appliedAI — TransferLab

We identify, test and disseminate established and emerging techniques in machine learning in order to provide practitioners with the best tools for their applications

What we do

Trainings

Our hands-on, one to three day workshops cover fundamental and advanced topics in machine learning. We illustrate concepts with abundant real-world examples backed by rigorous theory.

Playbooks

Guided walkthroughs and industry-grade implementations of common tasks in machine learning

Reviews

In-depth analyses of the recently possible, as well as established techniques essential to the daily application of machine learning

Showcases

Visually rich, accessible explanations of novel concepts in machine learning, using open data

Software

Code libraries developed during our research and supporting our work, open sourced

Explore our work

Review

Data valuation techniques. Applications, advances and problems

The attribution of value to data points can help in data collection, active learning, gauging model robustness, improving model performance …

Data Valuation

May 15, 2021

Review

Calibration of Classifiers

We discuss the calibration of probabilistic classifiers, i.e. the question of whether the output vectors of such classifiers accurately …

Classifier Calibration

Apr 29, 2021

Software

pyDVL: the python Data Valuation Library

pyDVL strives to offer reference implementations of algorithms for data valuation with a common interface compatible with sklearn and a …

Data Valuation

Apr 3, 2021

Training

Calibration of modern classifiers

An introduction to the pitfalls of uncalibrated classifiers and modern techniques for (re)calibration.

Read our blog

The hidden assumptions and pitfalls of density based anomaly detection

Anomaly detection is a notoriously ill-defined problem. The notion of an anomaly is arguably subjective since it depends to some extent on …

Anomaly Detection

Aug 11, 2022

Our take on ICLR 2022 - Part 2

ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …

Jul 6, 2022

Our take on ICLR 2022 - Part 1

ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …

May 10, 2022

Certified error rates for neural networks

Adversarial training has been shown to improve robustness of neural networks to certain classes of data perturbations. Despite constant …

Robustness in ML

Jan 14, 2022

What we are reading:
Paper pills

Tesselation Filtering ReLu Networks

Sep 1, 2022

Relu networks define piecewise linear functions on tessellations of the input space. The authors analyse the shape complexity of the decision surface of a Relu neural network. The main contribution is a framework for decomposing a network along a specific layer into a tessellation and a shape complexity part which is represented by a tessellation-filtering neural network, a special subclass of Relu networks which is also identified in the paper.

Explainable AI

Modeling Long Sequences with Structured State Spaces

Aug 17, 2022

Building on ideas of using state-spaces and linear ODEs for sequence modelling in optimal control, a new layer for sequence processing is introduced and optimized in a line of papers. The theory behind this layer has overlaps with LSTMs and convolutional layers, in a sense containing them as special cases. It is demonstrated that the new layers can handle very long-range dependencies in sequences with over 10.000 tokens, while being much faster than transformers.

Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation

Aug 10, 2022

The development of versatile robots demands a better understanding of physics simulators. A recent line of research focuses on engines whose parameters can be back-propagated and fine-tuned to a given problem.

Simulators

Simulation in Robotics Opportunities Challenges Suggestions

Aug 5, 2022

Despite great advancements in physics based simulators for robotics over the last few years, a big sim2real gap remains in the performance of systems when tested in-silico and in the real world. A recent paper, summed up in this pill, tries to outline where the key challenges are and suggests how to tackle them.

Simulators

Variational- and Metric-based Deep Latent Space for Out-of-Distribution Detection

Aug 2, 2022

A new method for detection of out-of-distribution samples in classification with neural networks which does not require OOD training data.

Robustness in ML

What we do

Trainings

Our hands-on, one to three day workshops cover fundamental and advanced topics in machine learning. We illustrate concepts with abundant real-world examples backed by rigorous theory.

Playbooks

Guided walkthroughs and industry-grade implementations of common tasks in machine learning

Reviews

In-depth analyses of the recently possible, as well as established techniques essential to the daily application of machine learning

Showcases

Visually rich, accessible explanations of novel concepts in machine learning, using open data

Software

Code libraries developed during our research and supporting our work, open sourced

Explore our work

Data valuation techniques. Applications, advances and problems

The attribution of value to data points can help in data collection, active learning, gauging model robustness, improving model performance …

Calibration of Classifiers

We discuss the calibration of probabilistic classifiers, i.e. the question of whether the output vectors of such classifiers accurately …

pyDVL: the python Data Valuation Library

pyDVL strives to offer reference implementations of algorithms for data valuation with a common interface compatible with sklearn and a …

Calibration of modern classifiers

An introduction to the pitfalls of uncalibrated classifiers and modern techniques for (re)calibration.

Read our blog

The hidden assumptions and pitfalls of density based anomaly detection

Anomaly detection is a notoriously ill-defined problem. The notion of an anomaly is arguably subjective since it depends to some extent on …

Our take on ICLR 2022 - Part 2

ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …

Our take on ICLR 2022 - Part 1

ICLR is one of the most important and prestigious conferences in the ML community. It spans all ML topics, from embeddings to Graph neural …

Certified error rates for neural networks

Adversarial training has been shown to improve robustness of neural networks to certain classes of data perturbations. Despite constant …

What we are reading:Paper pills

The development of versatile robots demands a better understanding of physics simulators. A recent line of research focuses on engines whose parameters can be back-propagated and fine-tuned to a given problem.

A new method for detection of out-of-distribution samples in classification with neural networks which does not require OOD training data.

Contact us

What we are reading:
Paper pills