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Found 17 papers, 3 papers with code
Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers
Instead, adversarial attacks should be able to be implemented by physical actions, for example, placing additional false objects as scatterers around the on-ground target to perturb the SAR image and fool the SAR ATR.
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
CoDEx identifies a rich set of complex concept abstractions from natural language explanations of videos-obviating the need to predefine the amorphous set of concepts.
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Using the scoring function of FastLAS, NSL searches for short, interpretable rules that generalise over such noisy examples.
A General Framework for Distributed Inference with Uncertain Models
Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network.
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
Uncertainty-Aware Deep Classifiers using Generative Models
Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution.
Spherical Text Embedding
While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding.
Quantifying Classification Uncertainty using Regularized Evidential Neural Networks
However, an ENN is trained as a black box without explicitly considering different types of inherent data uncertainty, such as vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due to conflicting evidence).
Non-Bayesian Social Learning with Uncertain Models
Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network.
Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units
In this paper, we introduce the concept of Prior Activation Distribution (PAD) as a versatile and general technique to capture the typical activation patterns of hidden layer units of a Deep Neural Network used for classification tasks.
Uncertainty Aware AI ML: Why and How
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios.
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables.
Evidential Deep Learning to Quantify Classification Uncertainty
Deterministic neural nets have been shown to learn effective predictors on a wide range of machine learning problems.
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects.
Identifying Semantically Deviating Outlier Documents
A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus.
