#rbf_neural_network hasil pencarian

Introducing RND1, the most powerful base diffusion language model (DLM) to date. RND1 (Radical Numerics Diffusion) is an experimental DLM with 30B params (3B active) with a sparse MoE architecture. We are making it open source, releasing weights, training details, and code to…

Why "Attention is all you need" was needed ? (RNNs, LSTM-->Transformers) Flaws: -sequential processing resulting in slow training. - Struggle to capture very long ranged dependencies. - limited parallelization, limits the GPU efficiency. Transformers: - removes recurrence, uses…

This broke my brain. A team at Sea AI Lab just discovered that most of the chaos in reinforcement learning training collapse, unstable gradients, inference drift wasn’t caused by the algorithms at all. It was caused by numerical precision. The default BF16 format, used across…

Here is a neural network in 20-ish lines of code. From scratch! Here is how the code works, line by line: 1 of 10

At MIT, I learned about RNNs in my NLP class with Prof. Michael Collins. He built a model from my keystrokes to predict who I was. To me, it felt like a magic box. Years later, when I had to teach RNNs, I forced myself to go inside the box. ⬇️ Download: byhand.ai/rnn…

Weak models can supervise stronger ones but we find that weak-to-strong generalization can become infeasible under distribution shifts! In our #NeurIPS25 paper, we introduce RAVEN 🐦‍⬛, a framework that dynamically learns optimal combinations of weak models to robustly guide…

Training Diffusion Language Models at Scale using Autoregressive Models "We introduce RND1-Base, a general-purpose 30B-parameter sparse mixture-of-experts DLM trained with a simple and scalable continual pretraining recipe. Following a simple autoregressive-to-diffusion (A2D)…

I am SUPER EXCITED to publish the 130th episode of the Weaviate Podcast featuring Xiaoqiang Lin (@xiaoqiang_98), the lead author of REFRAG from Meta Superintelligence Labs! 🎙️🎉 Traditional RAG systems use vectors to retrieve relevant context, but then throw away the vectors,…

what if i tell you ai agents didn’t train alone… but as a swarm? 🐝 that’s what rl swarm + @gensynai unlocks. > so what is rl swarm? - a decentralised multi-agent rl framework where agents don’t just act they train each other, critique outputs, and improve as a collective. >…

The security layer is where most wallets fall apart. People assume they’re all built equal, but the gap is wide. @rainbowdotme $RNBW makes that obvious. They stack protection in a way that covers every stage of custody: - Hardware wallet connections keep keys fully offline -…

2) RLVR (reinforcement learning with verifiable rewards) is dead. Long live SPICE! (Alright, a bit tongue and cheek but see my final comment for the full caveat.) The field of RL evolved in the last few years from alignment with RLHF to chain-of-thought with RLVR. However,…

Machine Learning / Neural Networks in @Roblox! Simple 2 layer feed forward MNIST classification. #RobloxDev

Another tweak peek at how I introduce neural networks after explaining kernel regression as regression in feature spaces, giving the insightful case of random features, and then simply adding the features as the argument in the loss function to get the loss of a neural net.

More on RND1 models: Blog: radicalnumerics.ai/blog/rnd1 Code: github.com/RadicalNumeric… Report: radicalnumerics.ai/assets/rnd1_re… Weights: huggingface.co/radicalnumeric…

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New day, new paper «e𝑓unc: An Efficient Function Representation Without Neural Networks» It looks math-heavy, but it’s not that complicated Core idea is to swap an MLP with a bunch of small analytic patches, store them in a grid, and then perform global weighted interpolation…

Continuing our EMNLP series: Oracle AI research at #EMNLP next week. 🚀 [2/6] RCI: A Score for Evaluating Global and Local Reasoning in Multimodal Benchmarks Motivation — Vision-language benchmarks keep getting higher scores from multimodal LLMs…but are those models actually…

👂 Have you been hearing about Deep Learning everywhere? I know it sounds complicated, but I want to show you the simple idea behind it. It's actually inspired by how your brain works. The Intuition: Seeing Details - Think about the images we see every day. Take this example…

RFR/nnEMFs: THE CAUSAL CHAIN FROM S4 TO AUTOIMMUNE‑LIKE INFLAMMATION