--> Library Title Metadata Abstract Files Global-Local Attention vs Graph Neural Networks in the Reinforcement Learning Approach for the Dynamic Berth Allocation Problem Bridging the Optimality Gap in Dynamic Berth Allocation Problem via Global-Local Attention open_in_newPreview File Bachelor Thesis (2026) Author(s) V. Anica-Popa (TU Delft - Electrical Engineering, Mathematics and Computer Science) Contributor(s) N. Yorke-Smith – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science
## Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-Based Sentiment Analysis Fei Liu , Trevor Cohn , Timothy Baldwin While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) — extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects — remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel
Chainer v7.0.0 Tutorials Examples - Neural Net Examples - MNIST using Trainer - MNIST with a Manual Training Loop - Convolutional Network for Visual Recognition Tasks - DCGAN: Generate images with Deep Convolutional GAN - Recurrent Nets and their Computational Graph - RNN Language Models - Word2Vec: Obtain word embeddings - Write a Sequence to Sequence (seq2seq) Model References Other Community Chainer - Docs » - Neural Net Examples - Edit on GitHub # Neural Net Examples ¶ - MNIST using Trainer
The text covers various deep learning topics, including the Adam optimizer, autoencoders, generative adversarial networks (GANs), AlphaGo, computation graphs, automatic differentiation, and an introduction to neural networks. It discusses the limitations of stochastic gradient descent (SGD), the improvements made by the Adam optimizer, the concept of transfer learning, the functioning of autoencoders, the adversarial training process in GANs, the development of AlphaGo, the use of computational graphs for b
Lorentz' dev blog Writing a neural network from scratch in C (part 1) 28 Jul 2025 - Lorentz Vedeler This is the first part of my notes on trying to learn machine learning and implementing a simple neural network from scratch in C. The code is intentionally very raw - we don’t even have a function matrix multiplication, I think this makes the details in the algorithms and math stand out more. The source code can be found here: https://github.com/loldot/nn Linear regression An linear function is a function
A joint research team led by Xu Bo from the Institute of Automation and Mu-Ming Poo from the Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, have discovered that self-backpropagation, ...
Neurodegenerative diseases, like Alzheimer’s, Parkinson’s, or ALS, and neurodevelopmental disorders, like autism, Down syndrome, or schizophrenia require the ability to study neural networks. Our MEA platform is ideal for studying disease-in-a-dish models. Discover our assay
A neural network model is a series of algorithms that mimics the way the human brain operates to identify patterns and relationships in complex data sets. Here's how they work
--> Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol, Stefan Zohren, Stephen Roberts. Year: 2022, Volume: 23 , Issue: 173, Pages: 1−65 Abstract We study the effect of mini-batching on the loss landscape of deep neural networks using spiked, field-dependent random matrix theory. We demonstrate that the magnitude of the extremal values of the batch Hessian are larger than those of the empirical Hessian. We also derive similar results for
IEI Concepts What Are Imagination Engines? Learn about artificial neural nets that generate ideas. What Are Creativity Machines? Learn about brainstorming artificial neural nets. What Are STANNOs?" Learn Self-Training Artificial Neural Network Objects. What Are Supernets? Learn how artificial neural nets connect into networks of networks. What is DABUS? Learn about a whole new school of artificial neural nets that truly conceive ideas. Artificial Inventors Learn about our efforts to grant machines patents