Latest Research in Machine Learning

Top Research Topics in Machine Learning

• Machine Learning — 3 papers
• Parallel Computing and Optimization Techniques — 2 papers
• Low-power high-performance VLSI design — 2 papers
• Neuroscience and Neuropharmacology Research — 2 papers
• Neural Networks and Applications — 1 papers
• Neural Networks and Reservoir Computing — 1 papers
• Dynamical Systems — 1 papers
• Functional Brain Connectivity Studies — 1 papers
• Artificial Intelligence — 1 papers
• Hardware Architecture — 1 papers

Highest-Cited Papers

1. Computational Primes: A Systematic Framework for Partitioning Neural Network Computation Across Analog and Digital Domains
2. Computational Primes: A Systematic Framework for Partitioning Neural Network Computation Across Analog and Digital Domains
3. Energy-efficient approximate multipliers and adders for multimedia computing applications: Design and evaluation of novel optimized arithmetic circuits with enhanced accuracy
4. SpectralNet: A Resolvent-Inspired Neural Architecture Based on Chernoff Approximations and Photonic Motivation
5. Formalized Hopfield Networks and Boltzmann Machines
6. Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Direction
7. Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Direction
8. Stability and Wandering of Bumps in Neural Fields with Interneuron Subtypes
9. Replacing Inputs of Look-Up-Table-Based Moore Finite State Machines with Two Cores of Input Memory Functions
10. E8-Resonant Synaptic Pruning Protocol via Non-Euclidean Tensor Flux Optimization — E8 Intelligence Research
11. A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance
12. Formation of structural attractors in neuromorphic systems
13. A Piecewise-Linear Approximation-based Energy-Efficient Error-Optimized Unsigned Square Rooter for Accuracy-Critical Applications
14. Research on Memristors in Hardware Security
15. How Temporal Correlations Shape Memory in Linear Recurrent Neural Networks
16. The 5-Bit Eisenstein-Norm CRT: A 96 Ops/Cycle Inference Engine for Binary Silicon
17. Adaptive self-organized criticality in deep neural networks
18. Analysis of Performance and Resource Utilization for FPGA-Based QR Decomposition Architectures
19. Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales
20. Manifold-constrained plasticity enables stable learning in recurrent neural circuits