A Dynamical View of the Question of Why
Authors: Mehdi Fatemi, Sindhu C. M. Gowda
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in fairly intricate experiments and through sheer learning, our framework reveals and quantifies causal links, which otherwise seem inexplicable. and 5 EXPERIMENTS |
| Researcher Affiliation | Collaboration | 1 Microsoft Research, 2 University of Toronto and Vector Institute |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor structured steps formatted like code. |
| Open Source Code | Yes | Our code and pretrained models to replicate the analysis (including figures) presented in this paper is publicly available at: https://github.com/fatemi/dynamical-causality. |
| Open Datasets | No | For the optimizer, we used Pytorch s implementation of Adam optimizer with the Huber loss function, and the results are obtained after training over 200 epochs of 250,000 steps each (each action is repeated 4 times, hence each epoch involves one million Atari frames). and For the remaining 64 samples, we choose 32 samples uniformly from the train data and append it with 6 uniformly selected event B, hypoglycemia transitions (r = 1 and terminal state = True), 2 uniformly selected hyperglycemia transitions (r = 0 and terminal state = True), and remaining samples are sampled from non-zero action samples. (While "train data" is mentioned, no specific split percentages or methodology for generating this data from the simulator or a larger dataset is provided to ensure reproducibility of the exact data partitions.) |
| Dataset Splits | No | No explicit mention of a 'validation' set or its specific split information (percentages, sample counts, or methodology) was found. |
| Hardware Specification | No | No specific hardware details (such as exact GPU/CPU models, processor types, or memory) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions software like Pytorch but does not provide specific version numbers for these or other key software components, which is necessary for reproducible software dependencies. |
| Experiment Setup | Yes | Both our network architecture and the base pipeline have the same structure as the original DQN paper Mnih et al. (2015). In particular, there are 3 convolutional layers followed by 2 fully-connected linear layers... For the optimizer, we used Pytorch s implementation of Adam optimizer with the Huber loss function, and the results are obtained after training over 200 epochs of 250,000 steps each... We use a simple deep network with 3 fully connected layers with GELU (Gaussian error linear unit) activation. In particular, we have 13 30, 30 30, and 30 1 fully connected layers... We use a learning rate of 0.00001 and minibatch size of 128. |