Friction
37 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
bioinf-jku/TTUR
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NeurIPS 2017
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
Robust Adversarial Reinforcement Learning
Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL).
Stochastic Gradient Hamiltonian Monte Carlo
JavierAntoran/Bayesian-Neural-Networks
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Hamiltonian Monte Carlo (HMC) sampling methods provide a mechanism for defining distant proposals with high acceptance probabilities in a Metropolis-Hastings framework, enabling more efficient exploration of the state space than standard random-walk proposals.
Automatic Latent Fingerprint Segmentation
We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet.
Machine Learning and System Identification for Estimation in Physical Systems
The main approach to estimation and learning adopted is optimization based.
DeepNeuro: an open-source deep learning toolbox for neuroimaging
Translating neural networks from theory to clinical practice has unique challenges, specifically in the field of neuroimaging.
Learning Object Manipulation Skills from Video via Approximate Differentiable Physics
petrikvladimir/video_skills_learning_with_approx_physics
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We evaluate our approach on a 3D reconstruction task that consists of 54 video demonstrations sourced from 9 actions such as pull something from right to left or put something in front of something.
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment.
Reinforcement Learning for Pivoting Task
In this work we propose an approach to learn a robust policy for solving the pivoting task.
Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization
We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization.
