The paper demonstrates that the temporal speckle of broadband light can encode probabilistic weights for Bayesian neural networks, allowing a photonic processor to perform probabilistic convolutions every 37.5 ps. By programming mean and variance into independently amplitude‑bandwidth‑controlled spectral channels, the system delivers uncorrelated samples without heavy random‑number generation. The resulting ultrafast photonic AI classifies blood‑cell images while detecting out‑of‑distribution samples and distinguishing epistemic novelty from aleatoric ambiguity on standard uncertainty benchmarks.
arXiv:2608. 02229v1 Announce Type: new Abstract: Classical neural networks frequently produce overconfident predictions on ambiguous or out-of-distribution (OOD) data, a liability that grows with each AI system deployed in safety-critical real-world scenarios.
Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored.
arXiv:2606. 18713v1 Announce Type: new Abstract: Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement.
arXiv:2608.27584v1 Announce Type: new Abstract: Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robot...
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.
Single-photon avalanche diode (SPAD) cameras produce binary images where each pixel records zero or one photon arrival, making scene reconstruction a fundamentally one-to-many inverse problem. As more binary frames are accumulated, the uncertainty diminishes, transitioning from stochastic to near-deterministic scene understanding. The study introduces a conditional generative framework using a Hypergeometric frame‑thinning process to model this ambiguity and evaluates its impact on tasks such as character recognition, QR code decoding, and facial analysis.
The paper introduces stochastic activation switching, a lightweight technique that randomly selects between multiple nonlinear activation functions during inference. This approach generates predictive diversity without disabling intermediate features, enabling uncertainty estimation from a single trained model. Experiments on classification, semantic segmentation, and out‑of‑distribution detection with both convolutional and transformer architectures show improved uncertainty quality while maintaining competitive primary‑task performance.
The paper presents a pipeline for training, compiling, and deploying Bayesian neural networks (BNNs) that use a single probabilistic forward pass (PFP) instead of multiple stochastic samples. By assuming Gaussian-distributed weights and activations, PFP enables fully analytic uncertainty propagation, allowing a single deterministic forward pass to replace sampling. Using the TVM compiler and a library of Gaussian-propagating operators, the authors achieve up to 4200× speedups on small mini-batches while maintaining accuracy, uncertainty estimation, and out-of-domain detection comparable to traditional stochastic variational inference (SVI).
The article explores how biological learning and decision-making, often modeled as Bayesian processes, can be replicated in computing systems by leveraging noisy neural and synaptic dynamics for stochastic sampling. It proposes a biologically grounded framework where internal energy functions capture uncertainty over latent states and model parameters, enabling predictive coding networks to perform Markov chain Monte Carlo sampling. By drawing parallels between intrinsic biological noise and electrical noise in emerging probabilistic analogue memory technologies, the authors argue that analogue in‑memory computing hardware offers a massively scalable and energy‑efficient solution for probabilistic inference.
arXiv:2509.10756v4 Announce Type: replace-cross Abstract: We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providi...
arXiv:2606. 13818v1 Announce Type: new Abstract: This thesis investigates how Bayesian principles can deepen our understanding of modern deep learning systems.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.