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Unfortunately the source code is currently not open sourced. Some more details at (https://www.researchgate.net/publication/370980395_A_NEURAL_INVERSE_TECHNIQUE
by nutanc 3y ago
Unfortunately the source code is currently not open sourced. Some more details at (https://www.researchgate.net/publication/370980395_A_NEURAL_INVERSE_TECHNIQUE_FOR_PATTERN_CLASSIFICATION_AND_MEMORY_RECALL https://www.researchgate.net/publication/370980395_A_NEURAL_...), the source code is built on top of this.
The approach is used to solve other problems and papers have been published under https://www.researchgate.net/profile/K-Eswaran https://www.researchgate.net/profile/K-Eswaran
We are currently trying a build a full fledged LLM using just this approach(no LLM training etc) and also an ASR. We should have something to share in a couple of months.
- licnep 3y agoAm I missing something or is this just a linear transformation? It says here ( https://www.researchgate.net/publication/370980395_A_NEURAL_INVERSE_TECHNIQUE_FOR_PATTERN_CLASSIFICATION_AND_MEMORY_RECALL https://www.researchgate.net/publication/370980395_A_NEURAL_... ) that each layer can be represented as a matrix multiplication (equation 3): Ax = s So concatenating multiple layers could just be reduced to a single matrix multiplication? If there is no non-linearity I don't see how this could replace neural networks, or am I missing something?
- nutanc 3y agoThe attempt is not to replace a particular neural network which has already been trained by using Sigmoid or Rel functions. If one does this then one would necessarily have to use non-linear maps. The whole point is that such a non-linear technique is not necessary for classifications. It is not necessary to confine clusters by hyperplanes for solving a classification problem. Our focus is on individual points. We believe the brain does not do nonlinear maps!