Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface
Authors: Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, Siddharth N, Samuel Gershman, Joshua B. Tenenbaum
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods. |
| Researcher Affiliation | Academia | 1MIT, 2Cornell University, 3University of Edinburgh & The Alan Turing Institute, 4Harvard University |
| Pseudocode | Yes | Algorithm 1 Hybrid Memoised Wake-Sleep (a single learning iteration) |
| Open Source Code | No | No explicit statement or link providing concrete access to source code for the methodology was found. |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was found. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment were found. |
| Experiment Setup | Yes | For training, we use the Adam optimizer with default hyperparameters. and Fixed parameters: Vocabulary V = {WN, SE, PER1, . . . , PER4, C, , +, (, )} Vocabulary size vocabulary_size = |V| = 11 Kernel parameters K = {σ2 WN, (σ2 SE, ℓ2 SE), (σ2 PER1, p PER1, ℓ2 PER1), . . . , (σ2 PER4, p PER4, ℓ2 PER4), σ2 C} Hidden size hidden_size = 128 Observation embedding size obs_embedding_size = 128 and Number of allowed primitives to learn P = 5 Maximum number of blocks per cell Bmax = 3 Number of cells in the x-plane = number of cells in the z-plane = N = 2 Image resolution I = [3, 128, 128] Observation embedding size obs_embedding_size = 676 |