Search Results for author:
Found 12 papers, 7 papers with code
Uncertainty Quantification via Stable Distribution Propagation
We propose a new approach for propagating stable probability distributions through neural networks.
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons.
Deep Differentiable Logic Gate Networks
Recently, research has increasingly focused on developing efficient neural network architectures.
Differentiable Top-k Classification Learning
In this work, we relax this assumption and optimize the model for multiple k simultaneously instead of using a single k. Leveraging recent advances in differentiable sorting and ranking, we propose a differentiable top-k cross-entropy classification loss.
Ranked #58 on
Image Classification
on ImageNet
GenDR: A Generalized Differentiable Renderer
In this work, we present and study a generalized family of differentiable renderers.
Monotonic Differentiable Sorting Networks
We introduce a family of sigmoid functions and prove that they produce differentiable sorting networks that are monotonic.
Learning with Algorithmic Supervision via Continuous Relaxations
The integration of algorithmic components into neural architectures has gained increased attention recently, as it allows training neural networks with new forms of supervision such as ordering constraints or silhouettes instead of using ground truth labels.
A Sampling-Free Approximation of Gaussian Variational Auto-Encoders
We propose a sampling-free approximate formulation of Gaussian variational auto-encoders.
Propagating Distributions through Neural Networks
We propose a new approach to propagating probability distributions through neural networks.
Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision
Sorting and ranking supervision is a method for training neural networks end-to-end based on ordering constraints.
$C^\infty$ Smooth Algorithmic Neural Networks for Solving Inverse Problems
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.
AlgoNet: $C^\infty$ Smooth Algorithmic Neural Networks
These networks integrate smooth versions of classic algorithms into the topology of neural networks.
