GNSN adds convection governed by a dynamic velocity field to graph message passing, adaptively balancing it with diffusion to handle varying homophily levels and reduce oversmoothing while outperforming baselines on 12 datasets.
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A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
PianoFlow generates coordinated bimanual piano motions from audio via MIDI-distilled flow-matching, asymmetric role-gated interaction, and autoregressive streaming continuation, outperforming priors with 9x faster inference.
scFM learns bidirectional velocity fields from entropically regularized OT couplings between snapshots, with added alignment and regularization to reduce drift in long-horizon predictions of single-cell trajectories.
SubFlow restores full mode coverage in one-step flow matching by conditioning on sub-modes from semantic clustering, yielding higher diversity on ImageNet-256 while preserving FID.
LITT adds a time-scaling gate to an LSTM and uses the spread of learned relative event times to discover patient trajectories, but the headline counterfactual What-If Machine is absent and the trajectory discovery is not validated against clinical ground truth.
citing papers explorer
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Graph Navier Stokes Networks
GNSN adds convection governed by a dynamic velocity field to graph message passing, adaptively balancing it with diffusion to handle varying homophily levels and reduce oversmoothing while outperforming baselines on 12 datasets.
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Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
A three-layer leaky integrate-and-fire spiking neural network estimates passive component parameters in power converters, cutting resistance error from 25.8% to 10.2% versus feedforward baselines at projected 270x lower energy on neuromorphic chips.
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PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination
PianoFlow generates coordinated bimanual piano motions from audio via MIDI-distilled flow-matching, asymmetric role-gated interaction, and autoregressive streaming continuation, outperforming priors with 9x faster inference.
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From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
scFM learns bidirectional velocity fields from entropically regularized OT couplings between snapshots, with added alignment and regularization to reduce drift in long-horizon predictions of single-cell trajectories.
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SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
SubFlow restores full mode coverage in one-step flow matching by conditioning on sub-modes from semantic clustering, yielding higher diversity on ImageNet-256 while preserving FID.
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Capture Timing-Attention of Events in Clinical Time Series
LITT adds a time-scaling gate to an LSTM and uses the spread of learned relative event times to discover patient trajectories, but the headline counterfactual What-If Machine is absent and the trajectory discovery is not validated against clinical ground truth.