QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
Finding structure in time,
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LSTM-MAS uses a chained multi-agent architecture modeled on LSTM input, forget, and output gates to improve long-context QA performance and reduce hallucinations compared with prior multi-agent baselines.
A structured review organizes deep learning models for electricity price forecasting via a backbone-head-loss taxonomy and identifies gaps in intraday and balancing market applications.
citing papers explorer
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QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
QLAM extends state-space models with quantum superposition in the hidden state for linear-time long-sequence modeling and reports consistent gains over RNN and transformer baselines on sequential image tasks.
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LSTM-MAS: A Long Short-Term Memory Inspired Multi-Agent System for Long-Context Understanding
LSTM-MAS uses a chained multi-agent architecture modeled on LSTM input, forget, and output gates to improve long-context QA performance and reduce hallucinations compared with prior multi-agent baselines.
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Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets
A structured review organizes deep learning models for electricity price forecasting via a backbone-head-loss taxonomy and identifies gaps in intraday and balancing market applications.