51 research outputs found

    Learning of Structurally Unambiguous Probabilistic Grammars

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    The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for structurally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using co-linear multiplicity tree automata (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a structurally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. We demonstrate the usefulness of our algorithm in learning PCFGs over genomic data

    Metropolitan Landscapes? Grappling with the urban in landscape design

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    On January 2016, a joint consortium of the Flemish and Brussels Chief Architects published Metropolitan Landscapes. Espaces ouvert, base de développement urbain/Open ruimte als basis voor stedelijke ontwikkeling. Based on the assumption that open spaces have the potential to spur and structure future urban development and surpass administrative boundaries, Metropolitan Landscapes presents research by design, authored by four prominent design firms with the intention of jumpstarting conversations about a shared spatial vision for the fragmented territory of Brussels and its periphery. In this article, we examine the methodology and definitions put forth by Bureau Bas Smets & List, explore the historical context that has rendered the landscape approach so promising in Brussels, and perform a thematic and critical reading of the four projects and their underlying rationale. These projects demonstrate the potential of landscape to engender novel territorial solutions. However, by choosing to ignore competing spatial claims and tending towards a techno-managerial rationale based on infrastructural and ecological systems, these designs raise questions as to the capacity of the landscape approach to deal with everpresent socio-political concerns in Brussels

    Metropolitan Landscapes? Grappling with the urban in landscape design

    Get PDF
    On January 2016, a joint consortium of the Flemish and Brussels Chief Architects published Metropolitan Landscapes. Espaces ouvert, base de développement urbain/Open ruimte als basis voor stedelijke ontwikkeling. Based on the assumption that open spaces have the potential to spur and structure future urban development and surpass administrative boundaries, Metropolitan Landscapes presents research by design, authored by four prominent design firms with the intention of jumpstarting conversations about a shared spatial vision for the fragmented territory of Brussels and its periphery. In this article, we examine the methodology and definitions put forth by Bureau Bas Smets & List, explore the historical context that has rendered the landscape approach so promising in Brussels, and perform a thematic and critical reading of the four projects and their underlying rationale. These projects demonstrate the potential of landscape to engender novel territorial solutions. However, by choosing to ignore competing spatial claims and tending towards a techno-managerial rationale based on infrastructural and ecological systems, these designs raise questions as to the capacity of the landscape approach to deal with everpresent socio-political concerns in Brussels

    Metropolitan Landscapes? Grappling with the urban in landscape design

    Get PDF
    On January 2016, a joint consortium of the Flemish and Brussels Chief Architects published Metropolitan Landscapes. Espaces ouvert, base de développement urbain/Open ruimte als basis voor stedelijke ontwikkeling. Based on the assumption that open spaces have the potential to spur and structure future urban development and surpass administrative boundaries, Metropolitan Landscapes presents research by design, authored by four prominent design firms with the intention of jumpstarting conversations about a shared spatial vision for the fragmented territory of Brussels and its periphery. In this article, we examine the methodology and definitions put forth by Bureau Bas Smets & List, explore the historical context that has rendered the landscape approach so promising in Brussels, and perform a thematic and critical reading of the four projects and their underlying rationale. These projects demonstrate the potential of landscape to engender novel territorial solutions. However, by choosing to ignore competing spatial claims and tending towards a techno-managerial rationale based on infrastructural and ecological systems, these designs raise questions as to the capacity of the landscape approach to deal with everpresent socio-political concerns in Brussels

    Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals

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    Causal explanations of the predictions of NLP systems are essential to ensure safety and establish trust. Yet, existing methods often fall short of explaining model predictions effectively or efficiently and are often model-specific. In this paper, we address model-agnostic explanations, proposing two approaches for counterfactual (CF) approximation. The first approach is CF generation, where a large language model (LLM) is prompted to change a specific text concept while keeping confounding concepts unchanged. While this approach is demonstrated to be very effective, applying LLM at inference-time is costly. We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space. This space is faithful to a given causal graph and effectively serves to identify matches that approximate CFs. After showing theoretically that approximating CFs is required in order to construct faithful explanations, we benchmark our approaches and explain several models, including LLMs with billions of parameters. Our empirical results demonstrate the excellent performance of CF generation models as model-agnostic explainers. Moreover, our matching approach, which requires far less test-time resources, also provides effective explanations, surpassing many baselines. We also find that Top-K techniques universally improve every tested method. Finally, we showcase the potential of LLMs in constructing new benchmarks for model explanation and subsequently validate our conclusions. Our work illuminates new pathways for efficient and accurate approaches to interpreting NLP systems

    Learning of Structurally Unambiguous Probabilistic Grammars

    Get PDF
    The problem of identifying a probabilistic context free grammar has two aspects: the first is determining the grammar's topology (the rules of the grammar) and the second is estimating probabilistic weights for each rule. Given the hardness results for learning context-free grammars in general, and probabilistic grammars in particular, most of the literature has concentrated on the second problem. In this work we address the first problem. We restrict attention to structurally unambiguous weighted context-free grammars (SUWCFG) and provide a query learning algorithm for \structurally unambiguous probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be represented using \emph{co-linear multiplicity tree automata} (CMTA), and provide a polynomial learning algorithm that learns CMTAs. We show that the learned CMTA can be converted into a probabilistic grammar, thus providing a complete algorithm for learning a structurally unambiguous probabilistic context free grammar (both the grammar topology and the probabilistic weights) using structured membership queries and structured equivalence queries. A summarized version of this work was published at AAAI 21
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