Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.
Backpropagation through time: what it does and how to do it
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
SPIKER-LL extends the open-source Spiker+ SNN accelerator with microarchitectural support for the STSF local learning rule, delivering up to 93% accuracy, sub-millisecond latency, and under 0.1 mJ per inference on MNIST variants while remaining DSP-free.
Introduces circulate-firing neurons, time-step-wise learnable surrogate gradients, and balanced loss for direct SNN training, reporting competitive results on datasets and Transformers.
citing papers explorer
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CaLiSym: Learning Symplectic Dynamics of Real-World Systems through Structured Canonical Lifts
Lifting non-conservative, actuated, and contact-constrained robot dynamics into an exactly symplectic phase-space map yields state-of-the-art out-of-distribution autoregressive rollout error at low parameter and FLOP cost.
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Scalable Quantum Reservoir Computing over Distributed Quantum Architectures
Quantum reservoir computing with distributed architectures reduces time-series forecasting errors by up to 78.8% MAE and 72.3% RMSE in NISQ simulations compared to classical methods.
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Spiker-LL: An Energy-Efficient FPGA Accelerator Enabling Adaptive Local Learning in Spiking Neural Networks
SPIKER-LL extends the open-source Spiker+ SNN accelerator with microarchitectural support for the STSF local learning rule, delivering up to 93% accuracy, sub-millisecond latency, and under 0.1 mJ per inference on MNIST variants while remaining DSP-free.
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Advancing Direct Training for Spiking Neural Networks with Circulate-Firing Neurons and Learnable Gradients
Introduces circulate-firing neurons, time-step-wise learnable surrogate gradients, and balanced loss for direct SNN training, reporting competitive results on datasets and Transformers.