phuayj
/

neural-rule-inducer

@@ -32,10 +32,10 @@ NRI is a pretrained neural model that, given a small set of labelled Boolean exa
 | Field | Value |
 | --- | --- |
 | Architecture | Statistical literal encoder + parallel slot-based set decoder + t-norm/t-conorm aggregator |
-| Parameters | ~8.92 M |
 | Output | Interpretable DNF rule (T_max=8 clauses × K_max=4 literals each) |
 | Training data | Synthetic Boolean DNF episodes (no real-world labels) |
-| Training compute | 500 steps, batch size 8192, 1 × NVIDIA RTX 6000 Pro (96 GB), ~2.5 minutes |
 | Seed | 42 |
 The model is **pretrained**, not fine-tuned. It performs rule induction zero-shot at inference time on previously unseen tasks.
@@ -75,7 +75,7 @@ NRI is evaluated zero-shot on 14 UCI tabular benchmarks. **Direct comparison bet
 | Setting | Eval protocol | Seeds | Mean acc. |
 | --- | --- | --- | --- |
-| **This checkpoint (release reference)** | 5-fold CV; **1 fold (~20%) used as support, 4 folds (~80%) as query**; no subsampling | 1 (seed 42) | **75.60 %** |
 | **Paper Table 1** | 5-fold CV; **train portion subsampled to 5% before induction** (≈4% of total as support, 20% as query) | 10 | 69.7 % ± 12.0 |
 The released checkpoint has **roughly 5× more support data per fold** than the paper's protocol, which is the dominant reason its UCI accuracy is higher (+5.9 pp) than the paper's 69.7 %. The paper's protocol deliberately targets a low-data regime where zero-shot transfer is most valuable.

 | Field | Value |
 | --- | --- |
 | Architecture | Statistical literal encoder + parallel slot-based set decoder + t-norm/t-conorm aggregator |
+| Parameters | ≈8.92 M |
 | Output | Interpretable DNF rule (T_max=8 clauses × K_max=4 literals each) |
 | Training data | Synthetic Boolean DNF episodes (no real-world labels) |
+| Training compute | 500 steps, batch size 8192, 1 × NVIDIA RTX 6000 Pro (96 GB), ≈2.5 minutes |
 | Seed | 42 |
 The model is **pretrained**, not fine-tuned. It performs rule induction zero-shot at inference time on previously unseen tasks.
 | Setting | Eval protocol | Seeds | Mean acc. |
 | --- | --- | --- | --- |
+| **This checkpoint (release reference)** | 5-fold CV; **1 fold (≈20%) used as support, 4 folds (≈80%) as query**; no subsampling | 1 (seed 42) | **75.60 %** |
 | **Paper Table 1** | 5-fold CV; **train portion subsampled to 5% before induction** (≈4% of total as support, 20% as query) | 10 | 69.7 % ± 12.0 |
 The released checkpoint has **roughly 5× more support data per fold** than the paper's protocol, which is the dominant reason its UCI accuracy is higher (+5.9 pp) than the paper's 69.7 %. The paper's protocol deliberately targets a low-data regime where zero-shot transfer is most valuable.