Alexander Immer | DeepAI

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∙ 09/14/2023

Hodge-Aware Contrastive Learning

Simplicial complexes prove effective in modeling data with multiway depe...

0 Alexander Möllers, et al. ∙

∙ 06/06/2023

Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

Selecting hyperparameters in deep learning greatly impacts its effective...

8 Alexander Immer, et al. ∙

∙ 05/26/2023

Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference

Deep neural networks (DNNs) have found successful applications in many f...

0 Kouroche Bouchiat, et al. ∙

∙ 04/17/2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization

The linearized-Laplace approximation (LLA) has been shown to be effectiv...

0 Agustinus Kristiadi, et al. ∙

∙ 10/13/2022

On the Identifiability and Estimation of Causal Location-Scale Noise Models

We study the class of location-scale or heteroscedastic noise models (LS...

0 Alexander Immer, et al. ∙

∙ 02/22/2022

Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Data augmentation is commonly applied to improve performance of deep lea...

58 Alexander Immer, et al. ∙

∙ 10/15/2021

Probing as Quantifying the Inductive Bias of Pre-trained Representations

Pre-trained contextual representations have led to dramatic performance ...

0 Alexander Immer, et al. ∙

∙ 10/08/2021

Pathologies in priors and inference for Bayesian transformers

In recent years, the transformer has established itself as a workhorse i...

0 Tristan Cinquin, et al. ∙

∙ 06/28/2021

Laplace Redux – Effortless Bayesian Deep Learning

Bayesian formulations of deep learning have been shown to have compellin...

0 Erik Daxberger, et al. ∙

∙ 04/11/2021

Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning

Marginal-likelihood based model-selection, even though promising, is rar...

0 Alexander Immer, et al. ∙

∙ 08/19/2020

Improving predictions of Bayesian neural networks via local linearization

In this paper we argue that in Bayesian deep learning, the frequently ut...

0 Alexander Immer, et al. ∙

∙ 07/21/2020

Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks

In this thesis, we disentangle the generalized Gauss-Newton and approxim...

0 Alexander Immer, et al. ∙

∙ 04/29/2020

Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, ye...

17 Pingbo Pan, et al. ∙

∙ 06/17/2019

Variational Inference with Numerical Derivatives: variance reduction through coupling

The Black Box Variational Inference (Ranganath et al. (2014)) algorithm ...

0 Alexander Immer, et al. ∙

∙ 06/05/2019

Approximate Inference Turns Deep Networks into Gaussian Processes

Deep neural networks (DNN) and Gaussian processes (GP) are two powerful ...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 11/28/2017

Generative Interest Estimation for Document Recommendations

Learning distributed representations of documents has pushed the state-o...

0 Danijar Hafner, et al. ∙

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