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Found 9 papers, 3 papers with code
Overcoming Saturation in Density Ratio Estimation by Iterated Regularization
Estimating the ratio of two probability densities from finitely many samples, is a central task in machine learning and statistics.
Universal Physics Transformers
Deep neural network based surrogates for partial differential equations have recently gained increased interest.
G-Signatures: Global Graph Propagation With Randomized Signatures
Graph neural networks (GNNs) have evolved into one of the most popular deep learning architectures.
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
To close this gap, we introduce LaMAR, a new benchmark with a comprehensive capture and GT pipeline that co-registers realistic trajectories and sensor streams captured by heterogeneous AR devices in large, unconstrained scenes.
Hopular: Modern Hopfield Networks for Tabular Data
In experiments on small-sized tabular datasets with less than 1, 000 samples, Hopular surpasses Gradient Boosting, Random Forests, SVMs, and in particular several Deep Learning methods.
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Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value function, the critic.
Hopfield Networks is All You Need
The new update rule is equivalent to the attention mechanism used in transformers.
Immune Repertoire Classification
Multiple Instance Learning
+1
Modern Hopfield Networks and Attention for Immune Repertoire Classification
We show that the attention mechanism of transformer architectures is actually the update rule of modern Hopfield networks that can store exponentially many patterns.
The History of Mobile Augmented Reality
This document summarizes the major milestones in mobile Augmented Reality between 1968 and 2014.
Human-Computer Interaction
