Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Data Science How to Easily Draw Neural Network Architecture Diagrams Using the no-code diagrams.net tool to showcase your deep learning models with diagram visualizations Kenneth Leung Aug 23, 2021 4 min read Share Image by Author As we work with increasingly complex neural networks, the architecture can grow into something difficult to c
pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
# Visualizing Features from a Convolutional Neural Network It's been shown many times that convolutional neural nets are very good at recognizing patterns in order to classify images. But what patterns are they actually looking for? I attempted to recreate the techniques described in Visualizing and Understanding Convolutional Networks to project features in the convnet back to pixel space. In order to do this, we first need to define and train a convolutional network. Due to lack of training power, I co
The discussion revolves around the limitations of using neural networks for trading strategies, highlighting the inefficiency of training complex networks and the potential of genetic programming as an alternative. The author questions the use of sigmoid functions for neurons with many inputs and suggests that genetic algorithms might be more suitable for optimizing trading strategies. The text also mentions the computational challenges of training large networks and the availability of GPU-based solutions
# Pointer Networks Oriol Vinyals, Meire Fortunato, Navdeep Jaitly We introduce a new neural architecture to learn the conditional probability of an output sequence with elements that arediscrete tokens corresponding to positions in an input sequence.Such problems cannot be trivially addressed by existent approaches such as sequence-to-sequence and Neural Turing Machines,because the number of target classes in eachstep of the output depends on the length of the input, which is variable.Problems such as sor
<p>Recurrent neural networks (RNNs), along with long short-term memory networks (LSTMs), have been successfully used on a wide range of sequential data problems and have been entitled as extraordinarily powerful tools for learning and processing such data. However, the search for a new or derived architecture that would model very long-term dependencies is still an active area of research. In this paper, a relatively psychologically plausible architecture named event buffering JANET (EB-JANET) is proposed
While deep neural networks take loose inspiration from neuroscience, it is an open question how seriously to take the analogies between artificial deep networks and biological neuronal systems. Interestingly, recent work has shown that deep convolutional neural networks (CNNs) trained on large-scale image recognition tasks can serve as strikingly good models for predicting the responses of neurons in visual cortex to visual stimuli, suggesting that analogies between artificial and biological neural networks
Spotlight Poster # Recurrent neural network dynamical systems for biological vision Wayne Soo ⋅ Aldo Battista ⋅ Puria Radmard ⋅ Xiao-Jing Wang 2024 Spotlight Poster ### Abstract In neuroscience, recurrent neural networks (RNNs) are modeled as continuous-time dynamical systems to more accurately reflect the dynamics inherent in biological circuits. However, convolutional neural networks (CNNs) remain the preferred architecture in vision neuroscience due to their ability to efficiently process visual
This explores whether the way to win at structured reasoning is to swap the transformer for a graph-native architecture — but the corpus reframes it: the real fault line isn't transformer-vs-GNN, it's
Neural Network Processing Unit (NPU) adopts a “data-driven parallel computing” architecture, which is particularly good at processing large-scale mult