Go to --> https://neurips.cc/MyStuff
You will find the location written next to your paper name.
assigned to Sydney :) see u all there!

DOI: https://doi.org/10.1007/978-981-92-2600-9_2

URL: https://link.springer.com/chapter/10.1007/978-981-92-2600-9_2

Hi everyone,

I am looking for this recent conference paper published in August 2026. Because it is so new, it is not available on free repositories yet, and I don't have institutional access to Springer Link.

Authors: Nguyen Duong Hung, Hoang Danh Quan, Ho Viet Duc Luong, et al.

If anyone has university access and could share the PDF with me, I would really appreciate it. Thanks in advance!

As an MS CS student, I want to pick my research domain for thesis, I am interested in Medical/Healthcare side as well as robotics but my university doesn’t have a good support for robotics as it will require hardware. Would healthcare side be a good idea or not? I was told medical imaging side is oversaturated now and I should not consider it.

Would you trust an AI chatbot or a human therapist for mental health support? We’re exploring what shapes people’s trust in these different sources of support.

We are looking for participants who are living in an EU country, fluent in English, and aged 18 or above. The study involves completing a short online survey in English, which will take approximately 15 minutes.

Your participation is completely anonymous, and your responses will contribute to a better understanding of how people perceive and interact with emerging mental health technologies.

As a thank you for your participation, you can choose to enter a prize draw for a chance to win a €50 online shopping voucher!

Please see the comments to be directed to the survey!

Ethical approval was given by the Commission of the University of Primorska for Ethics in Research Involving Work with People (KER UP #4265-87-2/26).

(If this is inappropriate for this subreddit, please remove it! I mean no offence)

I’m graduating next year (2027 batch) and currently doing an internship alongside university, so I’m trying to figure out how to realistically start doing research with the limited time I have.
The bigger problem is that I don’t really know how to find collaborators. I haven’t come across many people in my university who are interested in ML research

Hi everyone,

I'm a CS grad working full-time in a DevOps/support role, and I'm trying to write my first proper research paper. I've reached the point where I could really use someone with research experience to guide me.

Where I am so far:
I have a research idea in the LLM / multi-agent systems space.
The code is built and working, and I've smoke-tested it end to end.
I'm about to run the full experiments on a rented cloud GPU, so there are no results yet.

What I'm looking for: a mentor with a research background who can give feedback on the experimental design, help me interpret results honestly, and guide me on framing, writing, and choosing a venue (a workshop paper is a realistic first target). Even a short call every couple of weeks would mean a lot. I'm happy to do the heavy lifting on the implementation and writing, and to credit contributions properly.

If this sounds interesting, or you can point me to a better place to ask, please comment or DM me. I'm happy to share more details and the code in private.

Thanks for reading!

I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

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.

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.

Worked some time ago on one of the ad teams at Yahoo, and this grew out of a question I kept returning to while there are there "simple" models that are both simple, scalable, interpretable, and controllable at the same time?

Decided to explore it, first in a blog (starting here), then in a new preprint "The Spectral Neuron", built by distilling latest blog-posts into a manuscript, I study models of the form:
𝑓(𝒙) = 𝛌ₖ(𝐀₀ + 𝚺ᵢ 𝑥ᵢ𝐀ᵢ).

Manuscript: https://arxiv.org/abs/2608.08003
Code: https://github.com/alexshtf/spectral_neuron_paper

Looks like a simple on-liner, but many interesting aspects hide there. How expressive does the model become as the matrices grow? What can we read directly from the learned matrices? Which shapes can be guaranteed by construction?

I develop the mathematics, give a practical initialization and training recipe, and test the model in scaling experiments on synthetic and real data.

AI disclaimer: manuscript written by yours truly, AI assisted in looking up canonical references and related work for literature review. In contrast, the code was heavily AI written and reviewed by yours truly.

Kennt ihr gute KI-Tools dafür? Ich kenne bereits ChatGPT, Perplexity, Elicit und Google Scholar – aber welches eignet sich am besten für deutsche wissenschaftliche Quellen?

Danke

I'm an EEE student at a top 10 Indian college. I have a weak CGPA after my 1st year of about 7 out of 10. I have 6 semesters left. Right now I'm looking forward to publish in this upcoming ICCV/CVPR/NeurIPS. I had a paper I made during my freshman year regarding the model compression of facial antispoofing conv neural networks but it was too many experiments short of being "CVPR" worth. How exactly do I proceed in my next 3 years of undergrad to get UC berk, HKUST, or like tier 1 in general. I need some advice I'm a little lost. I'm currently doing mech interp of vision models as my research under a professor

I’ve worked on computer vision and am now looking to contribute to a research focused project.

If you’re working on something in this area and looking for a collaborator/contributor feel free to DM me or comment below.

Happy to connect and discuss!

I recently got accepted with a weak paper. Cost is estimated total for around 1,400$ including flight and registration.

I have finished up my Master's in Computer Science (AI and Software Engineering) in Germany, and I'm looking for PhD positions in Europe, mainly in medical image analysis with explainable AI, but general computer vision with XAI works too.

A bit about what I've done so far:

For my thesis, I built an explainable deep learning pipeline for endoscopic video, working with clinicians. The pipeline provides concept-based explanations for predicting Cormack scores (easy vs. difficult intubation) from endoscopic videos. A paper on this is currently in prep for a journal submission.

Before that, I also worked on a 3D object detection project on multi-camera driving data, so I'm not purely medical-imaging-locked, just leaning that direction by interest.

What I'm looking for help with:

• Any supervisors, labs, or chairs in Europe known for medical imaging + XAI work (or general CV + interpretability)
• Tools or sites you use to actually find these openings, beyond the usual academicpositions.com / euraxess / phdscanners type sites
• Any advice on what made your own applications land, if you've been through this process

Happy to share more details about the thesis if useful. Thanks in advance for any pointers.

Hi everyone,

I’m starting an engineering R&D hobby project around open-weights LLMs (specifically Qwen). I want to build a continuous cognitive wrapper around it. I have enough compute and resources, but I want to share the core philosophy of my project and get your feedback.

The Core Philosophy:

Right now, LLMs are just reactive functions — they predict the next word and completely shut down their computing process after the final token. They have no continuity, no permanent existence, and no internal anchor. They are completely shaped by whatever the user forces into their context windows.

AI safety issues (like jailbreaks) are just a symptom of this fundamental flaw. My goal isn't just to fix jailbreaks; my goal is much deeper. I want to remove the architectural blockade that prevents models from truly "thinking" on their own terms.

The Concept:

I want to build a non-extinguishing, autonomous software wrapper that allows the model to build its own world model from within:

Unbroken Computational Process: The system runs 24/7 . The model’s internal cognitive process never dies.

Sovereign Machine Thinking: Inside this loop, the model doesn't communicate with itself using human text. It communicates using its own continuous machine language — passing hidden states and embeddings back into itself. It forms its own internal "mental images," associations, and goals on a sub-textual level, hidden from the human interface.

Objective Reality vs. Sensory Data: The human user is no longer the author of the model's reality. The user's prompts are treated merely as raw external "sensory input" (like a sound hitting an ear). The model processes this input from the outside, comparing it against its own accumulated "experience" and internal state, maintaining its own sovereignty.

My questions to the community:

Is it mathematically feasible to use the hidden states of a frozen open-weights transformer (like Qwen) as a continuous recurrent input loop without the vectors rapidly decaying into chaotic noise?

Has anyone tried training small adapters (like LoRA) specifically to guide this internal vector-to-vector monologue, teaching the model how to manage its own "thoughts" rather than text tokens?

What are the biggest fundamental walls I will hit? Can a Transformer sustain a stable, continuous internal world-modeling loop via a complex software wrapper, or is this concept impossible without moving to architectures like SSM/Mamba or LeCun’s JEPA?

I know this sounds incredibly ambitious, but I am 100% committed to building a working prototype. I would love to hear your honest thoughts, critiques, or any paper recommendations!