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 usi...
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. Optimizer...
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...
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...
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 ...
Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. Specifically, we'll design a neural network architecture such that we impose a bottleneck in the network which forces a compressed knowledge representation of the original ...
FireXplainNet: Optimizing Convolution Block Architecture for Enhanced Wildfire Detection and InterpretabilityCONVOLUTIONAL neural networksFIRE detectorsEMERGENCY managementWILDFIRE preventionWILDFIRESENVIRONMENTAL disastersENVIRONMENTAL monitoringThe early detection of wildfires is a crucial challenge in environmental ...
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 ...
Because of its symmetry, the network has a large number of feature maps in the up-sampling path, which allows to transfer information. By comparison, the basic FCN architecture only had number of classes feature maps in its up-sampling path. U-Net architecture is separated in 3 parts: 1 ...
LSTM is a type of recurrent neural networks that allows modelling temporal dynamic behaviour by incorporating feedback connections in their architecture (Fig. 1). The choice of LSTM was mainly motivated by the sequential nature of the data. We did not explore models with larger capacity due to ...