Publications related to Kohonen Winner Takes All Neural Network Realized on Microcontrollers with AVR and ARM Cores

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

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Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

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Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

EPFL2023

From event-based surprise to lifelong learning.A journey in the timescales of adaptation

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Humans and animals constantly adapt to their environment over the course of their life. This thesis seeks to integrate various timescales of adaptation, ranging from the adaptation of synaptic connections between spiking neurons (milliseconds), rapid behav ...

EPFL2023

Deep Learning Generalization with Limited and Noisy Labels

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Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

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This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits.In the ...

EPFL2023

Bed Topography Inference from Velocity Field Using Deep Learning.

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Measuring bathymetry has always been a major scientific and technological challenge. In this work, we used a deep learning technique for inferring bathymetry from the depth-averaged velocity field. The training of the neural network is based on 5742 labora ...

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Algorithms for Efficient and Robust Distributed Deep Learning

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The success of deep learning may be attributed in large part to remarkable growth in the size and complexity of deep neural networks. However, present learning systems raise significant efficiency concerns and privacy: (1) currently, training systems are l ...

EPFL2022

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Maryam Kamgarpour, Andreas Krause

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Giuseppe Carleo

Neural-network quantum states (NQS) have been shown to be a suitable variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional systems using timedependent variational Monte Carlo (t-VMC). In particular, stable and accurate time propagat ...

SCIPOST FOUNDATION2022

Kohonen Winner Takes All Neural Network Realized on Microcontrollers with AVR and ARM Cores

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Data-driven approaches for non-invasive cuffless blood pressure monitoring

From event-based surprise to lifelong learning.A journey in the timescales of adaptation

Deep Learning Generalization with Limited and Noisy Labels

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Bed Topography Inference from Velocity Field Using Deep Learning.

Algorithms for Efficient and Robust Distributed Deep Learning

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

From event-based surprise to lifelong learning.A journey in the timescales of adaptation

Deep Learning Generalization with Limited and Noisy Labels

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Bed Topography Inference from Velocity Field Using Deep Learning.

Algorithms for Efficient and Robust Distributed Deep Learning

Role of stochastic noise and generalization error in the time propagation of neural-network quantum states

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation