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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Related publications (211)

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Cross-lingual Automatic Speech Recognition Exploiting Articulatory Features

Petr Motlicek

Articulatory features (AFs) provide language-independent attribute by exploiting the speech production knowl-edge. This paper proposes a cross-lingual automatic speechrecognition (ASR) based on AF methods. Various neural network(NN) architectures are explo ...

2019

Improving life cycle-based exploration methods by coupling sensitivity analysis and metamodels

Thomas Bernard Paul Jusselme

Exploration methods combine parametric energy assessments and data visualization to support building designers at early design stages. When exploration methods come to Life-Cycle Assessment (LCA) and the Global Warming Potential (GWP) assessment, a larger ...

2019

Transfer Learning from Pre-trained BERT for Pronoun Resolution

Qianqian Qiao, Xingce Bao

The paper describes the submission of the team "We used bert!" to the shared task Gendered Pronoun Resolution (Pair pronouns to their correct entities). Our final submission model based on the fine-tuned BERT (Bidirectional Encoder Representations from Tra ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2019

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

Immanuel Chee Beng Koh

The spatial and formal conception of architecture, and thus its modes of design perception and representation, directly contributes to its machine-learnability; and consequently, its capacity in leveraging today's machine learning apparatus for design inno ...

EPFL2019

Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem

Maksym Andriushchenko

Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show t ...

2019

Named Entity Processing for Historical Texts

Maud Ehrmann, Matteo Romanello

Recognition and identification of real-world entities is at the core of virtually any text mining application. As a matter of fact, referential units such as names of persons, locations and organizations underlie the semantics of texts and guide their inte ...

2019

Transformer-Based Multi-lingual Sentence Embeddings

In this thesis, we present a transformers-based multi-lingual embedding model to represent sentences in different languages in a common space. To do so, our system uses the structure of a simplified transformer with a shared byte-pair encoding vocabulary f ...

2019

Learning embeddings: efficient algorithms and applications

Cijo Jose

Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...

EPFL2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

École Polytechnique Fédérale de Lausanne2018

Deep Learning Works in Practice. But Does it Work in Theory?

Rachid Guerraoui, Le Nguyen Hoang

Deep learning relies on a very specific kind of neural networks: those superposing several neural layers. In the last few years, deep learning achieved major breakthroughs in many tasks such as image analysis, speech recognition, natural language processin ...

2018