectors by extracting their semantic information. Once these numerical representations are prepared, we identify Approximate Nearest Neighbors (ANN) using Locality-Sensitive Hashing (LSH). Our research employed two variants of LSH: Random Hyperplane-based LSH and Query-Aware LSH. We rigorously compared these two approaches across four parameters: HitRate, Mean Reciprocal Rank (MRR), Average Execution Time, and Relevance. Our study revealed that the Query-Aware (QA) approach showed superior performance over the Random Hyperplane-based (RH) method. Specifically, it exhibited a notable improvement of 20% to 35% in HitRate for query pairs compared to the RH approach. Furthermore, the QA approach proved significantly more time-efficient, with its speed in creating hashing tables and assigning data samples to buckets being at least four times faster. It can return code examples within milliseconds, whereas the RH approach typically requires several seconds to recommend code examples. Due to the superior performance of the QA approach, we tested it against PostFinder and FaCoY, the state-of-the-art baselines. Our QA method showed comparable efficiency proving its potential for effective code recommendation

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Pre-Trained Driving in Localized Surroundings with Semantic Radar Information and Machine Learning

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    Entlang der Signalverarbeitungskette von Radar Detektionen bis zur Fahrzeugansteuerung, diskutiert diese Arbeit eine semantischen Radar Segmentierung, einen darauf aufbauenden Radar SLAM, sowie eine im Verbund realisierte autonome Parkfunktion. Die Radarsegmentierung der (statischen) Umgebung wird durch ein Radar-spezifisches neuronales Netzwerk RadarNet erreicht. Diese Segmentierung ermöglicht die Entwicklung des semantischen Radar Graph-SLAM SERALOC. Auf der Grundlage der semantischen Radar SLAM Karte wird eine beispielhafte autonome Parkfunktionalität in einem realen Versuchsträger umgesetzt. Entlang eines aufgezeichneten Referenzfades parkt die Funktion ausschließlich auf Basis der Radar Wahrnehmung mit bisher unerreichter Positioniergenauigkeit. Im ersten Schritt wird ein Datensatz von 8.2 · 10^6 punktweise semantisch gelabelten Radarpunktwolken über eine Strecke von 2507.35m generiert. Es sind keine vergleichbaren Datensätze dieser Annotationsebene und Radarspezifikation öffentlich verfügbar. Das überwachte Training der semantischen Segmentierung RadarNet erreicht 28.97% mIoU auf sechs Klassen. Außerdem wird ein automatisiertes Radar-Labeling-Framework SeRaLF vorgestellt, welches das Radarlabeling multimodal mittels Referenzkameras und LiDAR unterstützt. Für die kohärente Kartierung wird ein Radarsignal-Vorfilter auf der Grundlage einer Aktivierungskarte entworfen, welcher Rauschen und andere dynamische Mehrwegreflektionen unterdrückt. Ein speziell für Radar angepasstes Graph-SLAM-Frontend mit Radar-Odometrie Kanten zwischen Teil-Karten und semantisch separater NDT Registrierung setzt die vorgefilterten semantischen Radarscans zu einer konsistenten metrischen Karte zusammen. Die Kartierungsgenauigkeit und die Datenassoziation werden somit erhöht und der erste semantische Radar Graph-SLAM für beliebige statische Umgebungen realisiert. Integriert in ein reales Testfahrzeug, wird das Zusammenspiel der live RadarNet Segmentierung und des semantischen Radar Graph-SLAM anhand einer rein Radar-basierten autonomen Parkfunktionalität evaluiert. Im Durchschnitt über 42 autonome Parkmanöver (∅3.73 km/h) bei durchschnittlicher Manöverlänge von ∅172.75m wird ein Median absoluter Posenfehler von 0.235m und End-Posenfehler von 0.2443m erreicht, der vergleichbare Radar-Lokalisierungsergebnisse um ≈ 50% übertrifft. Die Kartengenauigkeit von veränderlichen, neukartierten Orten über eine Kartierungsdistanz von ∅165m ergibt eine ≈ 56%-ige Kartenkonsistenz bei einer Abweichung von ∅0.163m. Für das autonome Parken wurde ein gegebener Trajektorienplaner und Regleransatz verwendet

    Efficient and Explainable Neural Ranking

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    The recent availability of increasingly powerful hardware has caused a shift from traditional information retrieval (IR) approaches based on term matching, which remained the state of the art for several decades, to large pre-trained neural language models. These neural rankers achieve substantial improvements in performance, as their complexity and extensive pre-training give them the ability of understanding natural language in a way. As a result, neural rankers go beyond term matching by performing relevance estimation based on the semantics of queries and documents. However, these improvements in performance don't come without sacrifice. In this thesis, we focus on two fundamental challenges of neural ranking models, specifically, ones based on large language models: On the one hand, due to their complexity, the models are inefficient; they require considerable amounts of computational power, which often comes in the form of specialized hardware, such as GPUs or TPUs. Consequently, the carbon footprint is an increasingly important aspect of systems using neural IR. This effect is amplified when low latency is required, as in, for example, web search. On the other hand, neural models are known for being inherently unexplainable; in other words, it is often not comprehensible for humans why a neural model produced a specific output. In general, explainability is deemed important in order to identify undesired behavior, such as bias. We tackle the efficiency challenge of neural rankers by proposing Fast-Forward indexes, which are simple vector forward indexes that heavily utilize pre-computation techniques. Our approach substantially reduces the computational load during query processing, enabling efficient ranking solely on CPUs without requiring hardware acceleration. Furthermore, we introduce BERT-DMN to show that the training efficiency of neural rankers can be improved by training only parts of the model. In order to improve the explainability of neural ranking, we propose the Select-and-Rank paradigm to make ranking models explainable by design: First, a query-dependent subset of the input document is extracted to serve as an explanation; second, the ranking model makes its decision based only on the extracted subset, rather than the complete document. We show that our models exhibit performance similar to models that are not explainable by design and conduct a user study to determine the faithfulness of the explanations. Finally, we introduce BoilerNet, a web content extraction technique that allows the removal of boilerplate from web pages, leaving only the main content in plain text. Our method requires no feature engineering and can be used to aid in the process of creating new document corpora from the web

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection

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    Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, systems are needed to quickly answer the question: What are the most similar bugs to a new one?, that is, efficiently find near-duplicates. It is thus natural to consider nearest neighbors search to tackle this problem and especially the well-known locality-sensitive hashing (LSH) to deal with large datasets due to its sublinear performance and theoretical guarantees on the similarity search accuracy. Surprisingly, LSH has not been considered in the crash bucketing literature. It is indeed not trivial to derive hash functions that satisfy the so-called locality-sensitive property for the most advanced crash bucketing metrics. Consequently, we study in this paper how to leverage LSH for this task. To be able to consider the most relevant metrics used in the literature, we introduce DeepLSH, a Siamese DNN architecture with an original loss function, that perfectly approximates the locality-sensitivity property even for Jaccard and Cosine metrics for which exact LSH solutions exist. We support this claim with a series of experiments on an original dataset, which we make available
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