MIT neuroscientists have performed the most rigorous testing yet of computational models that mimic the brain's visual cortex.

Spatial relations, such as above, below, between, and containment, are important mediators in children’s understanding of the world (Piaget, 1954). The development of these relational categories in infancy has been extensively studied (Quinn, 2003) yet little is known about their computational underpinnings. Using developmental tests, we examine the extent to which deep neural networks, pretrained on a standard vision benchmark or egocentric video captured from one baby’s perspective, form categorical

Transformer-Evolution-Paper ⌘Ctrlk Transformer-Evolution-Paper Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Parallelizing Legendre Memory Unit Training Simplified State Space Layers for Sequence Modeling Pretraining Without Attention What Makes Convolutional Models Great on Long Sequence Modeling? Hungry Hungry Hippos: Towards Language Modeling with State Space Models Hyena Hierarchy: Towards Larger Convolutional Language Models RWKV Simple Hardware-Efficient Long

# Explainable neural networks for trait-based multispecies distribution modelling—A case study with butterflies and moths Bourhis, Yoann , Bell, James , Shortall, Chris , Kunin, W. and Milne, Alice (2023) Explainable neural networks for trait-based multispecies distribution modelling—A case study with butterflies and moths. Methods in ecology and evolution, 14 (6). pp. 1531-1542. 10.1111/2041-210X.14097 Species response traits mediate environmental effects on species distribution. Traits are used in

Artificial Neural Network Input Vector From GM-RKB An Artificial Neural Network Input Vector is a Vector that contains all input values that are fed to the artificial neurons in a Neural Network Input Layer . AKA: Neuron Input Vector . … Example(s): $\vec{X} = \{x_1, x_2, \cdots , x_m\} $ in the following artificial neuron model:   . $\vec{U} = \{u_1, u_2, \cdots , u_n\} $ in the following artificial neuron model: $\vec{X}=\{x_1,x_2,x_3 \cdots, x_n\}$ in the following Single Layer Neural Network

”By the help of microscopes, there is nothing so small, as to escape our inquiry; hence there is a new visible world discovered to the understanding.”Robert Hooke

By Nick Bourdakos Convolutional neural networks have done an amazing job, but are rooted in problems. It’s time we started thinking about new solutions or improvements — and now, enter capsules. Previously, I briefly discussed how capsule networks co

# Why Initialize a Neural Network with Random Weights? The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand the role of nondeterministic and randomized algorithms as well as the need for stochastic optimization algorithms to harness randomness in their search

一文看懂什么是人工神经网络（Artificial Neural Network | ANN）， 与人类的神经网络有什么相似之处？在人工智能中起什么作用

Picture by Jaebum Joo I'm pleased to say that we've been able to release a first version of TensorFlow's quantized eight bit support. I was pushing hard to get it in before the Embedded Vision Summit, because it's especially important for low-power and mobile devices, so it's exciting to get it out there. All this…