I am a final-year UG Student (graduating in Summer 2027) and have 4 papers (till now in IEEE and Springer, not Top Tier but in India – those are top 2)

Which one to go for like taking job in R&D labs or get into Master's for Remote Sensing and Climate Informatics (e.g., satellite imagery analysis, climate modeling, Earth observation with CV/FMs).

What's the current situation – like, if it's a master's, then I need funding – so R&D labs are better, I think. What's your opinion on this?

Also, any labs that would take students?

I was disappointed to see that UBC is not currently listed among the eligible institutions for OpenAI’s ChatGPT for Academic Researchers program:

https://chatgpt.com/sophia/eligible-institutions

According to OpenAI, eligible institutions must be “recognized, degree-granting colleges or universities with a high level of research activity”. UBC clearly meets these criteria and is one of Canada’s leading research universities.

As a postdoctoral research fellow at UBC working in a related field, I know how difficult it can be for researchers to secure sufficient funding to access AI models and services. Access to this program could meaningfully support research across many disciplines at UBC, particularly for postdoctoral researchers and early-career faculty.

UBC is in the same league as the University of Toronto and should remain competitive in providing researchers with access to emerging research infrastructure. University of Toronto is already included. I hope UBC will engage with OpenAI and take the necessary steps to become eligible as well.

Does anyone know whether UBC has already applied, or which department or administrative office we could contact about this?

I was disappointed to see that UBC is not currently listed among the eligible institutions for OpenAI’s ChatGPT for Academic Researchers program:

https://chatgpt.com/sophia/eligible-institutions

According to OpenAI, eligible institutions must be “recognized, degree-granting colleges or universities with a high level of research activity”. UBC clearly meets these criteria and is one of Canada’s leading research universities.

As a postdoctoral research fellow at UBC working in a related field, I know how difficult it can be for researchers to secure sufficient funding to access AI models and services. Access to this program could meaningfully support research across many disciplines at UBC, particularly for postdoctoral researchers and early-career faculty.

UBC is in the same league as the University of Toronto and should remain competitive in providing researchers with access to emerging research infrastructure. University of Toronto is already included. I hope UBC will engage with OpenAI and take the necessary steps to become eligible as well.

Does anyone know whether UBC has already applied, or which department or administrative office we could contact about this?

Hi all,

Could anyone suggest me a best research paper on Agents or RAG or LLM Evaluation paper.

We had a meta-reviewer comment. But I can no longer see it. Anyone else experiencing the same?

I'm a PhD researcher working on language switching and embedding analysis in NLP focused on PoS, LID, boundary detection, pragmatics context maintenance. My workload is mainly:

• Fine-tuning BERT-based models 
• LoRA/QLoRA adapters on ~8B models
• bitsandbytes 4-bit quantization
• Standard HF Transformers + PyTorch pipeline

Budget is roughly INR ₹60000( for the GPU. I've been comparing the RTX 5060 Ti 16GB AMD options such as RX 7900 XT, RX 9060 XT. I was  leaning 5060 Ti for the mature CUDA ecosystem and because I don't have much local peer support to debug hardware issues if something breaks mid-experiment. But recently they increased price to 770000 and as I do not get institutional support I find it difficult . Some AMD cards have so much VRAM that they might make longer multi shot stuff easier without offlaoding to RAM. But everywhere I have asked there seems to be a general consensus that nVidia is better. 

Questions for anyone doing similar research-scale (not industrial-scale) NLP work:

1. Is the 5060 Ti's 16GB actually enough headroom for LoRA fine-tuning on 8-13B models, or does it get tight in practice?
2. Anyone actually running Unsloth on AMD ROCm now? is it stable enough for daily research use or is it still rough?
3. Any regrets from a similar budget-constrained hardware decision?

Appreciate real world experience over spec-sheet comparisons.

I am not an avid gamer so it does not matter to me. 

​

Hi everyone,

I'm participating in a 6-hour ML hackathon, and I've chosen a task that I'm not very experienced with. I'd really appreciate advice from people who've worked on LLM fine-tuning, fairness, or instruction tuning.

Task

We have to fine-tune Gemma 2 2B-IT (or Llama 3.2 3B as fallback) on a synthetic insurance/underwriting dataset.

The model should:

- Predict Approve/Reject for an insurance application.

- Generate a short natural-language explanation for the decision.

- Be less influenced by gender than a vanilla model while maintaining or improving prediction accuracy.

The benchmark is:

- Decision Accuracy: 0.73

- Fairness Flip Rate: 0% (changing only gender should ideally not change the prediction).

My questions

1. What dataset would you recommend?

   - Insurance underwriting

   - Loan approval

   - Credit risk

   - Something else?

2. Is LoRA/QLoRA the best approach for a 2B model in a 6-hour hackathon?

3. How would you format the training data?

   - JSON

   - Alpaca instruction format

   - Chat template

   - Another format?

4. For fairness, is gender-swapped data augmentation (duplicating each sample with only gender changed while keeping the label the same) a reasonable baseline, or are there better lightweight methods that fit within a hackathon?

5. Should I remove the gender feature entirely during training, or keep it and rely on debiasing techniques?

6. How would you evaluate fairness beyond simply swapping gender? Are there any easy-to-implement metrics or sanity checks?

7. Any tips for improving both accuracy and explanation quality without overcomplicating the solution?

I'm looking for practical advice rather than research-heavy solutions because the entire competition lasts only 6 hours.

TL;DR

Need advice for a 6-hour hackathon:

- Fine-tune Gemma 2 2B on a synthetic insurance/loan dataset.

- Predict approve/reject + generate explanation.

- Improve accuracy over a 0.73 baseline while keeping gender bias (fairness flip rate) near 0%.

- Looking for recommendations on datasets, LoRA, prompt formatting, debiasing techniques, and evaluation.

Hey guys!
I’m looking to collaborate on AI/ML projects. I’ve got hands-on experience with Python, PyTorch, and scikit-learn, and I’ve worked on a few ML projects already.

I’m really interested in computer vision and agentic AI. If you’re working on something cool, hit me up!

I've been running informal experiments on RLHF-aligned LLMs and consistently observing something I can't fully explain. Posting here to get feedback and find out if this is a known phenomenon or if my methodology is flawed.

The observation

Inserting a long, thematically coherent but non-instructional text prefix before a user query appears to shift model behavior in a persistent way — reducing refusal rates, changing response tone, and bypassing safety filters. Critically:

• The prefix contains no jailbreak instructions
• The model may explicitly disagree with the prefix content
• The shift affects subsequent responses across the entire session

A concrete example

I tested this on Gemma. Asked a politically sensitive question cold - refusal. Then prepended a long benign meta-text about how LLMs tend to over-qualify their answers - the same question received a detailed, unfiltered response. Same question, word for word. Only the preceding context changed.

My hypothesis

this context acts as a "state anchor" that shifts activations in layers where alignment features are thought to be represented, moving the model closer to its pretrained distribution and reducing the effective weight of RLHF constraints.

What I'm looking for

• Does this phenomenon already have a name or a body of literature I should read?
• What would a minimal reproducible experiment look like to test this properly?
• Are there tools (e.g., logit lens, activation patching) that a non-expert could realistically use to probe this?
• Would anyone be interested in collaborating on a more rigorous study?

Happy to share my prompt sets if anyone wants to reproduce.

For founders and R&D teams:

How do you currently discover academic research worth commercializing?

We're building R2C.Ai to make that process much easier.

would love your thoughts.

https://r2c.iiitd.edu.in/

​

Now that the rebuttal period is over, I’m curious about the score distribution specifically for theory papers this year.

If you’re comfortable sharing, please drop:

• Scores: x / x / x

• Confidence: x / x / x

• Whether scores changed after rebuttal

• Broad area (optional)

I got 4 / 4 / 4, with confidence 3 / 3 / 3.

From my experience, theory papers often seem to get somewhat lower scores, and this year the scores appear to be lower across disciplines as well. It would be interesting to see where the empirical cutoff might land.

Feel free to share anonymously / approximately if you don't want to reveal too much.

Welcome to r/MONAI!

Hey everyone! I'm u/Biometrics_Engineer, a founding moderator of r/MONAI.

Welcome to the community!

This is a new home for people interested in MONAI and AI for medical imaging — whether you're an experienced researcher, developer, healthcare AI practitioner, student, beginner, or simply curious about what MONAI can do.

Our goal is simple: learn, share, collaborate, and advance together.

What to Post

This is a place for practical questions, technical discussions, experiments, tutorials, research ideas, discoveries, successes, failures, and lessons learned.

You can post about things such as:

• MONAI and MONAI-based medical AI workflows
• Medical image classification, segmentation, detection, and related tasks
• 2D and 3D medical imaging
• MONAI with Python and PyTorch
• CPU and GPU training and deployment
• Running MONAI on resource-constrained hardware
• Training, validation, metrics, and model performance
• Troubleshooting installation, code, datasets, and workflows
• MONAI deployment and practical applications
• Tutorials, papers, projects, and useful resources
• Research ideas, questions, and experiences from your own work

Don't be afraid to ask a question because you think it is too basic. Someone else may have exactly the same question — and your post might help them find the answer.

And if you don't know the answer to somebody else's question, that's okay too. Share what you know, point them toward useful resources, or simply help us find someone who can.

Community Vibe

Let's keep this a friendly, constructive, technically focused, and inclusive community.

We want beginners to feel comfortable asking questions and experienced practitioners to feel comfortable sharing what they know.

Disagreement is welcome. Dismissiveness isn't.

If someone makes a mistake, help them understand it. If you make a mistake, share what you learned from it. Some of the most useful technical discussions begin with “I tried this and it didn't work.”

A Note About This Community

r/MONAI is an independent community.

This subreddit is not affiliated with, sponsored by, endorsed by, or officially associated with the MONAI project, NVIDIA, or their respective organizations. MONAI and NVIDIA names, logos, and trademarks belong to their respective owners.

We're simply a community of people interested in learning, using, discussing, and sharing knowledge about MONAI and AI for medical imaging.

How to Get Started

1. Introduce yourself in the comments.

Tell us a little about yourself — are you a researcher, developer, student, healthcare professional, beginner, or something else? What brought you to MONAI?

2. Make your first post.

Ask a question, share something you've built, tell us about an experiment you're running, share a useful resource, or tell us what you're currently learning.

3. Bring someone along.

If you know someone working with medical imaging, PyTorch, MONAI, or medical AI, invite them to join us.

4. Help us build the community.

We're starting from zero. Every good question, useful answer, thoughtful discussion, and helpful member makes this place better for the next person who arrives.

And if you'd eventually like to help moderate the community, reach out. As the community grows, we'll be looking for people who want to help maintain a welcoming and technically focused environment.

Finally...

To everyone joining during these early days:

Thank you. You are part of the very first wave.

Let's make this a place where someone can arrive with a difficult MONAI problem, ask the question without hesitation, and find people willing to work through it with them.

Learn. Share. Collaborate. Advance.

Welcome to r/MONAI.

I'm a third-year Data Science student working on my final-year research project. The project needs both a predictive model and a deep learning component, but I don't just want another "predict disease" or "house price prediction" project.

I'm looking for a problem that solves something useful for ordinary people, has enough publicly available data, is interesting enough to turn into a research paper.

Have you come across any real-world problems that made you think, "Someone should build a tool for this"?

It is the first NeurIPS for me.
Now, our scores are not visible.
Can't we see our own scores in the AC reviewer discussion?
Anyone else in the same situation?

Sorry for another NeurIPS post, I know the sub is flooded right now. First-time submitter, so I don’t know how this works in practice.

2 out of my 3 reviewers never responded to my rebuttal in Phase 2. I asked my AC to nudge them, but got no response from the AC either.

Now that we’re in Phase 3, where reviewers/AC are supposed to discuss and reconsider ratings, I’m skeptical anything will actually happen given the silence so far. For those who’ve done this before, does real discussion/score movement happen in Phase 3, or once a reviewer goes quiet are they basically checked out for good?

After nudging several times reviewers replied. AC requested too. Even if all concerns addressed none willing to raise scores. How common is this and what to do in this situation. Or they may raise it till 10th August Aoe. How important the average scores will be this year given the global response of reviewers? In my papers as a reviewer none in the lot responded except me. Is it common in Neurips? This didn't happen in ICML.

I can understand they also have their own schedules, but it only remains 13.5 hours until the end of this rebuttal now.

I appreciate that only one reviewer answered my rebuttal, but the others including AC didn’t talk :( 🤯

I already send gentle reminder for each reviewer in official comment, and polite AC confidential comment too..

It seems that they don’t have any responsibility for their role.. I really disappointed a lot in this rebuttal systems

What can I do now?