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Found 18 papers, 8 papers with code
Conformal Prediction via Regression-as-Classification
Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed.
Variational Learning is Effective for Large Deep Networks
We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks.
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training.
Model Merging by Uncertainty-Based Gradient Matching
Models trained on different datasets can be merged by a weighted-averaging of their parameters, but why does it work and when can it fail?
The Lie-Group Bayesian Learning Rule
This simplifies all three difficulties for many cases, providing flexible parametrizations through group's action, simple gradient computation through reparameterization, and updates that always stay on the manifold.
SAM as an Optimal Relaxation of Bayes
Sharpness-aware minimization (SAM) and related adversarial deep-learning methods can drastically improve generalization, but their underlying mechanisms are not yet fully understood.
Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable Approach for Continuous Markov Random Fields
In contrast to existing discretizations which suffer from a grid bias, we show that a piecewise polynomial discretization better preserves the continuous nature of our problem.
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning
Structured convex optimization on weighted graphs finds numerous applications in machine learning and computer vision.
Informative GANs via Structured Regularization of Optimal Transport
We tackle the challenge of disentangled representation learning in generative adversarial networks (GANs) from the perspective of regularized optimal transport (OT).
Flat Metric Minimization with Applications in Generative Modeling
We take the novel perspective to view data not as a probability distribution but rather as a current.
Lifting Vectorial Variational Problems: A Natural Formulation based on Geometric Measure Theory and Discrete Exterior Calculus
Numerous tasks in imaging and vision can be formulated as variational problems over vector-valued maps.
Controlling Neural Networks via Energy Dissipation
The last decade has shown a tremendous success in solving various computer vision problems with the help of deep learning techniques.
Combinatorial Preconditioners for Proximal Algorithms on Graphs
We present a novel preconditioning technique for proximal optimization methods that relies on graph algorithms to construct effective preconditioners.
Proximal Backpropagation
Specifically, we show that backpropagation of a prediction error is equivalent to sequential gradient descent steps on a quadratic penalty energy, which comprises the network activations as variables of the optimization.
Sublabel-Accurate Discretization of Nonconvex Free-Discontinuity Problems
In this work we show how sublabel-accurate multilabeling approaches can be derived by approximating a classical label-continuous convex relaxation of nonconvex free-discontinuity problems.
Sublabel-Accurate Convex Relaxation of Vectorial Multilabel Energies
Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.
Sublabel-Accurate Relaxation of Nonconvex Energies
We propose a novel spatially continuous framework for convex relaxations based on functional lifting.
The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings
This paper deals with the analysis of a recent reformulation of the primal-dual hybrid gradient method [Zhu and Chan 2008, Pock, Cremers, Bischof and Chambolle 2009, Esser, Zhang and Chan 2010, Chambolle and Pock 2011], which allows to apply it to nonconvex regularizers as first proposed for truncated quadratic penalization in [Strekalovskiy and Cremers 2014].
