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Found 10 papers, 2 papers with code
E3x: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy
This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group $\mathrm{E}(3)$, consisting of translations, rotations, and reflections of three-dimensional space.
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes.
The Impact of Reinitialization on Generalization in Convolutional Neural Networks
Recent results suggest that reinitializing a subset of the parameters of a neural network during training can improve generalization, particularly for small training sets.
Deep Learning Through the Lens of Example Difficulty
Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers.
What Do Neural Networks Learn When Trained With Random Labels?
We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.
Exact marginal inference in Latent Dirichlet Allocation
We generalize this algorithm to the case of sparse probabilities $\beta(w|z)$, in which we only need to assume that the tree width of an "interaction graph" on the observations is limited.
Fourier networks for uncertainty estimates and out-of-distribution detection
A simple method for obtaining uncertainty estimates for Neural Network classifiers (e. g. for out-of-distribution detection) is to use an ensemble of independently trained networks and average the softmax outputs.
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation.
Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem
Temporal-Difference learning (TD) [Sutton, 1988] with function approximation can converge to solutions that are worse than those obtained by Monte-Carlo regression, even in the simple case of on-policy evaluation.
Gradient Descent Quantizes ReLU Network Features
Deep neural networks are often trained in the over-parametrized regime (i. e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem.
