Comments (1)
This model seems somewhat different from S4 in spirit. The memorization mechanism seems more similar to the line of work on memory augmented neural networks (MANN), where the memory mechanisms are based on heuristic memory banks. In comparison, S4's mechanism has a precise mathematical interpretation of function reconstruction. I do think that S4's mechanism might not be sufficient for all settings and some form of memory augmentation could be useful.
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Related Issues (20)
- The Issue only occurs in the aan dataset HOT 1
- Using Neumann series to compute the DFT of basis kernels directly HOT 5
- Several examples doesn't work (Sashimi checkpoints / sampleRNN training) HOT 4
- information mismatch in s4/models/s4/experiments.md
- Paper, Table 1, Convolution number of parameters HOT 2
- About `krylov()` HOT 1
- Missing or misplaced "old" config folder? HOT 4
- "pretrained_model" is not defined before being called in train.py HOT 2
- Question on HMDB51 Dataset (S4ND Video Experiment)
- Unable to generate the weather using generate.py with time Series training checkpoint
- Large difference of inference result between forward and step
- AttributeError: 'SSMKernelDPLR' object has no attribute 'kernel' HOT 1
- Training on 12bits audio instead of 8bit? (Question, what do I need to change?)
- S4 Listops have nan loss HOT 2
- Quantization for S4/ Hippo
- The dynamics of the latent state of the model
- segmentation fault when running python -m train pipeline=mnist model=s4 HOT 1
- how to use the S4Block .step()
- KeyError in train.py self.dataset = SequenceDataset.registry[self.hparams.dataset._name_]
- Why is Sashimi's effect in speech signal enhancement (denoisy) so bad?
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