However, concepts learned by neural networks are very difficult to understand. Rule extraction can offer a promising perspective to provide a trained connectionist architecture with explanation power and validate its output decisions. In this paper, we present a novel approach, learning-based/search-...
Load the pretrained networkJapaneseVowelsConvNet. This network is a pretrained 1-D convolutional neural network trained on the Japanese Vowels data set as described in [1] and [2]. Get loadJapaneseVowelsConvNet View the network architecture. Get net.Layers ans = 10x1 Layer array with layers: ...
We can describe neural network training up to a certain P after which the correspondence to NTK regression breaks down due to the network’s finite-width. For large P, the neural network operates in under-parameterized regime where the network initialization variance due to finite number of ...
That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we...
Changing the architecture of the explained model. Training models with different activation functions improved explanation scores. We are open-sourcing our datasets and visualization tools for GPT‑4-written explanations of all 307,200 neurons in GPT‑2, as well as code for explanation and scoring...
Oscillatory architecture of event segmentation in adults and adolescents The above analysis revealed that there are differences between adolescents and adults in event segmentation; that is in the probability to set a segment boundary based on information presented in the movie. For the neurophysiological...
In spite of the simplicity of its architecture, the attractor neural network might be considered to mimic human behavior in the meaning of semantic memory organization and its disorder. Although this model could explain various phenomenon in cognitive neuropsychology, it might become obvious that this...
It's a DDPM model, with the UNet architecture as a backbone, trained to perform denoising in 1000 steps with the linear noise schedule from 0.0001 to 0.02. I'll explain later what all these words mean. It's been trained on theSmithsonian Butterflies dataset. It can unconditionally generate ...
. Machine learning is used to produce "good enough" classification systems that can handle vast quantities of information in a way that is more scalable than human labor; however, the tremendous volumes of data and the neural network architecture make it difficult or impossible to debug the ...
Fully convolutional network Fully convolutional networks owe their name to their architecture, which is built only from locally connected layers, such as convolution, pooling and upsampling. Note that no dense layer is used in this kind of architecture. This reduce the number of parameters and com...