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Found 9 papers, 3 papers with code
Characteristic Circuits
In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data.
HalluAudio: Hallucinating Frequency as Concepts for Few-Shot Audio Classification
Few-shot audio classification is an emerging topic that attracts more and more attention from the research community.
Probabilistic Circuits That Know What They Don't Know
In contrast to neural networks, they are often assumed to be well-calibrated and robust to out-of-distribution (OOD) data.
Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits
Probabilistic circuits (PCs) have become the de-facto standard for learning and inference in probabilistic modeling.
Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression
Inspired by recent advances in the field of expert-based approximations of Gaussian processes (GPs), we present an expert-based approach to large-scale multi-output regression using single-output GP experts.
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting
Time series forecasting is a relevant task that is performed in several real-world scenarios such as product sales analysis and prediction of energy demand.
When Few-Shot Learning Meets Video Object Detection
We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
Looking back to lower-level information in few-shot learning
In this work, we propose the utilization of lower-level, supporting information, namely the feature embeddings of the hidden neural network layers, to improve classifier accuracy.
TransMatch: A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning
Under the proposed framework, we develop a novel method for semi-supervised few-shot learning called TransMatch by instantiating the three components with Imprinting and MixMatch.
