Publications

Responsible Design Patterns for Machine Learning Pipelines

Saud Hakem Al Harbi

Lionel Nganyewou Tidjon

Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and respon… (voir plus)sible operation. AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as algorithm biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee ethical and fair outcomes. In this paper, we propose a comprehensive framework incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical development of AI systems. Our framework comprises new responsible AI design patterns for ML pipelines identified through a survey of AI ethics and data management experts and validated through real-world scenarios with expert feedback. The framework guides AI developers, data scientists, and policy-makers to implement ethical practices in AI development and deploy responsible AI systems in production.

2023-05-31

ArXiv (prépublication)

doi.org

arxiv.org

Spotlight Attention: Robust Object-Centric Learning With a Spatial Locality Prior

Ayush K Chakravarthy

Trang M. Nguyen

Anirudh Goyal

Yoshua Bengio

Michael Curtis Mozer

2023-05-31

ArXiv (prépublication)

doi.org

arxiv.org

A graphical user interface for calculating the arterial input function during dynamic positron emission tomography

Y. Daoud

Liam Carroll

Shirin A. Enger

Purpose. Dynamic positron emission tomography (dPET) requires the acquisition of the arterial input function (AIF), conventionally obtained … (voir plus)via invasive arterial blood sampling. To obtain the AIF non-invasively, our group developed and combined two novel solutions consisting of (1) a detector, placed on a patient’s wrist during the PET scans to measure the radiation leaving the wrist and (2) a Geant4-based Monte Carlo simulation software. The simulations require patient-specific wrist geometry. The aim of this study was to develop a graphical user interface (GUI) allowing the user to import 2D ultrasound scans of a patient’s wrist, and measure the wrist features needed to calculate the AIF. Methods. The GUI elements were implemented using Qt5 and VTK-8.2.0. The user imports a patient’s wrist ultrasound scans, measures the radial artery and veins’ surface and depth to model a wrist phantom, then specifies the radioactive source used during the dPET scan. The phantom, the source, and the number of decay events are imported into the Geant4-based Monte Carlo software to run a simulation. In this study, 100 million decays of 18F and 68Ga were simulated in a wrist phantom designed based on an ultrasound scan. The detector’s efficiency was calculated and the results were analyzed using a clinical data processing algorithm developed in a previous study. Results. The detector’s total efficiency decreased by 3.5% for 18F and by 51.7% for 68Ga when using a phantom based on ultrasound scans compared to a generic wrist phantom. Similarly, the data processing algorithm’s accuracy decreased when using the patient-specific phantom, giving errors greater than 1.0% for both radioisotopes. Conclusions. This toolkit enables the user to run Geant4-based Monte Carlo simulations for dPET detector development applications using a patient-specific wrist phantom. Leading to a more precise simulation of the developed detector during dPET and the calculation of a personalized AIF.

2023-05-30

Physics in Medicine & Biology (publié)

doi.org

The neuroconnectionist research programme

Adrien C. Doerig

R. Sommers

Katja Seeliger

Blake Richards

J. Ismael

Grace W. Lindsay

Konrad Paul Kording

Talia Konkle

M. Gerven

Nikolaus Kriegeskorte

Tim Kietzmann

2023-05-30

Nature Reviews Neuroscience (publié)

doi.org

arxiv.org

Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models

Zhong Zhang

Bang Liu

Junming Shao

2023-05-27

ArXiv (prépublication)

doi.org

arxiv.org

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

Dinghuai Zhang

Hanjun Dai

Nikolay Malkin

Aaron Courville

Yoshua Bengio

Ling Pan

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to appl… (voir plus)y machine learning methods. The highly structured constraints in these problems can hinder either optimization or sampling directly in the solution space. On the other hand, GFlowNets have recently emerged as a powerful machinery to efficiently sample from composite unnormalized densities sequentially and have the potential to amortize such solution-searching processes in CO, as well as generate diverse solution candidates. In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space. Efficient training techniques are also developed to benefit long-range credit assignment. Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlowNet policies can efficiently find high-quality solutions. Our implementation is open-sourced at https://github.com/zdhNarsil/GFlowNet-CombOpt.

2023-05-26

ArXiv (prépublication)

doi.org

arxiv.org

Motor cortex latent dynamics encode arm movement direction and urgency independently

Andrea Colins Rodriguez

Matt Perich

Lee Miller

Mark D. Humphries

2023-05-26

bioRxiv (prépublication)

doi.org