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AI 资讯 Reddit r/MachineLearning

First paper acceptance (ICML Workshop), should I attend? [D]

I just finished my first year of undergrad, and I got my first first-author paper accepted to an ICML workshop! Super stoked, especially since I was lowk a crashout in high school I wanted to know if it is worth it for me to go? It's quite expensive, and I will be the only one in my lab in attendance, so I will be on my own. If I do attend, how would I best maximize this opportunity? I got an email saying main conference tickets would also be made available for accepted authors, so I would likely be able to attend that as well. What are the best ways to network, meet people, and make sure it's worth it? Also, I am applying for transfer for this next cycle, so any advice relevant to that is also appreciated. submitted by /u/YukiOnnaLake [link] [留言]

/u/YukiOnnaLake 2026-06-04 03:48 8 原文
AI 资讯 Reddit r/artificial

Google just dropped Gemma 4 12B on your laptop!!

bro google just casually released a 12 billion parameter multimodal model that runs on 16gb of ram like… your macbook pro can run this. no cloud. no api calls. no monthly bill. it’s encoder-free, handles images and text, apache 2.0 license so you can do whatever with it commercially the “cloud is the only way” narrative is dying fast. on-device AI is not a gimmick anymore, it’s where the serious money is going submitted by /u/NewMuffin3926 [link] [留言]

/u/NewMuffin3926 2026-06-04 03:42 8 原文
AI 资讯 Reddit r/artificial

I think this might be one of the best use cases for AI music

Dunno if it’s the best overall, but it’s definitely been one of the most meaningful ones for me. I’ve been using MiniMax Music 2.6 quite a bit lately, even though it’s rate limited. For me it’s been nice for quickly testing song ideas, generating short melodies, and retrying different versions when I want a slightly different feel. I was recently using Genspark to make a PPT, and kind of accidentally discovered that it could also generate music. That led me to try something a lot more meaningful than just making random tracks: I asked it to create three short melodies for my kid, each one reflecting a different country or ethnic musical style.It turned the lesson from something abstract into something they could actually hear and compare. That’s what made it feel special to me,not just “AI can make music,” but “AI can make learning more vivid.” submitted by /u/ResultOk1259 [link] [留言]

/u/ResultOk1259 2026-06-04 03:30 8 原文
AI 资讯 Reddit r/MachineLearning

How are production ML systems typically handling distribution shift over time? [D]

In deployed ML systems, data distribution drift seems unavoidable over longer time horizons. I’m trying to understand what approaches are commonly used in practice: Continuous retraining pipelines (fixed intervals vs trigger-based) Online monitoring for feature or prediction drift Use of shadow models or fallback models in production Human-in-the-loop review for edge cases In most real deployments I’ve seen discussed, retraining strategy seems more operationally constrained than model-related. Curious what approaches are actually working reliably in production environments and what tends to fail first. submitted by /u/Electrical_Mine1912 [link] [留言]

/u/Electrical_Mine1912 2026-06-04 03:12 8 原文