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Sequential Image Classification On Sequential 1

Metrics

Unpermuted Accuracy

Results

Performance results of various models on this benchmark

Model Name
Unpermuted Accuracy
Paper TitleRepository
S491.80%Efficiently Modeling Long Sequences with Structured State Spaces-
SMPConv84.86%SMPConv: Self-moving Point Representations for Continuous Convolution-
Transformer (self-attention) (Trinh et al., 2018)62.2%Learning Longer-term Dependencies in RNNs with Auxiliary Losses-
Sparse Combo Net65.72RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks-
MultiresConv93.15%Sequence Modeling with Multiresolution Convolutional Memory-
LSSL84.65%Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers-
CKCNN (1M)63.74%CKConv: Continuous Kernel Convolution For Sequential Data-
LRU89.0Resurrecting Recurrent Neural Networks for Long Sequences-
LipschitzRNN64.2Lipschitz Recurrent Neural Networks-
FlexTCN-680.82%FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes-
Trellis Network73.42%Trellis Networks for Sequence Modeling-
UR-GRU74.4%Improving the Gating Mechanism of Recurrent Neural Networks-
CKCNN (100k)62.25%CKConv: Continuous Kernel Convolution For Sequential Data-
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Sequential Image Classification On Sequential 1 | SOTA | HyperAI