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Found 7 papers, 1 papers with code
Analog Bayesian neural networks are insensitive to the shape of the weight distribution
Recent work has demonstrated that Bayesian neural networks (BNN's) trained with mean field variational inference (MFVI) can be implemented in analog hardware, promising orders of magnitude energy savings compared to the standard digital implementations.
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty
Bayesian neural networks (BNNs) are an important type of neural network with built-in capability for quantifying uncertainty.
Generative Adversarial Network
Out of Distribution (OOD) Detection
+1
On the Accuracy of Analog Neural Network Inference Accelerators
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference.
Device-aware inference operations in SONOS nonvolatile memory arrays
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference.
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e. g. accuracy continues to scale with parameters and complexity in the face of potential disturbance.
Probability Series Expansion Classifier that is Interpretable by Design
This work presents a new classifier that is specifically designed to be fully interpretable.
Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator
A detailed circuit and device-level analysis of energy, latency, area, and accuracy are given and compared to relevant designs using standard digital ReRAM and SRAM operations.
