Sebastian Goldt | DeepAI

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∙ 06/21/2023

Quantifying lottery tickets under label noise: accuracy, calibration, and complexity

Pruning deep neural networks is a widely used strategy to alleviate the ...

0 Viplove Arora, et al. ∙

∙ 06/17/2023

The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions

Reinforcement learning (RL) algorithms have proven transformative in a r...

0 Nishil Patel, et al. ∙

∙ 05/18/2023

Attacks on Online Learners: a Teacher-Student Analysis

Machine learning models are famously vulnerable to adversarial attacks: ...

0 Riccardo Giuseppe Margiotta, et al. ∙

∙ 04/14/2023

Optimal inference of a generalised Potts model by single-layer transformers with factored attention

Transformers are the type of neural networks that has revolutionised nat...

0 Riccardo Rende, et al. ∙

∙ 11/21/2022

Neural networks trained with SGD learn distributions of increasing complexity

The ability of deep neural networks to generalise well even when they in...

11 Maria Refinetti, et al. ∙

∙ 05/29/2022

The impact of memory on learning sequence-to-sequence tasks

The recent success of neural networks in machine translation and other f...

0 Alireza Seif, et al. ∙

∙ 05/18/2022

Maslow's Hammer for Catastrophic Forgetting: Node Re-Use vs Node Activation

Continual learning - learning new tasks in sequence while maintaining pe...

0 Sebastian Lee, et al. ∙

∙ 02/01/2022

Data-driven emergence of convolutional structure in neural networks

Exploiting data invariances is crucial for efficient learning in both ar...

2 Alessandro Ingrosso, et al. ∙

∙ 01/06/2022

The dynamics of representation learning in shallow, non-linear autoencoders

Autoencoders are the simplest neural network for unsupervised learning, ...

0 Maria Refinetti, et al. ∙

∙ 07/09/2021

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity

Continual learning-the ability to learn many tasks in sequence-is critic...

0 Sebastian Lee, et al. ∙

∙ 06/07/2021

Representation mitosis in wide neural networks

Deep neural networks (DNNs) defy the classical bias-variance trade-off: ...

0 Diego Doimo, et al. ∙

∙ 05/16/2021

Bayesian reconstruction of memories stored in neural networks from their connectivity

The advent of comprehensive synaptic wiring diagrams of large neural cir...

0 Sebastian Goldt, et al. ∙

∙ 02/23/2021

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

A recent series of theoretical works showed that the dynamics of neural ...

0 Maria Refinetti, et al. ∙

∙ 02/16/2021

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model

Teacher-student models provide a powerful framework in which the typical...

6 Bruno Loureiro, et al. ∙

∙ 11/24/2020

The dynamics of learning with feedback alignment

Direct Feedback Alignment (DFA) is emerging as an efficient and biologic...

13 Maria Refinetti, et al. ∙

∙ 06/25/2020

The Gaussian equivalence of generative models for learning with two-layer neural networks

Understanding the impact of data structure on learning in neural network...

87 Sebastian Goldt, et al. ∙

∙ 09/25/2019

Modelling the influence of data structure on learning in neural networks

The lack of crisp mathematical models that capture the structure of real...

9 Sebastian Goldt, et al. ∙

∙ 06/18/2019

Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup

Deep neural networks achieve stellar generalisation even when they have ...

11 Sebastian Goldt, et al. ∙

∙ 01/25/2019

Generalisation dynamics of online learning in over-parameterised neural networks

Deep neural networks achieve stellar generalisation on a variety of prob...

12 Sebastian Goldt, et al. ∙

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