Hey, I know this sounds a bit ridiculous and maybe it is, but one of the ways I am trying to get a high score in my ML class at school is to have some of my work get published or something adjacent to that, as the professor specifically benchmarked that as a criteria for giving out higher scores. How can I go about this? Any advice would be helpful!! submitted by /u/FitNail254 [link] [comments]

Hey everyone, I’m a final year Software Engineering student looking for a strong FYP idea. My main interest is in AI and Machine Learning, but I’m also open to Cloud Computing or other modern tech fields if the idea is impactful and useful. I want to build something that is: ✔ Practical and solves a real-world problem ✔ Impressive for internships/jobs ✔ Doable within 6–8 months Some directions I’m considering: AI-powered applications (recommendation systems, chatbots, prediction models) Cloud-based smart systems (scalable apps, SaaS ideas) Automation tools using AI Anything innovative that stands out Would love your suggestions: 👉 Unique or trending FYP ideas in AI/ML or Cloud? 👉 Which type of project has the best career impact? 👉 Any ideas that combine AI + Cloud? If you’ve done your FYP recently, please share what worked (or didn’t) 🙌 Thanks a lot! 🚀 submitted by /u/Far_Fun_4284 [link] [comments]

I am looking at a product that will do runtime quant on DeepSeek V3.2. I want to measure quality loss compared to no quant. What kind of benchmarks can I run? submitted by /u/Chachachaudhary123 [link] [comments]

I recently found a paper accepted to CVPR 2026 reproduced many technical details from my paper submitted to arXiV on June 2025 (5 months before the CVPR 2026 submission deadline). Apart from technical similarities (they rephrased / reframed the term / key ideas), the CVPR paper uses exactly same equation without changes to any notations from our paper without proper citation. Several figures show high similarities in style and pipeline. We tried to contact authors from the CVPR paper, but they framed the technical similarity as "general method" so no need to cite. While they admitted that they refer to our paper for figure design, writing style, and equation, they can only update the arXiv version of their paper (the CVPR camera ready deadline has passed), claiming that they are "inspired" by us. Basically they would not do anything to their proceeding paper. I am wondering how CVPR identify the plagiarism between their accepted papers and arXiv papers? Will it be considered as plagiarism only if they reproduce a published work? Thanks for any advice! Attached part of the reproduction: Our arXiv work applied a multi-turn extension on the basic GRPO algorithm (with notation changes). The CVPR paper directly adopted the exact same equation without citation. Our arXiv paper The CVPR paper submitted by /u/sukays [link] [comments]

I am curious what everyone will be doing. I myself am torn, on the one hand I understand it boosts a paper’s credibility but on the other hand I worry about plagiarism, especially during current times. Thoughts? submitted by /u/undesirable_12 [link] [comments]

Hey everyone, We just open-sourced our reasoning model, Chaperone-Thinking-LQ-1.0, on Hugging Face. It's built on DeepSeek-R1-Distill-Qwen-32B but goes well beyond a simple quantization — here's what we actually did: The pipeline: 4-bit GPTQ quantization — compressed the model from ~60GB down to ~20GB Quantization-aware training (QAT) via GPTQ with calibration to minimize accuracy loss QLoRA fine-tuning on medical and scientific corpora Removed the adaptive identity layer for transparency — the model correctly attributes its architecture to DeepSeek's original work Results: Benchmark Chaperone-Thinking-LQ-1.0 DeepSeek-R1 OpenAI-o1-1217 MATH-500 91.9 97.3 96.4 MMLU 85.9 90.8 91.8 AIME 2024 66.7 79.8 79.2 GPQA Diamond 56.7 71.5 75.7 MedQA 84% — — MedQA is the headline — 84% accuracy, within 4 points of GPT-4o (~88%), in a model that fits on a single L40/L40s GPU. Speed: 36.86 tok/s throughput vs 22.84 tok/s for the base DeepSeek-R1-32B — about 1.6x faster with ~43% lower median latency. Why we did it: We needed a reasoning model that could run on-prem for enterprise healthcare clients with strict data sovereignty requirements. No API calls to OpenAI, no data leaving the building. Turns out, with the right optimization pipeline, you can get pretty close to frontier performance at a fraction of the cost. Download: https://huggingface.co/empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit License is CC-BY-4.0. Happy to answer questions about the pipeline, benchmarks, or deployment. submitted by /u/AltruisticCouple3491 [link] [comments]

I'm a data scientist by training with my own process for AI-assisted analysis, SOPs, asserts, sanity checks. Just want to see if others feel what I feel. Claude Code for products: incredible, tight feedback loop, works or it doesn't. Claude Code for analysis: paranoid every time. Wrong analysis looks identical to right analysis, silently dropped rows, miscoded variables, a slightly wrong groupby, the code runs, the number has decimals, and you have no idea if it's real unless you read every line. And I feel one step removed from the data now. I used to write every line myself and notice the weird distribution, the unexpected category, the row that didn't belong. That peripheral awareness is where real insight comes from. With the LLM in the loop, I touch the data less, and I catch less. Do you also feel one step removed from the data compared to before these tools existed? What are you doing to safeguard and double-check AI-assisted analysis? Has AI-assisted analysis ever caused you to ship a wrong number to a stakeholder? What happened? submitted by /u/Ghost-Rider_117 [link] [comments]

Since I felt like I was relying on Claude Code a lot recently, I wanted to see how hard it is to implement a diffusion language model from scratch without the help of AI-Generated code. So I built one while waiting for the training for my master's thesis. This is what I got after a few hours of training on my MacBook Air M2. I trained on the tiny Shakespeare dataset from Karpathy and prompted "to be, " To be, fo hend! First her sense ountier to Jupits, be horse. Words of wisdom! The model has around 7.5M Params and vocabulary size is 66 (65 chars + [MASK]. I definitely did not train long enough, but I ran out of time for this one. Projects like these help me make sense of big scary words like (discrete) diffusion, encoder, decoder, tokenizer. Maybe this encourages someone 🙂 Check out the code here if you're interested: https://github.com/Encrux/simple_dlm Thanks for reading! Be horse. submitted by /u/Encrux615 [link] [comments]

At my last role we had to move fast, so we relied on an LLM to help with a lot of the thinking and coding for us so we could focus on the business use case and managing meetings and stakeholders. The role was heavy on project management as well as development, research, and deployment so basically doing everything While I got good at scoping projects and managing them, my technical skills totally deteriorated in less than 1 year. It's scary going back to problems I know I can solve and but have some brain fog when getting to the answer. If I could have gone slower, had more time to thinking about modeling/coding than I probably wouldn't feel like this Don't get GPT brained. You'll have to crawl out of that pit eventually. Like technical debt but for your brain submitted by /u/LeaguePrototype [link] [comments]

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I am currently Biostatistics PhD student, and my advisors want me to graduate next year (2027). Orginally, my first advisor want me to graduate in 2028, but there were funding issues, so it looks like I have next year to prepare for job search. NGL, I am super worried, as I don't have any internships and my research is mostly computational (not theoretical). I am wondering if research direction is important? I know that I probably would not get into top research labs or become top quantitative researcher. I am just hoping I have good chance to become data scientist at tech company or work at pharma. I am little clueless how to do job search. I am super worried. I do have a paper or two published, but they are applied/collobration (large scale data analysis). submitted by /u/edsmart123 [link] [comments]

It’s a nightmare trying to find data I need in correct format while the company is in process of modernization. Also even if I find data I need to filter a lot of garbage out submitted by /u/Professional_Ball_58 [link] [comments]

So ICLR is coming and apparently the biggest value one can get from these conferences is to network. Let's take my example: I'm a PhD student looking for industry internships. Say I have located about 15-20 posters regarding topics adjacent or directly related to my area of research, some of which are by authors from industry labs. I go to the poster, ask the authors about their paper, discuss a bit, perhaps ask some insightful questions and mention that I work in similar things, and then after the conference I email them asking if they have internships? Is this how I should be extracting the networking value of it? Also, how overwhelmed are authors with these kind of requests? Seems like cold emailing vs this doesn't make that much of a difference, besides the fact that they might remember me from the conversation we had during 15 minutes during their poster session. submitted by /u/howtorewriteaname [link] [comments]

Hi r/datascience 👋 I've spent a lot of time digging through databases & docs, and one thing that keeps slowing me (and my coding agents) is not being able to search across everything all at once. So I built bm25-cli. It's a zero-config CLI that lets you run full-text search across your database schemas, tables, columns, keys, docs, comments, and metadata — in one command So, how does it work? Just point it at a source and search: $ bm25 "payment handling refund" ./db_docs $ bm25 "payment handling refund" mysql://user@localhost/mydb $ bm25 "payment handling refund" postgres://user@localhost/mydb Mix and match: $ bm25 "join error" postgres://user@localhost/mydb mysql://user@localhost/mydb ./mydocs No config files. No servers. No setup. Works with everything Source Example Directory ./src, ., /home/user/project Glob "**/*.md", "src/**/*.py" PostgreSQL postgres://user@host/mydb MySQL mysql://user@host/mydb SQLite sqlite:./local.db Website https://ngrok.com/docs/api Why I find it useful One command for everything — files, schemas, and docs in a single search BM25 ranking — same algorithm that powers Elasticsearch and Lucene Databases too — searches table names, columns, types, foreign keys, and comments Fast after first run — indexes are cached in ~/.bm25/ and reused If you're working with databases + coding agents, i'd love to hear what you think. --- GitHub: https://github.com/statespace-tech/bm25 A ⭐ on GitHub really helps with visibility! submitted by /u/Durovilla [link] [comments]

I implemented two recent ideas for long-context inference / KV-cache compaction and open-sourced both reproductions: Cartridges: https://github.com/shreyansh26/cartridges STILL: https://github.com/shreyansh26/STILL-Towards-Infinite-Context-Windows The goal was to make the ideas easy to inspect and run, with benchmark code and readable implementations instead of just paper/blog summaries. Broadly: cartridges reproduces corpus-specific compressed KV caches STILL reproduces reusable neural KV-cache compaction the STILL repo also compares against full-context inference, truncation, and cartridges Here are the original papers / blogs - cartridges - https://arxiv.org/abs/2506.06266 STILL - https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ Would be useful if you’re interested in long-context inference, memory compression, or practical systems tradeoffs around KV-cache reuse. submitted by /u/shreyansh26 [link] [comments]

Did anyone get an email? I emailed the chairs. They say every participant got an email titled: "CVPR26 BP Scholarship Decision Has Been Released", and participants got a separate email with the award and details. But I got no such email, yet. submitted by /u/Erika_bomber [link] [comments]

The current AI conference acceptance culture feels like it leaves little room for the kind of spark we once cherished in research (at least in my own experience). It seems to run on tons of evaluations to let reviewers believe solid, often far beyond the level of interest that can be realistically sustained for any single project, and almost nobody will verify them again. submitted by /u/NuoJohnChen [link] [comments]

Dragons, Data Science, and Game Design I'm a tabletop game designer. I recently built machine learning models to help with playtesting. However, the more I used AI the more I realized how important the human side of data was. From basic machine learning algorithms to complicated neural networks, the AI playtesting models were only ever as useful as the people building and running them made them. So I wanted to take a step back from AI and take a look at the role of data scientists. I felt the best way to do this was to look at all the mistakes I made when first using data for game design (I made a ton) because without those human errors, the AI tools wouldn't have had a functional foundation I definitely have a lot of room for growth as an author. Please feel free to leave any and all feedback! Hope that mistakes made in this article make the next one better! Key insights: Sample size matters (its not just something your statistics prof rambles about) Stratify your data! Data drift can hit in unexpected ways, so remember the business case and don't get lost in the data itself I will update the visual cues section. I also wrote a tips and tricks document for playtester which might have had a bigger impact than new art, so want to mention that as well In you're more interested in the pure AI side please check out: How to Train Your AI Dragon submitted by /u/BSS_O [link] [comments]

I saw many discussions about TMLR and other journals lately and how their review processes are considered fairer and less random. My question is, how much does it hurt one's chance much of getting interviewed/hired as a ML research scientist if they choose to publish at only journals like TMLR, JMLR, or Neurocomputing, instead of conferences? Edit: just to clarify, I mean corporate research scientist positions instead of academic positions. submitted by /u/dontknowwhattoplay [link] [comments]

I’m tired of training a lot of models and trying different datasets but still my model is trash and can’t detect clearly it sometimes has mAP50 pf 80% but it is only in numbers not practical, what can i do to have a good model that can be used? I trained using YOLO11n to use it in RPI5 16GB RAM no AI hat, but still can’t get the results i want, i tried searching and learning what could go wrong but I can’t seem to find the right solution+ i’m not that big of an AI expert so. submitted by /u/vDHMii [link] [comments]