https://arxiv.org/abs/2008.07669
 
HiPPO: Recurrent Memory with Optimal Polynomial Projections
A central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the online compression of continuous signals and discrete time series by projection onto polynomial bases. Given a measure that specifies the importance of each time step in the past, HiPPO produces an optimal solution to a natural online function approximation problem. As special cases, our framework yields a short derivation of the recent Legendre Memory Unit (LMU) from first principles, and generalizes the ubiquitous gating mechanism of recurrent neural networks such as GRUs. This formal framework yields a new memory update mechanism (HiPPO-LegS) that scales through time to remember all history, avoiding priors on the timescale. HiPPO-LegS enjoys the theoretical benefits of timescale robustness, fast updates, and bounded gradients. By incorporating the memory dynamics into recurrent neural networks, HiPPO RNNs can empirically capture complex temporal dependencies. On the benchmark permuted MNIST dataset, HiPPO-LegS sets a new state-of-the-art accuracy of 98.3%. Finally, on a novel trajectory classification task testing robustness to out-of-distribution timescales and missing data, HiPPO-LegS outperforms RNN and neural ODE baselines by 25-40% accuracy.
arxiv.org

전전문과 AI깔짝충 새끼들 거품물면서 자꾸 딴얘기하네 ㅋㅋㅋ

수학공부가 필요없으면 저런 모델들이랑 담쌓고 사는거고

병신인거 맞는데 자꾸 이악물고 아니래 ㅋㅋ

만약 LLM에 저 후속모델들이 채택되면

나는 LLM을 연구하지만 Hippo가 어떻게 작동하는지는 몰라요 해

딴데가서 LLM 연구자라고 소개해

딴새끼들이 모델 만들면 코드긁어다 니꺼인척해

하 불나방새끼들 ㅋㅋ 뭔 AI임