Concept
Symbolic artificial intelligence
Related publications (121)
Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep Learning
Michael Stefan Daniel Liebling, Adrian Shajkofci
Optical flow is a method aimed at predicting the movement velocity of any pixel in the image and is used in medicine and biology to estimate flow of particles in organs or organelles. However, a precise optical flow measurement requires images taken at hig ...
IEEE2021MATHICSE Technical Report : Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings
Fabio Nobile, Juan Pablo Madrigal Cianci
In this work, we present, analyze, and implement a class of Multi-Level Markov chain Monte Carlo (ML-MCMC) algorithms based on independent Metropolis-Hastings proposals for Bayesian inverse problems. In this context, the likelihood function involves solvin ...
MATHICSE2021Contextual semantic interpretability
Devis Tuia, Sylvain Lobry, Nicolas Courty
Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in t ...
LNCS2020Exact DAG-Aware Rewriting
We present a generic resynthesis framework for optimizing Boolean networks parameterized with a multi-level logic representation, a cut-computation algorithm, and a resynthesis algorithm. The framework allows us to realize powerful optimization algorithms ...
IEEE2020FEM Based Statistical Data-Driven Modeling Approach for MFT Design Optimization
This paper proposes a novel class of neural-network inspired statistical data-driven models, especially derived for the purpose of design optimization of medium frequency transformers. These models allow for an efficient (3-5 orders of magnitude faster com ...
2020Multilingual Training and Adaptation in Speech Recognition
State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...
EPFL2020Comparison of Subword Segmentation Methods for Open-vocabulary ASR using a Difficulty Metric
Philip Neil Garner, Claudiu-Cristian Musat
We experiment with subword segmentation approaches that are widely used to address the open vocabulary problem in the context of end-to-end automatic speech recognition (ASR). For morphologically rich languages such as German which has many rare words main ...
2020Learning and leveraging shared domain semantics to counteract visual domain shifts
One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...
EPFL2020Graph-to-Graph Transformer for Transition-based Dependency Parsing
James Henderson, Alireza Mohammadshahi
We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependenc ...
Association for Computational Linguistics2020