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Someone Trained a Transformer Without Backpropagation. I Rebuilt the Core Trick in Under 60 Lines of NumPy.
1+ hour, 56+ min ago (222+ words) Dust trains a language model using only forward passes and noise. It’s not faster than … Someone Trained a Transformer Without Backpropagation. I Rebuilt the Core Trick in Under 60 Lines of NumPy. Dust trains a language model using only forward passes…...
Hypergraph Adapters Push Parameter-Efficient Multimodal Fine-Tuning to New Heights
12+ hour, 13+ min ago (30+ words) Researchers have introduced HGA-Net, a hypergraph-based adapter that connects related examples within each mini-batch and achieves near-perfect fine-grained recognition with only 1.573 million trainable parameters on a frozen CLIP backbone....
Reshape vs View in PyTorch: What's the Difference (and When `view()` Breaks)
21+ hour, 48+ min ago (401+ words) A tensor's data and its shape are two separate things. The data is a flat row of numbers sitting in memory; the shape is just the lens you look at it through. Reshaping changes the lens without touching the numbers…...
Production MMM: Why Uncalibrated Models Fail and How Meridian 2.1.0 Solves It
1+ day, 5+ hour ago (297+ words) Marketing measurement is fundamentally a causal inference problem constrained by observational data. The core challenge is observational confounding. Historical ad spend is rarely randomized; rather, ad pacing is strictly endogenous to expected demand. Advertisers increase budgets during high-conversion periods, creating…...
How Accurate Is Your Model Right Now? Estimating Accuracy Without Labels
1+ day, 13+ hour ago (272+ words) Label-free accuracy estimation under data shift — and the monitoring sidecar that uses it. Your model was 91% accurate in validation. Three months later, nobody knows what it is. Labels are expensive, slow, and often weeks away. Drift dashboards show you input…...
Aggregating Decoder in Transformer Model
2+ day, 19+ hour ago (537+ words) From the previous experiment makes me think how the important of the identity of the original token embedding vector. Which x_0 is token embedding without any transformation and f is a decoder function. Compare to the Transformer model in general, this…...
The Math Behind Neural Networks, Part 2.2
3+ day, 5+ hour ago (644+ words) Why Cross-Entropy Beats Squared Error — From the Inside. A calm, sequential guide for anyone entering the field. The article is divided into smaller …...
PyTorch Explained: The Complete Guide to Deep Learning & Neural Networks in 2026
3+ day, 23+ hour ago (27+ words) A practical 2026 guide to PyTorch covering tensors, neural networks, autograd, backpropagation, optimizers, GPU …...
An uncalibrated classifier is worse than no classifier
4+ day, 3+ hour ago (405+ words) What I learned building a website that rewrites itself for whoever is reading it, on ₹0 of infrastructure. My portfolio gets very different readers. A recruiter clicking through from a LinkedIn job post wants to know whether I'm hireable for an…...
Evaluating Speculative Decoding in vLLM on AMD MI300X GPUs
4+ day, 12+ hour ago (282+ words) Standard LLM serving remains bound by memory bandwidth, requiring a full forward pass for every output token. Speculative decoding in vLLM attempts to break this bottleneck on AMD Instinct MI300X and MI355X GPUs by using fast draft methods like MTP, EAGLE-3, DFlash,…...