Following up on the on-demand comparison from a couple weeks back - pulled spot/ interruptible pricing this time since that's where the real savings conversation actually lives for anyone running checkpointed training or batch jobs. Checked: June 2026. Spot/interruptible tier, single GPU. ~ H100 80GB -Spot/Interruptible RunPod :- $1.80-$2.40/hr , Community spot, can terminate without notice. Vast.ai :- $1.47-$2.00/hr (low end seen as low as $1.03/hr in thinner markets) wide range, host-dependent. AWS (P5, spot) :- technically available, $2.50-$3.10/hr extremely limited, frequently unavailable at any price. ~ A100 80GB - Spot/Interruptible RunPod :- community spot as low as $0.20-0.40/hr (high variance) , reliability drops fast at this end. Vast.ai:- $0.67/hr typical, lower with thinner-reliability hosts, marketplace bidding, varies by host score. AWS (P4d, spot) :- ~$1.00-1.50/hr more consistently available than P5 spot. What stood out: The spot discount vs on-demand is real - 40-60% off on H100, sometimes more on A100 - but the spread between providers on spot is much wider than on-demand. You're not comparing apples to apples, you're comparing apples to "whatever fell off the truck this hour." AWS spot for H100 (P5) is more of a theoretical price point than a practical one right now - availability is thin enough that "checked the price" and "could actually get one" are two different questions. Vast.ai's floor prices look incredible until you check host reliability scores. The $0.67/hr A100 and the $1.50/hr A100 are not the same product even though they're listed the same way. This tier only makes sense if your job checkpoints well - anything customer-facing or latency-sensitive, spot isn't worth the risk regardless of price. Not selling anything, just tracking this for my own training runs and figured others here are doing the same math. Anyone actually running production batch jobs on spot right now? Curious what interruption rates you're actually seeing vs what's advertised. submitted by /u/Shot-Calligrapher166 [link] [Kommentare]
Been chewing on this for a while and I honestly can't tell if it's genuinely open or if I'm just not googling the right terms, so I'll throw it out here. There's a paper by Roynard that put words to something I'd been vaguely bothered by for a while. He calls it a category error, the idea being that pretty much every memory system applies the same persistence model to everything it stores, usually some kind of decay, no matter what the thing actually is. Which is fine for some throwaway message, but it gets weird when the same treatment gets applied to something you've actually validated and would want to stick around and get corrected if it turns out wrong. So you end up storing very different kinds of information under the same rules, even though how much you should trust them or how long they should live is completely different, and I don't think many people are treating that difference as a real property of the memory. That kind of lines up with the thing that keeps biting me once an agent's been running a while, which is that the system doesn't really have any idea how it came to know something in the first place. Something it actually observed, something it inferred a few steps back, and something it just made up all get written down more or less the same way and come back out later at the same confidence, and because none of it is marked as one kind or another, the agent doesn't really have a way to notice when it's wrong. So something stale or hallucinated can resurface later and it looks basically the same as something solid. Timestamps help a little, but they don't tell you what actually backed a belief, and they don't do much when whatever supported it turns out to be wrong and you'd want the agent to walk that back. What bugs me is that this feels like it should already map onto stuff people worked out decades ago. Belief revision (AGM and everything after it) is basically about updating a set of beliefs without it going inconsistent, but I've never really seen it bolted onto an agent's memory at any serious scale. Truth maintenance systems (JTMS/ATMS) are almost exactly this, since the whole point is tracking justifications and retracting conclusions when their premises disappear, and yet they seem basically absent from the agent stacks I've looked at. Calibration work is more about confidence on outputs than about the support behind things you've already stored as they age. And the smarter retrieval stuff, like HippoRAG or the temporal knowledge graph systems (Zep, Graphiti), makes retrieval better but still kind of treats memory as things to find rather than claims that have a support status attached. The closest thing I've found to what I'm describing is Hindsight, which actually splits memory into separate networks and has real conflict-resolution policies, but even there contradictions get surfaced when you query rather than reconciled up front, and corrections don't really propagate to the beliefs that depended on them. A recent paper gets at the human side of it pretty well, The Missing Knowledge Layer in AI. Their framing is that language sort of collapses uncertainty, so when a model states a guess, an inference, and an actual recollection all at the same surface confidence, the person on the other end can't really tell which one they're reacting to, which felt like the same problem I'm describing just viewed from the user's side instead of from inside the memory. What I actually want, at the end of all this, is a small set of primitives for it. Some reasonably clean way to assert something, say what supports it, note that two things contradict, supersede one with another, retract it when it falls apart, with the epistemic status being an actual property of the thing instead of something I bolt on after the fact. Belief revision and TMS feel like the right ancestors, but neither was really built for noisy, high-volume, LLM-generated memory, so a lot of my time just goes into trying to figure out what the right primitives even are. So for anyone who's actually shipped long-horizon agents, I'm curious whether anyone types their memory by epistemic status (observed, inferred, asserted, retracted) instead of just storing flat text plus embeddings, whether anyone's gotten classical belief revision or a TMS to hold up as the actual live memory layer rather than falling over on scale and noise, and whether handling contradictions at write time is even worth the latency or if most people just eat the inconsistency and sort it out at read time. And honestly, if this is already a solved thing and I'm just reinventing some old formalism with extra steps, I'd genuinely appreciate someone pointing me at it so I can go read. But if it's actually underexplored then I'm a little surprised, because it really looks like one of the things that quietly wrecks agents once they've been running longer than a week. submitted by /u/Bright-Fun-1638 [link] [Kommentare]
Do you guys get a miccai grants result? I do not receive any mail. Don’t I accept? submitted by /u/CrazyIndependent7436 [link] [Kommentare]
With the release of meta-reviews, ECCV sent out a google form for dissatisfied authors to submit an appeal for the following reasons: Policy errors, e.g., reviewers or Area Chairs applied a policy that does not exist, or reviewers or Area Chairs applied policies that are not applicable for the contribution type of the paper; Clerical errors, e.g., it is clear from the meta-review that an Area Chair intended to accept a paper but the paper was rejected; and Obvious and major misunderstandings on the side of the reviewers or Area Chairs (historically, these are extremely rare). Is anyone considering submitting an appeal? I was rejected with 6/4/3 final scores on criteria that policy and AC/Reviewer guidelines explicitly state my contribution type should not be penalized for. Also, according to their policies, if they were to use the criteria in their decision, they have to explicitly disagree with the declared contribution type and state so in the meta review (all three reviewers agreed with the declared contribution type and the AC does not mention changing the paper's type.) submitted by /u/Muted-Ad4511 [link] [Kommentare]
I've been working through the internals of LLM inference and writing up what I learn as an open, in-progress handbook. Just wrapped another chapter on GPU execution and memory internals: why a GPU sits mostly idle during inference, how the memory hierarchy gates throughput, and where the real bottlenecks live. Added mermaid diagrams for the architecture pieces so the flow is easier to follow than a wall of text. It's a personal learning project, still growing chapter by chapter. I'd value feedback or corrections from anyone who's run inference in production, where my mental model breaks down is exactly what I want to find. Issues and PRs welcome. github.com/harshuljain13/llm-inference-at-scale submitted by /u/YouFirst295 [link] [Kommentare]
Hello Folks, we start our discussion on Lecture 10 of Probabilistic Machine Learning, now starting with Probability Multivariate Models. Univariate models are toy cases, in real life, ML models are multivariate. To understand dependence of more than one variables on each other we study ideas as Covariance, Correlations, we delve ourselves into the interesting concept of Simpson’s Paradox, with an example. We define the Multivariate Gaussian distribution, understand the level sets(curves) that we see in our computers while plotting, and gain insights into the geometric shape of the Gaussian density by using “Mahalanobis distance”. Mathematical foundations are extremely important, in that they make an ML engineer, data scientist stand out. These concepts are becoming so ubiquitous today, that folks from all backgrounds of engineering are interested in the mathematics behind these algorithms. I hope the learning community finds it helpful, and suggestions are always welcomed. These are FREE lectures. Link in comments submitted by /u/Negative_War_65 [link] [Kommentare]
I have a main paper in ACL and a workshop paper in ICML. I'm looking for jobs in U.S. as a graduating student. Would it be worth going to ICML after ACL presentation such that I have more chance to network? ACL is in San Diego and ICML is in Korea, if it changes things. submitted by /u/Appropriate_Willow27 [link] [Kommentare]
Submitted a short theoretical paper to TMLR and got desk-rejected with "does not meet our editorial standards or allow us to assess claims and evidence" and "not a suitable venue for this work." Is this a common outcome for first submissions? Curious what typically drives this kind of rejection, scope mismatch, insufficient experiments, or something else. Not looking to appeal, just trying to understand the bar so I don't waste time on the wrong venue next time. Anyone else gotten this and figured out what the actual issue was? submitted by /u/observer678 [link] [Kommentare]
Hello everyone, tomorrow I have a meeting with my dissertation supervisor and I wanted to have a dissertation proposal ready. Initially, I moved forward with the following proposal: "Interpreting the Routing Dynamics of Capsule Networks for Explainable AI." My first approach to this topic was to study the paper "Transforming autoencoders," which is the first paper about capsule networks. Next, I did a search on the state of the art of transforming autoencoders and only found 2 papers since 2011. I think I should take advantage of the work I have developed so far on transforming autoencoders and write a dissertation about them. If anyone could take a look at the readme and tell me what they think, I would appreciate it. What do you think? I should suggest another topic involving transforming autoencoders. There isn't much scientific research on them. The professor is approachable, and if I present a good new topic, he'll let me change it! submitted by /u/Future-Persimmon5393 [link] [Kommentare]
Hey All, I am currently working on ASR models, and I have gathered some recent literature. From my literature search, it seems like the ASR models are getting more and more powerful due to two main things. Because pseudo-labelled data is growing, supervised models are rising rapidly. Whisper-large-v3 has been trained on 5M hours of weakly supervised data, and Nvidia Parakeet v3 has been trained on 660k hours of labelled data (open-sourced). Funny enough, Nvidia Parakeet v3 actually beats Whisper-large-v3 on almost every benchmark, even though it has a smaller model size and smaller data scale. So clearly, scale is not everything. New architectures are on the rise; We used to have self-supervised + CTC to solve the ASR task, but now it seems like Transducer, and Token-Duration-Transducers are taking off. As well as attention encoder-decoder architectures (Qwen) that are all trained in a supervised manner. Now, given that the labelled data is very huge, and the new architectures are coming up, are we saying bye to the self-supervised learning approaches like Data2Vec2.0, WavLM, etc., for ASR, and will we only use them for general-purpose speech tasks? This is actually not similar to how computer vision operates now. Dinov3 is a self-supervised approach that is extremely performant in segmentation, classification, depth estimation etc but I do not see this in the speech domain now. ASR is dominated by these huge supervised architectures (which is a dense-prediction task), as well as emotion recognition, diarization, and speech seperation are also all dominated by the supervised approaches. Do you think we will have our Dino moment with a new self-supervised architecture? Or supervised learning is the way to go? How would these methods actually perform if we trained a self-supervised model on these huge datasets? submitted by /u/ComprehensiveTop3297 [link] [Kommentare]
Hi r/MachineLearning, Wanted to share something I'm excited about. I’ve been fascinated by AlphaEvolve and its results for more than a year now, but using open source frameworks seems overwhelming because of the high costs. I can’t really afford hundreds of Claude Opus calls every time I want to run it. I want to be able to try it out many times and all sorts of unique domains. What if it was possible for AlphaEvolve to be much more affordable while getting a better performance? Over the last six months or so, I’ve been working on LEVI, an open source AlphaEvolve-like system that can outperform existing open source frameworks at a fraction of the cost (upto 35x cheaper!). It can also run on Claude Code or Codex, making it even more accessible (I've mostly been using it with a QWEN-30B). LEVI comes in two flavors where I felt it’ll make the most difference: Code Optimization, and Prompt Optimization (sorry math, you got a less direct path; workable through the code route). The core thesis behind LEVI is that with the right search architecture, smaller models can substitute for or outperform larger ones. This means it’s much more economical to rely on smaller models for most of the work. That’s the entire takeaway. Making this work in practice is a different problem, but if you forget everything else from this post this is the only message I think I’m really trying to convey here. LEVI does it in three ways: 1) Invest in solution diversity from the start and ensure its maintained. We don’t want to converge to the same solution, especially with smaller models in the mix, and rely on large models to pull us out of the basin. 2) Use smarter routing across larger and smaller models (i.e. most mutations don’t require a Claude Opus X) 3) For prompt optimization not every rollout is as important. Build a proxy subset to approximate. I’ve tried LEVI on systems problems (like MoE scheduling or database transaction scheduling) and found that LEVI outperforms existing frameworks on almost every problem I threw at it while consistently using a smaller budget (unto 7x cheaper). For prompt optimization, across problems like IFBench and HotSpotQA, LEVI reaches a similar or better score as GEPA while using less than half the rollouts! Happy to answer any questions or take any suggestions! If there are unexpected or niche domains where this can be applied, I would love to hear. Technical Blog: https://ttanv.github.io/levi/ GitHub: https://github.com/ttanv/levi submitted by /u/Longjumping-Music638 [link] [Kommentare]
There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [Kommentare]
Hi all, I have been working on this method based on a hunch along with many llm for quite some time. Though first it was being engineered by me but I was learning in supervised ml area but this hunch took to semi-supervised ml and that to too deep. I then became llm orchestrator of sort while 4 llm's tried to figure it out. I put up a live demo on Hugging Face Spaces where you can try it yourself — set the number of labels, click run, see the accuracy. No installation, no code required. Brief about method Optimus — Graph SSL under Extreme Label Scarcity Key Results (PathMNIST, N=2000, 9 classes) Labels Total Optimus GCN 9(1 per class) 73.9 60.6 27(3 per class) 77.3 68.5 45(5 per class) 79.8 77.1 https://huggingface.co/spaces/Keshu007/optimus-graph-ssl Edit : You can can even run the code on your own dataset submitted by /u/Loner_Indian [link] [Kommentare]