backpropagation through time
PulseAugur coverage of backpropagation through time — every cluster mentioning backpropagation through time across labs, papers, and developer communities, ranked by signal.
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New research explores local learning and continuous dynamics in Spiking Neural Networks
Two new research papers explore advancements in Spiking Neural Networks (SNNs). The first paper introduces a novel local learning framework inspired by Neural Activity Synchronization, which reduces computational comple…
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Looped Transformers: Full Backpropagation Outperforms Truncation in Scaling Study
A new study on looped transformers suggests that fully training all loops, rather than truncating backpropagation, yields better performance when model size is flexible. Researchers trained nearly 600 models to develop …
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FPGA-based Spiking Neural Network enables on-chip training and classification
Researchers have developed a novel FPGA-based Spiking Neural Network (SNN) designed for efficient spatiotemporal pattern recognition and on-device adaptation. This implementation supports both inference and training dir…
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New method improves autoregressive forecasting by reliability-weighting gradients
Researchers have introduced Internal Dual-Wiener routing (Internal-DW), a novel method for improving autoregressive forecasting by addressing the unreliability of distant gradients in backpropagation through time (BPTT)…
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New RDNN model uses divisive normalization for continuous working memory
Researchers have introduced the Recurrent Divisive Normalization Network (RDNN), a novel model inspired by biological divisive normalization, to address limitations in artificial neural networks for continuous working m…
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New arXiv papers explore disconnect between neural network computation and learning
Two new arXiv papers explore the dynamics of neural computation, focusing on the divergence between complex forward computation and simpler learning mechanisms. The first paper introduces a "Generation-Fact Graph" to un…
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New Recurrent Network Model Mimics Brain Computation for Working Memory
Researchers have introduced the Recurrent Divisive Normalization Network (RDNN), a novel artificial neural network model inspired by biological divisive normalization. This model is designed to overcome the limitations …
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New RNNs bypass gradient descent using Koopman operator theory
Researchers have developed Koopman-informed recurrent neural networks (RNNs) that bypass traditional gradient-based training methods like backpropagation through time. This novel approach combines random feature network…
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New Unified Complex-valued Neural Network integrates continuous and event-driven learning
Researchers have developed a novel Unified Complex-valued Neural Network (UCNN) that integrates continuous-valued representations with event-driven temporal processing. This new model, based on the Unified Complex-value…
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New method trains recurrent networks without recurrence
Researchers have developed a new method called Supervised Memory Training (SMT) to pretrain recurrent neural networks (RNNs) without relying on traditional recurrence. SMT trains RNNs by reducing the process to supervis…
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LSTM networks overcome RNN memory limitations with gating mechanisms
The Long Short-Term Memory (LSTM) network was developed to address the limitations of traditional Recurrent Neural Networks (RNNs) in handling sequential data. Vanilla RNNs struggle with remembering information over lon…