2. The LLTSA algorithm takes the global and local structures of the dataset fully into account, which enables better clustering of irregular and inhomogeneous nonlinear data after performing dimensionality reduction. Consequently, LLTSA is chosen for feature dimensionality reduction in this paper. To ...
Traditional linear/nonlinear dimensionality reduction methods have grown explosively during the last decades, including local geometric structure preservation and global geometric structure preservation methods. The former aims to find a subspace by preserving the local geometric structure such as locally linear...
When input data is highly nonlinear, more hidden layers are required to deal with this complexity. They are mostly used in dimensionality reduction, like a non linear Principal Component Analysis. From a more mathematical approach, the encoder takes a given input x and transforms it into a ...
When working on this survey, Google Scholar was the leading search engine for finding relevant papers containing keywords such as “multi-agent” or “multiagent”, “reinforcement learning”, and “deep learning”. We cover works from leading journals, conference proceedings, relevant arXiv papers,...
Similar to multi-task learning, it needs to extract effective and sufficient information across tasks and to learn the correlation between tasks. (2) How to learn and transfer knowledge from previous tasks to the target/new task? The number of tasks in lifelong learning is possibly never ending...
(x), we let the stacked nonlinear layers fit another mapping of F(x) := H(x) − x. The original mapping is recast into F(x) + x. We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity...
Similar to the fully connected NNs, nonlinearities such as ReLU are then applied that allows us to deal with nonlinear and complicated data. The pooling operation preserves spatial invariance, downsamples and reduces the dimension of each feature map obtained after convolution. These downsampling/pooli...
(x), we let the stacked nonlinear layers fit another mapping of F(x) := H(x) − x. The original mapping is recast into F(x) + x. We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity...
The increasing interest in modeling the dynamics of ever larger proteins has revealed a fundamental problem with models that describe the molecular system as being in a global configuration state. This notion limits our ability to gather sufficient stati
(ANNs) are believed to be one of the most powerful ML frameworks. They imitate the neurons in brain structure. Owing to the nonlinear activation functions applied to each neuron unit and multi-layer structures, ANNs are capable of learning complicated relationships between inputs and outputs ...