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DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein Daiwen Sun 1, He Huang 2,1, Y ao Li
We propose a novel neural network-based approach to modeling protein dynamics using an implicit representation of a protein's surface in 3D and time.
VarGrad ALow
We refer to this divergence as thelog-variance loss.
Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data
The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.
67496dfa96afddab795530cc7c69b57a-Supplemental-Conference.pdf
Theoptimalbaseline, however, israrelyusedinpractice (Sutton & Barto (2018); foran exception, see (Peters & Schaal, 2008)). Equation (1) thentakesthefollowingform: r E R(x)= E (R(x) B)r log (x).
On Reinforcement Learningand Distribution Matchingfor Fine-Tuning Language Models withno Catastrophic Forgetting
Twoofthemcanbecharacterizedas "Reward Maximization" (RM): Standard Policy Gradients (PG) and KL-control.
9b8b50fb590c590ffbf1295ce92258dc-Paper.pdf
The problem of learning the parameters of a neural network is two-fold. First, we want that their training on a set of data via minimization of a suitable loss function succeed in finding a set of parameters for which the value of the loss is close to its global minimum.
