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8 results for "computation"
- ai · arxiv/cs.LG · 4 min
Selective-Update RNNs Match Transformers While Using Less Memory
A new RNN architecture learns when to update internal state, preserving memory across long sequences and reducing computational waste on redundant input.
May 3, 2026 Read → - ai · arxiv/cs.LG · 4 min
Efficient Rationale Retrieval via Student-Teacher Distillation
Rabtriever reduces computational cost of LLM-based document ranking by distilling cross-encoder knowledge into independent query-document encoders.
Apr 28, 2026 Read → - engineering · arxiv/cs.LG · 8 min
Learning turbulence closures via nudging sidesteps solver backprop
A data-assimilation-inspired approach trains neural network turbulence models on DNS data without embedding them in solvers, reducing computational cost and improving stability.
Apr 28, 2026 Read → - ai · arxiv/cs.AI · 5 min
Fast Entropic Approximations cut entropy computation by 37x
Horenko et al. propose non-singular rational approximations of Shannon entropy and KL divergence that preserve mathematical properties while reducing computation cost and improving ML model training.
Apr 27, 2026 Read → - ai · arxiv/cs.AI · 8 min
GEM activation functions match ReLU speed with smoother gradients
Krause proposes rational activation functions with tunable smoothness that reduce optimization friction in deep networks while maintaining computational efficiency.
Apr 24, 2026 Read → - engineering · arxiv/cs.LG · 8 min
Routing Optimization for Satellite Federated Learning: Tractable Boundaries
Researchers map which routing problems in orbital federated learning can be solved efficiently and which are computationally hard.
Apr 22, 2026 Read → - ai · arxiv/cs.LG · 8 min
Chromatic Clustering Requires New Algorithms to Match Standard Performance
Adding color constraints to correlation clustering increases computational difficulty; a new coupled approach recovers optimal approximation bounds.
Apr 20, 2026 Read → - ai · arxiv/cs.LG · 8 min
Foundation Models vs. Task-Specific ML in Electricity Price Forecasting
Time series foundation models outperform traditional deep learning on probabilistic forecasts, but well-tuned conventional models remain competitive at lower computational cost.
Apr 17, 2026 Read →