Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
A stochastic approximation method
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CBEN provides paired optical-radar images with cloud occlusion, revealing 23-33 point AP drops in clear-sky trained models and 17-29 point relative gains when models are trained on cloudy data.
In overparameterized quadratic networks, one-pass SGD escapes generalization plateaus only modestly faster and selects the initialization-closest zero-loss solution due to a conserved quantity in the overlap ODEs.
PCMECL improves speech-preserving facial expression manipulation by learning personalized prompts from individual visuals and using feature differencing to align visual and semantic changes from VLMs.
The work introduces a residual noise learning framework for cross-dose PET denoising that avoids averaged mappings by estimating noise directly from low-dose inputs and shows gains over one-size-for-all and dose-specific baselines on multi-center data.
Benchmarks of MPI, gRPC, and PyTorch RPC in cross-silo FL plus a new gRPC+S3 hybrid backend deliver up to 3.8x speedup for large-model transmission under realistic network conditions.
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Escape dynamics and implicit bias of one-pass SGD in overparameterized quadratic networks
In overparameterized quadratic networks, one-pass SGD escapes generalization plateaus only modestly faster and selects the initialization-closest zero-loss solution due to a conserved quantity in the overlap ODEs.