Researchers introduced SupraSNN, a hardware-software co-design that treats synaptic events as parallelizable micro-operations and physically decouples synaptic and neuronal computation, achieving syna…
A developer has demonstrated that the 1982 Hopfield associative memory update rule and the 2017 Transformer scaled dot-product attention mechanism are mathematically identical operations, with one equ…
A systematic study of query, key, and value (QKV) projection variants in Transformers found that sharing the key and value projections (Q-K=V) performs on par with or better than the standard three-pr…
Researchers have developed a tensor similarity method that can detect changes in neural network behavior, such as backdoor attacks, by comparing the weight-space structure of models rather than just t…
Researchers developed Riemannian Archetypal Analysis, a method that combines the interpretability of classical archetypal analysis with the flexibility of non-linear models by using data-driven pullba…
The author successfully ran MNIST digit recognition on an ESP32-C3 microcontroller by compiling a neural network directly into a C header file and executing it as firmware, without using TensorFlow, T…
Deep neural networks can be understood as pipelines of simple functions, and that the intermediate values (or "activations") within these networks can be viewed as high-dimensional vectors. To analyze…