Be honest... How many of you sold your XRP too early and are now watching from the sidelines? 👀 Every time XRP moves, the same debate starts: "Too late to buy?" "Going to the moon?" "Just another pump?" Love it or hate it, XRP keeps getting people talking. So I'll ask again Where do you honestly see XRP by the end of this year? Drop your prediction below. Let's see who gets it right. 🚀👇 #XRP #Ripple #Crypto #CryptoCommunity #XRPArmy #Investing submitted by /u/Outrageous-Hat9277 [link] [Kommentare]
“…5 papers at ICML (1 Spotlight)…” “…Five ICML papers is what a strong PhD produces in four years. I did it in five months…” I recently saw these posts from people at the same AI company. At first, I was extremely surprised. It turned out they were workshop papers. Am I missing something here, or are workshop papers now being treated as equivalent to main-track papers? submitted by /u/Terrible-Chicken-426 [link] [Kommentare]
No the thumbnail is not fake and shes quite talented would not be surprised if she is in here anyways enjoy —————————————————————————————————————-——————————————————————- submitted by /u/fake_odelay [link] [Kommentare]
Dear Folks, I have created multiple content on Machine Learning(work in progress), and they are free. I am a data scientist and a post grad degree holder in AI/ML from IIT. To help the machine learning community with important Machine Learning Concepts, I have created multiple long form videos, and structured topicwise digestible contents structured as playlists for learning. If you go through the first two playlists: Introductory Machine Learning Concepts Probability Foundations: Univariate Models You might find helpful content, I have tried explaining with intuitions, derivations, and this is work in progress. For code implementations, scikit learn website has great content on them as well. In total they have 60+ topicwise videos so far, and I think they have the potential to help folks a lot in starting with concepts, or getting with mathematical concepts, or whether you are preparing for an AI/ML/Data job interviews etc. When I sat for my interviews, I was grilled on my project, but majority of questions from my project tested more on foundational concepts and there know how’s. These are FREE content on youtube. This is for the benefit of the learning community. Link: https://youtube.com/@aayushsugandh4036?si=w5MKORU2fWzLRrAJ submitted by /u/Negative_War_65 [link] [Kommentare]
link - https://arxiv.org/abs/2606.06158 Abstract : Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous-regime approaches achieve this via iterative binarised searches or trained neural regressors, while discrete methods often require a full-rate decoder pass to estimate information content. We demonstrate that such computational overheads are not strictly necessary. We show that the latent space of a frozen continuous video tokeniser inherently encodes temporal redundancy that can be exploited directly: spatial positions whose latent representations change minimally between consecutive frames carry near-zero additional information. We introduce a parameter-free adaptive token allocation mechanism that applies a fixed threshold to per-position temporal-L1 differences, identifying and dropping redundant latent positions. Consequently, the compression rate emerges naturally from the input content rather than being enforced top-down: static scenes get compressed aggressively, while highly dynamic sequences retain more tokens. To reconstruct the dropped positions, we propose the Latent Inpainting Transformer (LIT), a lightweight factorised spatial-temporal attention architecture. The resulting inference pipeline is highly efficient, requiring only a single encoder pass and one LIT forward pass, eliminating the need for auxiliary routing networks. Evaluations across TokenBench and DAVIS, which are the standard benchmarks used by recent tokenisers, indicate that our framework yields meaningful, content-driven token allocation while maintaining competitive reconstruction fidelity, and delivers a 31x inference-time speedup over the continuous adaptive baseline (ElasticTok-CV) and an 2x speedup over the discrete information-theoretic baseline (InfoTok) submitted by /u/chhaya_35 [link] [Kommentare]
