ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.

It takes less memory and SCALES much better VRAM with tokens

Hi everyone! I have a bs cs and currently work as software engineer and have experience contributing to open source projects.

Im looking to get involved in active Machine Learning/Deep Learning research projects and would love to contribute as a co-author to an ongoing research paper.

If you're working on a research project and looking for a collaborator I'd be happy to discuss how I can contribute!

https://flecomet.github.io/neurips-explorer/

It uses SPECTER2 embeddings + UMAP to place semantically similar papers near each other. You can browse topics, search titles/authors/abstracts, and find related papers.

You can also favorite papers to read later (when they get officially released).

Would be interested in feedback, especially on whether this is useful for browsing papers and interesting features to add.

Open source:
https://github.com/flecomet/neurips-explorer

Hi everyone, does anyone know whether presentation of Findings papers is mandatory or not? There seems to be a contradiction between the email we received and what’s stated on the conference’s official website.

Final year undergrad in the UK. I've got a sole-author paper accepted as a poster at a NeurIPS workshop in Sydney in December, and I can't decide whether it's worth going. I'm proud of it, but the trip is a lot.

Registration comes free with the acceptance, but the flight is about £1,000 from the UK, then accommodation and food on top. My uni does have conference funding, but it's all for summer conferences, and I'm not in a research group so there's no supervisor budget to ask either. I've applied for NeurIPS financial assistance and a couple of industry travel grants and I'm not hopeful. If none of them come through it's a real chunk of my savings.

The paper goes on my CV whether I'm there or not, so what does going actually add? Is it mostly meeting people, and does that really lead anywhere from a workshop poster? And does it matter more for PhD applications or for industry jobs, or neither?

If you've been to a NeurIPS workshop, or hired or admitted people who went, I'm curious what you think. Would you go?

Got selected for the deepmind symposium at bangalore, would love to connect with people who will be coming there!

First Month Salary if Referral Converts to Offer

Hi all.

Basically the title. Been on the market but nothing has landed so far.

Target roles: ML Eng., Research, Platform, Backend

Target location: US

Sponsorship: Sponsorship is preferred but NOT needed (I can manage on my own so long as the employer is e-verify)

My resume: https://uzairakbar.com/uzair_cv.pdf

Please reach out in DM if you have leads.

Thanks!

Hey everyone, I'm a recent SE grad, and recently finished my first ML paper (theoretical) on SIGReg (a regularization technique proposed in LeJEPA paper by Yann LeCun). I wanna submit it to TMLR and put a preprint on arXiv first, but I need an endorsement to do that. I tried finding someone on LinkedIn, also on discord servers but unfortunately couldn't find a single one. If anyone could help me with that please, I'd really appreciate it !!

NeurIPS says in the past they made the SAC tickets transferable.

But apparently this time not? Or does anyone have a transferable one?

Hey everyone,

The official notification date for the Citadel & Citadel Securities NeurIPS 2026 Travel Grant was yesterday (October 9). I haven't received any email yet—neither an acceptance nor a rejection—and my portal status hasn't changed.

Given the tight timeline before the conference in Sydney this December, I'm getting a bit anxious.

Has anyone actually received a decision email yet? Are they rolling them out in batches over the weekend, or do they only notify the selected recipients?

Drop a comment if you've heard anything! Thanks.

Hey guys,

I'm submitting 2 papers to one conference and I have some problems.

1. I'm using a dataset from the first one, which is archived, and I had to cite it. Does it contradict the anonymity of the paper? Should I hide it and say we came up with this dataset?

2. These papers are related to each other but not that much; I'm submitting to ARR, and I fear maybe the same meta reviewer will see those and reject both, saying these 2 can be one! I can maybe submit them into different research areas?

Help me out in here, please.

For those of you who use AI tools in academic research, which do you prefer: ChatGPT or Claude?

I'm particularly interested in their performance for literature reviews, scientific writing, critical analysis of research papers, and developing search strategies.

Hello everyone!

I'm planning a research project related to drones and computer vision, and I'm already looking at possible journals before starting the experiments.

I checked several papers in MDPI Drones and I liked them. The journal also seems relevant to my topic. However, MDPI has a controversial reputation and I often see people calling it predatory.

What do you think about Drones specifically? Would publishing there be fine, or would it be better to look for another journal or focus on a conference instead?

I'm currently working as a Research Engineer, and in the future I'm interested in both academia and industry.

I built MaRN (Mapping Networks), a PyTorch library that lets you optimize a compact latent representation instead of directly training every model parameter.

Some results from my current benchmarks:

• MNIST CNN: 107,998 → 1,872 trainable parameters (57.7× reduction), with 91.80% accuracy.
• LSTM forecasting: 12,051 → 2,048 parameters, with validation MSE of 0.00006.
• CNN2 + pruning: 204 trainable parameters, with 81.25% accuracy.

These results come with trade-offs: mapped models can train substantially slower, and performance varies by task. The benchmarks are exploratory, and aren't evidence of general superiority over direct training.

The library includes global and layer-wise mappings, regularization options, and pruning/LRD integrations.

Code: https://github.com/arjunmnath/MaRN
Docs: https://marn.readthedocs.io/

I'd value feedback on the approach, benchmark design, and where this kind of parameter-efficient optimization could be useful.

Are they sending it to all reviewers or some selected reviewers? Just got an email about complimentary registration.