对于这个问题,过去的解释说明集成可以增大随机特征映射(Random feature mapping, 这里指基于NTK(Neural Tangent Kernel )的方法)的特征空间,但是其结论难以直接扩展到深度学习中。 为了充分验证过去方法的问题,在下图中作者给出了在NTK和深度学习中进行平均训练、集成和蒸馏得到的实验结果。可以发现,两种方法在平均训练和...
And since the tangent kernel stays constant during training, the training dynamics isnow reduced toa simple linear ordinary differential equation. The authors in the seminal paper insist that the limit of the NTK is a powerful tool to understand the generalisation properties of neural networks, and ...
On the linearity of large non-linear models: when and why the tangent kernel is constant We show that the transition to linearity of the model and, equivalently, constancy of the (neural) tangent kernel (NTK) result from the scaling properties of the norm of the Hessian matrix of the netwo...
especially differently from ensemble of random feature mappings or the neural-tangent-kernel feature mappings, and is potentially out of the scope of existing theorems. Thus, to properly understand ensemble and knowledge distillation in deep learning, we develop a theory s...
The neural net experiments also have a similar flavor -- the nets before the interpolation threshold are required to reuse weights from the previous run, while the ones after the interpolation threshold do not have any such requirement. When this is removed, the results are much more muted. Th...
Inspired by the success of recent vision transformers and large kernel design in convolutional neural networks (CNNs), in this paper, we analyze and explore essential reasons for their success. We claim two factors that are critical for 3D large-scale scene understanding: a larger receptive field...
the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of ...
The CSPN network has shown good performance in real-time and thus is well-suited for applications such as robotics and autonomous driving. CSPN++ [76] is an improved version of the CSPN network with adaptively learning convolutional kernel sizes and numbers of iterations for propagation. The ...
kernel_initializer="glorot_uniform", bias_initializer="zeros", kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs ) Keras Dense Layer Hyperparameters As we can see a set of hyperparameters being used in the above synta...