Looking for critical review of an NN architecture (possible evaluation bias?) [D](reddit.com)

Hi everyone, I’m an amateur student who has been experimenting with neural networks mostly out of curiosity. Over the past few weeks, I ended up going fairly deep into a specific architecture I designed, which I call a Directional Neural Network (DirNN). This isn’t meant as a polished or formal contribution — it’s something I’ve been tinkering with, iterating on, and testing in my spare time. That said, the architecture does impose real structural constraints and uses a custom backward pass. In my own experiments on simple tasks (including some using GloVe embeddings), the DirNN has repeatedly performed better than standard MLP baselines. This result has been consistent enough that I don’t think it’s pure luck — but I’m very aware that I might be fooling myself. What I’m unsure about is whether I’ve been unfair in my comparisons. I don’t know if: the DirNN is effectively a special or degenerate case of an MLP my training procedure, initialization, or optimizer choices favor it in subtle ways the tasks or datasets I’m using make the comparison misleading I’ve put together a small repository with a README describing the architecture, the custom backward pass, and a minimal script to reproduce what I’m seeing. I’m posting here because I could really use a sanity check from people more experienced than me. If this is obviously flawed, I’d much rather learn that now. Blunt technical criticism, references, or “you’re missing X” comments are all very welcome. Repository: DirNNs Thanks for reading — I’m genuinely here to learn. submitted by /u/jos_lucas73 [link] [Kommentare]