Variance Networks: When Expectation Does Not Meet Your Expectations
Authors: Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that such layers can learn surprisingly well, can serve as an efficient exploration tool in reinforcement learning tasks and provide a decent defense against adversarial attacks. We also show that a number of conventional Bayesian neural networks naturally converge to such zero-mean posteriors. We observe that in these cases such zero-mean parameterization leads to a much better training objective than more flexible conventional parameterizations where the mean is being learned. 6 EXPERIMENTS We perform experimental evaluation of variance networks on classification and reinforcement learning problems. |
| Researcher Affiliation | Collaboration | Kirill Neklyudov Samsung-HSE Laboratory, National Research University Higher School of Economics Samsung AI Center Moscow k.necludov@gmail.com Dmitry Molchanov Samsung-HSE Laboratory, National Research University Higher School of Economics Samsung AI Center Moscow dmolch111@gmail.com Arsenii Ashukha Samsung AI Center Moscow ars.ashuha@gmail.com Dmitry Vetrov Samsung-HSE Laboratory, National Research University Higher School of Economics Samsung AI Center Moscow vetrovd@yandex.ru |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Experiments were implemented using Py Torch (Paszke et al. (2017)). The code is available at https://github.com/ da-molchanov/variance-networks. |
| Open Datasets | Yes | We consider three image classification tasks, the MNIST (Le Cun et al. (1998)), CIFAR-10 and CIFAR-100 (Krizhevsky & Hinton (2009)) datasets. |
| Dataset Splits | No | The paper mentions training and test sets but does not provide specific details on validation splits (percentages or sample counts), nor does it explicitly state the use of predefined standard validation splits for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch (Paszke et al. (2017))' and 'Caffe (2014)' but does not provide specific version numbers for the software dependencies needed for replication. |
| Experiment Setup | Yes | In all experiments the policy was approximated with a three layer fully-connected neural network containing 256 neurons on each hidden layer. Parameter noise and variance network policies had the second hidden layer to be parameter noise (Fortunato et al. (2017); Plappert et al. (2017)) and variance (Section 3) layer respectively. For both methods we made a gradient update for each episode with individual samples of noise per episode. Stochastic gradient learning is performed using Adam (Kingma & Ba (2014)). |