Search Results for author:
Found 9 papers, 3 papers with code
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
The performance of trained neural networks is robust to harsh levels of pruning.
Temporal Abstractions-Augmented Temporally Contrastive Learning: An Alternative to the Laplacian in RL
In reinforcement learning, the graph Laplacian has proved to be a valuable tool in the task-agnostic setting, with applications ranging from skill discovery to reward shaping.
Flexible Learning of Sparse Neural Networks via Constrained $L_0$ Regularization
We propose to approach the problem of learning $L_0$-sparse networks using a constrained formulation of the optimization problem.
Exploration-Driven Representation Learning in Reinforcement Learning
In this work, we introduce a method that explicitly couples representation learning with exploration when the agent is not provided with a uniform prior over the state space.
Combining adaptive algorithms and hypergradient method: a performance and robustness study
Wilson et al. (2017) showed that, when the stepsize schedule is properly designed, stochastic gradient generalizes better than ADAM (Kingma & Ba, 2014).
A3T: Adversarially Augmented Adversarial Training
Recent research showed that deep neural networks are highly sensitive to so-called adversarial perturbations, which are tiny perturbations of the input data purposely designed to fool a machine learning classifier.
Image Segmentation by Iterative Inference from Conditional Score Estimation
We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.
On Random Weights for Texture Generation in One Layer Neural Networks
Recent work in the literature has shown experimentally that one can use the lower layers of a trained convolutional neural network (CNN) to model natural textures.
Diet Networks: Thin Parameters for Fat Genomics
It is based on the idea that we can first learn or provide a distributed representation for each input feature (e. g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units).
