Note that no dense layer is used in this kind of architecture. This reduce the number of parameters and computation time. Also the network can work regardless of the original image size, without requiring any fixed number of units at any stage, given that all connections are local. To ...
Model architecture: We tested LeNet [37], AlexNet [34], VGG [52], and ResNet [23]. The ResNet architecture seems advantageous toward previous inventions at different levels: it reports better vanilla test accuracy, smaller generalization gap (diffe...
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 usi...
To illustrate the non-SQL related portions of this post, I'll be using a ready-to-use, pretrained model that I found on HuggingFace. This model is calledgnokit/ddpm-butterflies-64. It's a DDPM model, with the UNet architecture as a backbone, trained to perform denoising in 1000 steps ...
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 ...
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...
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 ...
A theoretical understanding of generalization remains an open problem for many machine learning models, including deep networks where overparameterization leads to better performance, contradicting the conventional wisdom from classical statistics. Here,
As a consequence of this exposure, (1) Receptive fields shrink and come into spatial register, and (2) SC neurons gained the adult characteristic integrative properties: enhancement, depression, and inverse effectiveness. Importantly, the unique architecture of the model guided the development so ...