multilayer perceptrons
Multilayer perceptrons (MLPs) are a class of feedforward neural networks that consist of multiple layers of neurons, allowing them to learn complex representations of data. MLPs are foundational elements in deep learning and are commonly used for tasks like classification and regression.
- AlignVLM: Bridging Vision and Language Latent Spaces for Multimodal Document Understanding
- FLOWING: Implicit Neural Flows for Structure-Preserving Morphing
- Optimal Minimum Width for the Universal Approximation of Continuously Differentiable Functions by Deep Narrow MLPs
- Scaling can lead to compositional generalization
- Spatially-aware Weights Tokenization for NeRF-Language Models
- Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders
- Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks