Tag
The paper introduces MatFAE, a functional neural network for learning representations from trajectories on the symmetric positive definite (SPD) manifold, with applications in neuroimaging and other scientific domains.
An educational tool built in NumPy with a GUI to visualize the training of a small MLP, including weight distributions, t-SNE per layer, and neuron ablation, aimed at helping students and teachers understand neural networks.
The paper presents Neurogenesis Network (NGN), a differentiable parameterization for learning the optimal size of neural networks during training, applicable to various architectures like MLPs, CNNs, and Transformers.
The paper introduces HARN, a hierarchical associative resonance network for event-driven multi-timeframe forecasting in financial time series, showing competitive results against baselines through evaluations on multiple assets.
OpenTrainDNN is an open-source, client-side web application that provides real-time visualization of deep neural network training, including backpropagation and weight updates, directly in the browser.
Spi-Fly is a fruit fly-inspired neural network that uses sparse activity and associative learning to quickly learn odors with minimal memory, demonstrating strong few-shot performance but requiring further testing.
ResNLS is a hybrid neural network model combining ResNet and LSTM that improves stock price forecasting by emphasizing dependencies between stock prices, achieving at least 20% improvement over baselines and demonstrating practical trading applications.
This paper proposes a neural network model for fast and accurate identification of text content file types, outperforming existing tools like Magika in accuracy and speed while being smaller in size.
The author discusses how their 2021 neural network for classifying scientific publications has been rendered obsolete by newer AI models like Jev and Qwen 27b.
This paper introduces a channel-informed neural network for physical-layer key generation, using received IQ measurements and ray tracing augmentation to improve key diversity and pass NIST randomness tests.
WangNet is a lightweight, zero-dependency neural network that determines if a number is Numberwang in 11 languages, packaged in a 1.8 MB JSON file with simple Python inference.
The user trained a fly model to perform gym exercises like bicep curls and squats using the DeepSeek-V4-Flash AI model and Cline Desktop app, mapping 139,255 neurons from the FlyWire connectome on a MuJoCo physics engine.
A tweet reports that @hammer_mt has trained the flybrain system to perform vibe checks, indicating a development in AI or related technology.
CAHR-Net proposes a condition-adaptive hysteresis reconstruction network that improves magnetic core loss modeling by injecting operating conditions into intermediate representations, achieving lower errors with fewer parameters compared to existing methods.
DiffIE introduces a diffusion-based method for open information extraction that uses stochastic reverse-diffusion to generate multiple candidate triplets, achieving state-of-the-art performance on benchmarks like CaRB and BenchIE.
The user questions whether Mixture of Experts (MoE) routers can be designed to predict future expert needs for token sequences to enable faster caching between RAM and VRAM, or if a separate neural network could be trained for this purpose.
This paper introduces a mesh-free multiresolution deep energy method using neural networks for phase-field modeling of brittle fracture, demonstrating performance comparable to finite element methods in crack propagation simulations.
Author Alexey Fateev dissected the Qwen3.8-27B AI model to carve out a MoE structure through zero-training weight surgery, finding only two neurons active on over 90% of tokens.
This paper proposes SW-ProxyCE, a zero-query adversarial attack framework that transfers from public EEG encoders to private downstream models, demonstrating security risks in EEG foundation models.
This study presents an efficient neural-network-based surrogate model for high-resolution radiative transfer simulations, aimed at improving CO2 concentration retrieval from satellite measurements for climate monitoring.