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Found 7 papers, 3 papers with code
Learning and Inference in Sparse Coding Models with Langevin Dynamics
Moreover we show that Langevin dynamics lead to an efficient procedure for sampling from the posterior distribution in the 'L0 sparse' regime, where latent variables are encouraged to be set to zero as opposed to having a small L1 norm.
Classification in the dark using tactile exploration
Combining information from different sensory modalities to execute goal directed actions is a key aspect of human intelligence.
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Touch sensing is widely acknowledged to be important for dexterous robotic manipulation, but exploiting tactile sensing for continuous, non-prehensile manipulation is challenging.
Novel deep learning methods for track reconstruction
The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.
High Energy Physics - Experiment Data Analysis, Statistics and Probability
Exascale Deep Learning for Climate Analytics
The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21. 0 PF/s and parallel efficiency of 79. 0%.
Distributed, Parallel, and Cluster Computing
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1.
Hamiltonian Monte Carlo Without Detailed Balance
We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection for typical hyperparameters.
