r/ResearchML

I just finished a 4-year diploma in Bangladesh (after SSC, equivalent to HSC) and haven't started a bachelor's yet. I have a sole-author paper accepted as a poster in the NeurIPS 2026 main track in Sydney this December, and I can't decide whether the trip is worth it. I'm proud of it, but the trip is a lot.

Although NeurIps may give free registration and maybe 7 night hotel stay but he plan ticket is roughly 1600$ from dhaka to sydney and i did apply different organization and even tried to approach our education board to support me but there is no hope. And from my side to cover these is huge!!

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 poster? Does it matter more for undergrad admissions and scholarships, for PhD applications later, or for industry? I'm applying for fully funded BSc programs abroad, so I'd especially like to know whether showing up helps there.

One more thing: NeurIPS reserves the right to remove a paper from the proceedings if no author presents in person, and as the sole author nobody can present for me. Has anyone had a paper stay in the proceedings with a virtual presentation?

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

Hey guys i’m running a survey to study appalachian biases and how to improve perspective to better the appalachian community!

I would love for ANYONE who has a minute or two to fill it out to aid in my research!

Thank you from all of us here in Appalachia!

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?

Hi all,

I'm a med student in Portugal with no coding background. I've been learning R alone (R for Data Science and Irizarry's Introduction to Data Science, plus an epidemiology book). My goal is to become independent in data analysis for clinical research.

Tomorrow I'm meeting a professor who is an R specialist and works in clinical research, and I'd like to go in with good questions. A few things I'd love your input on:

1. Timeline: how long did it take you to be autonomous enough in R (cleaning data, descriptive stats, regression, plots, reproducible reports) to be trusted with real projects?

2)Paid work: does a market exist for med students or juniors doing paid statistical or data analysis for research groups, or is it mostly unpaid or authorship-based at this stage? If it does exist, what are typical rates (per hour or per project)?

3)What should I ask the professor? Especially considering I'm in Portugal: grants, research fellowships, summer internships, or ways to build a track record that leads to independence.

Any advice from people in research, biostatistics or medicine is very welcome. Thanks!

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

Update (Oct 11): Thanks for the feedback! I've re-scraped the NeurIPS website and expanded coverage from 6,231 to 9,110 papers. I've also added filters for conference location (Paris, Sydney, Atlanta) and presentation type (oral, spotlight, poster).

The UMAP projection has been recomputed with the additional papers, so some clusters have changed in relative size.

More improvements are planned. Feature suggestions are welcome!

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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

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

Reviewers need to document AI usage with their reviews, what are you actually writing? I am not very sure whether it’s better to say that you used it or not

Hi everyone,

I am an undergraduate student from Syria, sole author of an accepted paper at a NeurIPS workshop. I applied for the conference grant (covers hotel/registration), but flights and visa costs are a severe financial burden.

In-person vs Virtual: For future PhD/grad school admissions, is presenting a workshop poster in person worth this extreme financial and visa stress compared to virtual presentation?

Travel Grants: Are there affinity groups, workshop travel stipends, or funds that help cover flight tickets for low-income student authors from countries like Syria?

How did other researchers from unbanked/cash-based countries handle embassy proof-of-funds for conferences?

Appreciate your advice!

I recently finished The Principles of Diffusion Models, and honestly I think it’s exceptional.

The authors strike a really good balance between mathematical rigor and intuition, with dedicated appendices for anyone who wants to go deeper into the math.

It’s aimed at researchers, graduate students, and practitioners with basic deep learning knowledge, so you don’t need to already specialize in diffusion models (in my case, a strong background in Information and Probability Theory and a solid understanding of DDPMs helped me get more out of it).

Just wanted to share it in case anyone missed it. The full text is freely available on the official website.

Has anyone else read it? Would love to hear your thoughts.

In our #NeurIPS2026 paper “A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems (DS)” (preprint: https://arxiv.org/abs/2607.14937) we reduce a DS foundation model to the ingredients minimally necessary to faithfully reproduce long-term statistical and geometrical properties of DS:

1. A piecewise affine map with only a single (!!) parameter α that controls local con-/divergence rates, and …

2. … a context selector that chooses from the provided context signal the data point closest to the current state of the map, thus ensuring the generated dynamics stays close to the context in its temporal and geometrical properties.

With just these two mechanisms, this minimal form – which we coined DynaBase – can reproduce all major dynamical regimes, including fixed points (α<1), limit cycles (α=1), and chaotic attractors (α>1). Thus, unlike other simple mechanisms like context parroting, DynaBase even preserves the correct dynamical regime!

Surprisingly, it turns out that this simple context-driven 1-parameter map outperforms most major time series and DS foundation models, as well as custom-trained models, in both long-term statistics and even short-term predictions, even when run in zero-shot mode.

Both inference and training are extremely cheap – training can be done either analytically in one step by linear regression on forward-predictions, or by 1-parameter grid search directly on DS reconstruction objectives → this reveals interesting performance differences induced by different training mechanisms.

Most importantly in our minds, DynaBase owing to its formal simplicity may thus provide a tractable mathematical handle on analyzing, improving & understanding the performance and training of some time series and DS foundation models.

https://preview.redd.it/jmlcnocq5gth1.png?width=3000&format=png&auto=webp&s=1fc322e17a77575ddb0be3525482b45c70fa28f1

I have a model that I've been tuning on various train-validate splits, picked the best one and then evaluated against a sealed hold out set. While checking the results I noticed something off, and after reviewing my code I noticed a small bug in my code.

But now I'm not sure if I'm allowed to use the same sealed test to report my numbers. Because I used the results of the last run to improve my model, which introduces a small bias. If I had a different test set I probably would not have noticed the bug.

The bug in question was an off-by-one indexing error in a data ingestion stage.

I plan to write one sentence in my paper for transparency, but not sure if the editors/reviewers will flag this as a concern.

Looking for people interested in AI × Infrastructure × Sustainability research

I've been exploring an area that I find increasingly interesting: the environmental and infrastructure impact of AI, and what we could potentially do about it.

AI isn't just a software problem anymore. Behind every model are GPUs, data centers, electricity, cooling, water, networking, hardware manufacturing, and increasingly complex infrastructure.

I'm interested in exploring questions such as:

• How do we accurately measure the energy, water and carbon footprint of AI workloads?
• Can AI inference be made significantly more resource-efficient?
• Could workloads be scheduled based on energy availability, carbon intensity or water stress?
• Do we actually need large models for every task?
• Can smaller models, routing, caching, quantization, batching, etc. meaningfully reduce resource consumption?
• Can AI itself help optimize energy grids, water systems, agriculture, conservation and other environmental problems?
• What are the gaps in current research?
• What interesting research questions haven't been explored enough yet?

At this stage, I'm not trying to sell a specific idea or claim to have the answers.

I'd like to find people who are interested in:

• Reading and analyzing research papers
• Discussing existing findings and identifying gaps
• Challenging assumptions and ideas
• Brainstorming potential research questions
• Designing small experiments or prototypes
• Potentially collaborating on a research paper/project in the future

You don't necessarily need to be an academic researcher. I'm interested in people from AI/ML, backend/infrastructure, distributed systems, data centers, energy, sustainability, environmental science, or related fields.

I'm approaching this as an open exploration: research → discussion → experiments → ideas → potentially a paper/project.

If this sounds interesting to you, feel free to comment or DM me. I'd be happy to start with a small group and see where the discussions lead.

Would love to hear what problems you think are currently being overlooked in AI's environmental/infrastructure impact.

What does the paper imported deadline in neurips website mean? Do authors have to do anything for this?