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-...
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...
The ResNet architecture seems advantageous toward previous inventions at different levels: it reports better vanilla test accuracy, smaller generalization gap (difference between training and testing accuracy), and a weaker tendency in capturing HFC. Optimize...
Load the pretrained network JapaneseVowelsConvNet. This network is a pretrained 1-D convolutional neural network trained on the Japanese Vowels data set as described in [1] and [2]. Get load JapaneseVowelsConvNet View the network architecture. Get net.Layers ans = 10×1 Layer array with ...
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 ...
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 ...
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...
The advantage of such an architecture is that automatic diagnoses can be explained simply by an image and/or a few sentences. ExplAIn is evaluated at the image level and at the pixel level on various CFP image datasets. We expect this new framework, which jointly offers high classification ...