Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Authors: Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally evaluated the approach on both synthetic and real-world tasks. For a program synthesis problem, we observe significant accuracy improvements over the baseline methods. More notably, for a software testing task, a fuzz test guided by an EBM achieves comparable performance to a well-engineered fuzzing engine on several open source software projects. |
| Researcher Affiliation | Industry | Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans Google Research, Brain Team {hadai, rising, bodai, charlessutton, schuurmans}@google.com |
| Pseudocode | Yes | Algorithm 1: Main algorithm of ALOE; Algorithm 2: Update sampler q |
| Open Source Code | Yes | Please also refer to our open sourced implementation for more details 1. 1https://github.com/google-research/google-research/tree/master/aloe |
| Open Datasets | Yes | We first collect synthetic 2D data in a continuous space [60]... We collect three software binaries (namely libpng, libmpeg2 and openjpeg) from OSS-Fuzz3 as test target... Data is generated synthetically, following Devlin et al. [3]. |
| Dataset Splits | No | The paper mentions "10k test examples" and "training data is generated on the fly" but does not provide specific training/validation/test splits or mention a validation set. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper does not specify software versions for any dependencies (e.g., Python, TensorFlow, PyTorch) used for implementation. |
| Experiment Setup | Yes | All the methods learn the same score function f, which is parameterized by a 4-layer MLP with ELU [61] activations and hidden layer size= 256. ... we train a truncated EBM with a window size of 64. ... We train baseline seq2seq with 483M examples (denoted as seq2seq-init), and fine-tune with additional 264M samples (denoted as seq2seq-tune) with reduced learning rate. |