Concept
Multi-pass compiler
Publications associées (17)
Exploration of Hyperdimensional Computing Strategies for Enhanced Learning on Epileptic Seizure Detection
David Atienza Alonso, Tomas Teijeiro Campo, Una Pale
Wearable and unobtrusive monitoring and prediction of epileptic seizures has the potential to significantly increase the life quality of patients, but is still an unreached goal due to challenges of real-time detection and wearable devices design. Hyperdim ...
2022Scaffolding protein functional sites using deep learning
Bruno Emanuel Ferreira De Sousa Correia, Karla Montserrat Castro Gilabert, Basile Isidore Martin Wicky, Jue Wang
The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing ...
AMER ASSOC ADVANCEMENT SCIENCE2022Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...
2020Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We rst investigate regularized algorithms adapted to a projection operator on a closed subspace ...
2020Compiler Generation for Performance-Oriented Embedded DSLs (Short Paper)
Christoph Koch, Amir Shaikhha, Vojin Jovanovic
In this paper, we present a framework for generating optimizing compilers for performance-oriented embedded DSLs (EDSLs). This framework provides facilities to automatically generate the boilerplate code required for building DSL compilers on top of the ex ...
ASSOC COMPUTING MACHINERY2019Coherent rendering of virtual smile previews with fast neural style transfer
Coherent rendering in augmented reality deals with synthesizing virtual content that seamlessly blends in with the real content. Unfortunately, capturing or modeling every real aspect in the virtual rendering process is often unfeasible or too expensive. W ...
IEEE2019Spatial: A Language and Compiler for Application Accelerators
Christos Kozyrakis, Yunqi Zhang
Industry is increasingly turning to reconfigurable architectures like FPGAs and CGRAs for improved performance and energy efficiency. Unfortunately, adoption of these architectures has been limited by their programming models. HDLs lack abstractions for pr ...
ASSOC COMPUTING MACHINERY2018Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...
2018Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes over th ...
2018Algorithmic Resource Verification
Ravichandhran Kandhadai Madhavan
Static estimation of resource utilisation of programs is a challenging and important problem with numerous applications. In this thesis, I present new algorithms that enable users to specify and verify their desired bounds on resource usage of functional p ...
EPFL2017