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Hamiltonian Monte Carlo on ReLU Neural Networks is Inefficient
When the activation function of a network is analytic (e.g., sigmoid
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces Luis H. Cubillos
BMI applications, ranging from speech production to finger control.
Off-policy estimation with adaptively collected data: the power of online learning
Off-policy estimation: Oftentimes, one needs to estimate the linear functional based on observational data collected from a behavior policy.
Alleviating Distortion in Image Generation via Multi-Resolution Diffusion Models and Time-Dependent Layer Normalization
Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, where
DiffuPac: Contextual Mimicry in Adversarial Packets Generation via Diffusion Model
Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations.
A Practitioner's Guide to Continual Multimodal Pretraining
However, practical model deployment often operates in the gap between these two limit cases, as real-world applications demand adaptation to specific subdomains, tasks or concepts -- spread over the entire, varying life cycle of a model.
f169ec4d47933ea4896b994af8ff4f17-Paper-Conference.pdf
Empirical results show consistent and significant performance gains afforded by a singleround structurization.