Bridging Neural and Symbolic Representations with Transitional Dictionary Learning
Authors: Junyan Cheng, Peter Chin
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on three abstract compositional visual object datasets, which require the model to utilize the compositionality of data instead of simply exploiting visual features. Then, three tasks on symbol grounding to predefined classes of parts and relations, as well as transfer learning to unseen classes, followed by a human evaluation, were carried out on these datasets. |
| Researcher Affiliation | Academia | Junyan Cheng Thayer School of Engineering Dartmouth College Hanover, NH 03755, USA jc.th@dartmouth.edu Peter Chin Thayer School of Engineering Dartmouth College Hanover, NH 03755, USA pc@dartmouth.edu |
| Pseudocode | Yes | Algorithm 1 Decompose an input |
| Open Source Code | Yes | Our code and data are available at https://github.com/chengjunyan1/TDL. |
| Open Datasets | Yes | Omni Glot (Lake et al., 2015) contains handwritten characters. Shape Net5 is composed of 3D shapes in 5 categories (bed, chair, table, sofa, lamp) from Shape Net (Chang et al., 2015) voxelized by binvox (Min, 2004 2019). |
| Dataset Splits | Yes | In total, we synthesize 50000 samples, which are divided into 8:1:1 splits for training, development, and testing. |
| Hardware Specification | Yes | We conducted our experiments on our internal clusters, and a major workload has the following configuration: six Quadro RTX 5000 GPUs and one Quadro RTX 8000 GPU, along with an Intel (R) Xeon (R) Silver 4214R CPU @ 2.40GHz and 386 GB RAM. |
| Software Dependencies | No | The paper mentions software like PyTorch Lightning and Weights & Biases Sweep but does not provide specific version numbers for these or other key software components. |
| Experiment Setup | Yes | Table 3: Hyper-parameter search distributions. lr {1e 3, 2e 3, 5e 4} th S U(0.1, 0.5; 0.05) or None K U(3, 9; 1) quota U(8, 32; 4) αoverlap U(0.1, 0.25) demb {64,128,256,512} αresources U(0.05, 0.2) dsampler {32,64} γcluster U(5e 3, 2e 2) dmapper {128,256,512} σ {2.5,5,10,15,25} |