neural networks (CNNs)によって埋め込んだベクトルと,画像に対する自然言語の修正指示文をLong short-term memory neural networks (LSTM)によって埋め込んだベクトルを入力とし,敵対的学習によって指示通りに修正された画像の生成を行う枠組みを提案した.実験では
# Multi-layer networks ### A nonlinear problem Consider again the best linear fit we found for the car data. Notice that the data points are not evenly distributed around the line: for low weights, we see more miles per gallon than our model predicts. In fact, it looks as if a simple curve might fit these data better than the straight line. We can enable our neural network to do such curve fitting by giving it an additional node which has a suitably curved (nonlinear) activation function . A useful functi
This is the story of how I trained a simple neural network to solve a well-defined yet novel challenge in a real iOS app. The problem is unique, but most of what I cover should apply to any task in any iOS app. That’s the beauty of neural networks
The paper presents SAINT, a transformer model employing row and intersample attention with contrastive pre-training, outperforming traditional tabular models.
Deep Neural Networkを使って画像を好きな画風に変換できるプログラムをChainerで実装し、公開しました
Volume 18, Number 4 ASRA-GNN: Adaptive Signed Relation-Aware Graph Neural Network for Friend Recommendation Authors Pharsana Parveen M and Stanis Arul Mary A, Nirmala College for Women, India Abstract Existing Signed Graph Neural Networks optimize link sign prediction objectives fundamentally misaligned with friend recommendation, while discarding trust asymmetry, edge strength, and adaptive social theory application. We present ASRA-GNN, addressing these gaps through three contributions: Sign-Aware Structu
Struggling to trust AI in high-stakes science? A Physics-Informed Neural Network (PINN) embeds physical laws directly into AI, eliminating impossible predictions
Introduction Imagine you are working at a major investment bank like J.P. Morgan or Goldman Sachs, or maybe at a hedge fund, where modeling how the market behaves is critical, whether for trading strategies, risk controls, or stress scenario simulations. In these types of environments, being able to generate realistic synthetic financial time-series data isn't…
Neural architecture search with reinforcement learning Zoph & Le, ICLR'17 Earlier this year we looked at 'Large scale evolution of image classifiers' which used an evolutionary algorithm to guide a search for the best network architectures. In today's paper, Zoph & Le also demonstrate that learning network architectures (and also in their case recurrent cell
# Invertible Neural Skinning ###### Abstract Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer from the limited expressiveness of Linear Blend Skinning (LBS), require costly mesh extraction to generate each new pose, and typically do not preserve surface correspondences across different poses. In this work, we introduce Invertible Neural Skinning (INS) to address these shortcomings. To maintain correspondences