Gated Graph Sequence Neural Networks. Contribute to yujiali/ggnn development by creating an account on GitHub
Abstract page for arXiv paper 2107.04086: Robust Counterfactual Explanations on Graph Neural Networks
visualrambling.space about × About visualrambling.space is created by Damar, someone who loves to exploring new topics and rambling about them visually. I'm also open for collaborations or commissioned work. Feel free to reach out anytime! Follow Me Contact [email protected] UNDERSTANDING NEURAL NETWORK, VISUALLY Loading assets, please wait... Understanding Neural Network, Visually An interactive visualization to understand how neural networks work tap/click the right side of the screen to go forward → I
opinion mining with deep recurrent nets back   Paper : Opinion Mining with Deep Recurrent Neural Networks O. Irsoy, C. Cardie EMNLP, 2014, Doha, Qatar. Abstract : Recurrent neural networks (RNNs) are connectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, ``depth in space" --- as an orthogonal notion to ``depth in time" --- in RNNs has been investigated by stacking multiple layers of RNNs and shown empirically to bring a temporal hierarchy
Trace a CodeAssist timeout-risk network from one neuron to a batched NumPy forward pass, then diagnose activation collapse, silent transposes, feature scale, and sigmoid overflow.
It is well known that convolutional neural networks (CNNs or ConvNets) have been the source of many major breakthroughs in the field of Deep learning in the last few years, but they are rather unintuitive to reason about for most people. I’ve always wanted to break down the parts of a ConvNet and see what an image looks like after each stage, and in this post I do just that
NeurIPS Proceedings Search Evaluating Attribution for Graph Neural Networks Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Qian, Kevin McCloskey, Lucy Colwell, Alexander Wiltschko Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging. Attribution is one approach to interpretability, which highlights input dimensions that are influent
Learn how Probabilistic Neural Networks analyze financial data using probability models to improve forecasting, risk analysis, and business performance
## Availability Attacks against Neural Networks New research on using specially crafted inputs to slow down machine-learning neural network systems: Sponge Examples: Energy-Latency Attacks on Neural Networks shows how to find adversarial examples that cause a DNN to burn more energy, take more time, or both. They affect a wide range of DNN applications, from image recognition to natural language processing (NLP). Adversaries might use these examples for all sorts of mischief—from draining mobile phone bat
Modelling dynamical systems with recurrent neural networks