Deep Learning Games
Authors: Dale Schuurmans, Martin A. Zinkevich
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To investigate the utility of these methods for supervised learning, we conducted experiments on synthetic data and on the MNIST data set [20]. |
| Researcher Affiliation | Collaboration | Dale Schuurmans Google daes@ualberta.ca Martin Zinkevich Google martinz@google.com Work performed at Google Brain while on a sabbatical leave from the University of Alberta. |
| Pseudocode | Yes | Algorithm 1 Main Loop, Algorithm 2 Regret Matching (RM), Algorithm 3 Exp. Weighted Average (EWA), Algorithm 4 Projected SGD |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | Yes | We conducted experiments on synthetic data and on the MNIST data set [20]. ... The third experiment was conducted on MNIST, which is an n = 10 class problem over m = 784 dimensional inputs with T = 60, 000 training examples, evidently not linearly separable. |
| Dataset Splits | No | The paper mentions 'training loss' and 'test loss' and refers to 60,000 training examples for MNIST, but it does not explicitly define a separate 'validation' split or its size, nor does it refer to a standard three-way split that includes validation. |
| Hardware Specification | No | The paper mentions a "Tensorflow implementation" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "Tensorflow implementation" but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | For this experiment, we used mini-batches of size 100. ... Here we chose the L1 constraint bound to be β = 10 and the initialization scale as σ = 100. For the nonlinear activation functions we used a smooth approximation of the standard Re LU gate fv(x) = τ log(1 + ex/τ) with τ = 0.5. ...RM was run with β = 30 and initialization scales (σ1, σ2, σ3) = (50, 200, 50). ...where RM was run with (β1, β2, β3, β4) = (30, 30, 30, 10) and initialization scales σ = 500. |