Publications associées à Standard ML

Advancing first principle-based molecular dynamics of biological systems with machine learning

Molecular dynamics (MD) simulations have emerged as a transformative approach to analyse molecular systems at the atomic level, offering valuable insights into complex biological processes. Many biological phenomena can only accurately be described by inco ...

EPFL2023

The splay-list: a distribution-adaptive concurrent skip-list

Amirkeivan Mohtashami, Dan Alistarh

The design and implementation of efficient concurrent data structures has seen significant attention. However, most of this work has focused on concurrent data structures providing good worst-case guarantees, although, in real workloads, objects are often ...

SPRINGER2023

On the advantages of P2P ML on mobile devices

Rachid Guerraoui, Alexandre David Olivier Maurer

Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, in- cluding the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against ...

ACM2022

A case for DOT: Theoretical Foundations for Objects with Pattern Matching and GADT-Style Reasoning

Yichen Xu, Lionel Emile Vincent Parreaux, Aleksander Slawomir Boruch-Gruszecki

Many programming languages in the OO tradition now support pattern matching in some form. Historical examples include Scala and Ceylon, with the more recent additions of Java, Kotlin, TypeScript, and Flow. But pattern matching on generic class hierarchies ...

Assoc Computing Machinery2022

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Fast screening of covariates in population models empowered by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervise ...

2021

Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

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 ...

EPFL2020

Ductility reduction factor formulations for seismic design of RC wall and frame structures

Katrin Beyer, Matteo Zerbin

Seismic design of standard structures is typically founded on a force-based design approach. Over the years this approach has proven robust and easily applicable by design engineers and - in combination with capacity design principles - it provides a good ...

2019

Contract-Based Resource Verification for Higher-Order Functions with Memoization

Viktor Kuncak, Ravichandhran Kandhadai Madhavan, Sumith Kulal

We present a new approach for specifying and verifying resource utilization of higher-order functional programs that use lazy evaluation and memoization. In our approach, users can specify the desired resource bound as templates with numerical holes e.g. a ...

Assoc Computing Machinery2017

Interactive Programming by Example

Mikaël Mayer

As of today, programming has never been so accessible. Yet, it remains a challenge for end-users: students, non-technical employees, experts in their domains outside of computer science, and so on. With its forecast potential for solving problems by only o ...

EPFL2017