Explore activation patching, a method that swaps internal activations to causally probe neural network components and improve interpretability
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Randomization for adversarial robustness: the Good, the Bad and the Ugly How Does It Feel? Self-Supervised Costmap Learning for Off-Road Vehicle Traversability → Multi-teacher knowledge distillation as an effective method for compressing ensembles of neural networks 投稿日: 2023年2月15日 作成者: jarxiv 要約 深層学習は、近年の人工知能の多くの成功に大きく貢献しています。 現在
--> Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be Valerio Biscione, Jeffrey S. Bowers. Year: 2021, Volume: 22 , Issue: 229, Pages: 1−28 Abstract When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation. It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling
Spiking Neural Networks As Universal Function Approximators
A Pytorch Example of a Neural Network. This Pytorch tutorial describes how to build a neural network and train it
Abstract page for arXiv paper 2004.05909: kDecay: Just adding k-decay items on Learning-Rate Schedule to improve Neural Networks
Explore and motivate the need for representation via embeddings.
The perceptron is where it all began. Understanding this simple unit—inputs, weights, dot products, and activation functions—gives you the mental model to understand modern neural architectures, from CNNs to Transformers
# Package edu.stanford.nlp.neural.rnn RNNCoreAnnotations Annotations used by Tree Recursive Neural Networks. RNNCoreAnnotations.GoldClass The index of the correct class. RNNCoreAnnotations.NodeVector Used to denote the vector (distributed representation) at a particular node. RNNCoreAnnotations.PredictedClass Get the argmax of the class predictions. RNNCoreAnnotations.PredictionError RNNCoreAnnotations.Predictions Used to denote a vector of predictions at a particular node. TopNGramRecord This
Modular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be