Researchers propose a loss-aware feature-map pruning framework for convolutional neural networks that uses multi-armed bandits (UCB1 and Thompson Sampling) to identify and remove redundant feature map…
A new Bayesian framework called Neural Atom Prevalence (NAP) achieves state-of-the-art structural sparsity in feedforward neural networks, reducing active nodes to as few as 8% of the original dense a…
Every machine learning model, from linear regression to deep neural networks, rests on linear algebra, calculus, and convex optimization, according to a Stochastic Blog post. The post builds each conc…
NVIDIA TensorRT's IProgressMonitor API lets developers make long-running engine builds observable and cancelable in Python and C++, preventing wasted GPU-hours in agent workflows. The interface provid…
Researchers have introduced SechKAN, a Kolmogorov-Arnold Network architecture using hyperbolic secant functions, which achieves superior performance over MLPs and other KAN variants on benchmark datas…
A new implementation of the Fuzzy-Pattern Tsetlin Machine achieves 37 million MNIST predictions per second at 98% accuracy with 4.7 GB/s throughput on a desktop CPU, and demonstrates the first Tsetlin…
A new study on arXiv (2607.16231v1) evaluates OrthoGrad, a geometric intervention that removes the radial component of weight gradients during optimizer updates, in noisy-label image classification. O…
A new study evaluating variational quantum circuits (VQCs) in diffusion models on MNIST and CIFAR-10 found that quantum cores matched classical Fréchet Inception Distance (FID) scores despite using 4.…
A developer published a Keras-based implementation of the LeNet-5 convolutional neural network architecture, originally proposed by Yann LeCun in 1989 for handwritten digit recognition. The implementa…
Researchers introduced the input-skip LBBNN (ISLaB) technique, which reduces neural network complexity by over 99% while maintaining accuracy, achieving 97% accuracy on MNIST with only 935 weights. Th…
Researchers have developed quantum Wasserstein generative adversarial networks (GANs) that generate full-resolution MNIST images without dimensionality reduction or patchwork models, achieving state-o…
Researchers introduced Meta Neural Cellular Automata (MetaNCA), a framework that learns local rules to self-organize the weights of artificial neural networks without backpropagation. The method gener…
A developer visualized the training of a deep neural network on the MNIST dataset using PyTorch. The project displays real-time updates of the input image, hidden layer activations, output probability…
Researchers proposed a controllability-observability framework for compressing deep neural networks by viewing them as nonlinear dynamical systems. The method achieved up to 83% parameter compression …
Researchers introduced Sheaf-ADMM, a framework for multi-agent coordination that divides complex tasks into overlapping pieces assigned to individual agents. In tests, it achieved a 93% solve rate on …
Researchers propose S-GAI, a spectral geometry-aware initialization framework for sigmoidal MLPs that encodes dataset geometry into network weights. Using SVD to extract class-wise spectral features, …
Researchers introduced Pre-Warm, a zero-training-cost method for data-conditioned initialization of the first convolutional layer in CNNs. The technique clusters patches from a single training batch t…
Researchers introduced the apothem measure for computing robustness certifications in neural networks, achieving optimal certifications with linear oracle calls. They proved volume-optimal oracle-base…
Neural Cellular Automata (NCA) extend Conway's Game of Life by learning neural network weights to solve problems like MNIST digit classification, maze solving, and playing Pong. NCA use local receptiv…
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…