Neural Networks news and analysis on Web Pulse: 1259 curated articles tracking the latest Neural Networks developments, tools, and research, updated continuously from vetted sources.
Researchers at Princeton University, led by Professor Ken Norman, have developed a system that reconstructs images from brain activity measured by fMRI in real time, producing reconstructions about 15 seconds after viewi…
A research overview lists key topics in machine learning, AI, and deep learning, including Euclidean geometry, group invariance theorem, universal approximation, player perceptron, curse of dimensionality, neocognitron, …
A developer's blog post traces the evolution of language models from rule-based systems to modern LLMs, framing each breakthrough as a bug fix. The post highlights key milestones such as n-gram models, word2vec's vector …
A new paper by Kaarel Hänni and an unnamed author, available on arXiv (2607.05735), concludes an analysis of generalization complexity in Bayesian infinite-width neural networks, resolving when functions can be learned w…
Marvin Minsky and Seymour Papert's 1969 book 'Perceptrons' demonstrated that a single-layer perceptron cannot solve the XOR problem, and they incorrectly argued that multi-layer networks would not help, leading to a fund…
Researchers are advancing whole-brain emulation by combining neuroscience, AI, and rigorous data to explore consciousness scientifically, according to a Forbes Innovation article by John Werner. The effort aims to map an…
A new neural network compression method called PIN v2, developed by an unnamed research team, allows model size and speed to be chosen in advance by tying groups of cells to hold identical values, keeping the grid size c…
A neural network trained to predict the stiffness of perforated metal sheets achieved perfect accuracy on held-out test sets but failed to generalize across different hole geometries, performing worse than a trivial dens…
A developer implemented batch normalization, layer normalization, and group normalization from scratch and compared their performance on a simple multi-layer perceptron (MLP) classifying the MNIST dataset, finding that e…
PLOS Computational Biology published a research article on July 30, 2026, describing a spiking neural-network model that combines sampling-based probabilistic inference with attractor dynamics in the head-direction syste…
A 15-year-old developer from Germany has released ABSL v1.0.0, an integer-only learning method for AI that solves XOR with 92.9% accuracy, written in Rust.
A developer built a transformer model from scratch using pure PyTorch, without relying on libraries like HuggingFace, to gain a deeper understanding of the architecture. The project involved implementing multi-head atten…
Lizy K. John, a professor of electrical and computer engineering at the University of Texas at Austin, argues that weightless neural networks can be 1,000 times smaller or faster than conventional AI models while maintai…
A 15-year-old developer in Germany has released ABSL (Adaptive Bit-Shift Learning) v1.0.0, a 100% integer-only neural network training algorithm written in Rust that achieves 91.1% global accuracy on the XOR problem by e…
Sami Kaski, a professor at the Centre for AI Fundamentals, has been appointed as program chair for the Paris satellite event of NeurIPS 2026, alongside Lorenzo Rosasco from the University of Genova. The 2026 conference w…
A new computational framework called Quotient Tree Arithmetic (QTA) prevents gradient underflow in deep neural network training by representing values as deferred quotient pairs evaluated lazily at a materialization boun…
FloDR, an invertible dimensionality reduction method based on a normalising flow, embeds high-dimensional data into two dimensions while retaining all coordinates to enable exact inverse and density calculations. Unlike …
Researchers have developed a deep neural network approach to estimate weight, height, and Body Mass Index (BMI) from a single human image captured in the wild, achieving best results with full-body images. The team creat…
A new arXiv preprint (2607.26059v1) reports that deep reinforcement learning agents using frozen, randomly initialized CNN feature extractors spontaneously develop extremely sparse fully-connected representations, with a…
A new study from arXiv (arXiv:2607.26192v1) shows that dynamic parameterization in neural networks does not imply dynamic inference or computational savings. Testing on FeatureGate Transformers (76M and 504M) and MUDDPyt…