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Found 19 papers, 1 papers with code
Self-supervised predictive coding and multimodal fusion advance patient deterioration prediction in fine-grained time resolution
Therefore, in this study, we propose an hourly prediction method of critical events in ED, i. e., mortality and vasopressor need.
Moving from 2D to 3D: volumetric medical image classification for rectal cancer staging
In this study, we present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge
Text-to-Speech (TTS) services that run on edge devices have many advantages compared to cloud TTS, e. g., latency and privacy issues.
Dual Attention in Time and Frequency Domain for Voice Activity Detection
The results show that the focal loss can improve the performance in various imbalance situations compared to the cross entropy loss, a commonly used loss function in VAD.
Strong Equivalence for LPMLN Programs
We show that the verification of strong equivalence in LPMLN can be reduced to equivalence checking in classical logic via a reduct and choice rules as well as to equivalence checking under the "soft" logic of here-and-there.
Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language
In this paper, we present a unified framework to integrate icorpp's reasoning and planning components.
Explainable Fact Checking with Probabilistic Answer Set Programming
One challenge in fact checking is the ability to improve the transparency of the decision.
Elaboration Tolerant Representation of Markov Decision Process via Decision-Theoretic Extension of Probabilistic Action Language pBC+
Alternatively, the semantics of pBC+ can also be defined in terms of Markov Decision Process (MDP), which in turn allows for representing MDP in a succinct and elaboration tolerant way as well as to leverage an MDP solver to compute pBC+.
Reducing the Model Variance of a Rectal Cancer Segmentation Network
As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0. 90; data augmentation further reduced the variance by a factor of 0. 89.
Weight Learning in a Probabilistic Extension of Answer Set Programs
Learning in LPMLN is in accordance with the stable model semantics, thereby it learns parameters for probabilistic extensions of knowledge-rich domains where answer set programming has shown to be useful but limited to the deterministic case, such as reachability analysis and reasoning about actions in dynamic domains.
Translating LPOD and CR-Prolog2 into Standard Answer Set Programs
Logic Programs with Ordered Disjunction (LPOD) is an extension of standard answer set programs to handle preference using the construct of ordered disjunction, and CR-Prolog2 is an extension of standard answer set programs with consistency restoring rules and LPOD-like ordered disjunction.
A Probabilistic Extension of Action Language BC+
We present a probabilistic extension of action language BC+.
Representing Hybrid Automata by Action Language Modulo Theories
We first show how to represent linear hybrid automata with convex invariants by an action language modulo theories.
Computing LPMLN Using ASP and MLN Solvers
LPMLN is a recent addition to probabilistic logic programming languages.
On the Semantic Relationship between Probabilistic Soft Logic and Markov Logic
Markov Logic Networks (MLN) and Probabilistic Soft Logic (PSL) are widely applied formalisms in Statistical Relational Learning, an emerging area in Artificial Intelligence that is concerned with combining logical and statistical AI.
Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming
Based on the discovery that circumscription and the stable model semantics coincide on a class of canonical formulas, we reformulate the situation calculus and the event calculus in the general theory of stable models.
First-Order Stable Model Semantics and First-Order Loop Formulas
Lin and Zhaos theorem on loop formulas states that in the propositional case the stable model semantics of a logic program can be completely characterized by propositional loop formulas, but this result does not fully carry over to the first-order case.
A Functional View of Strong Negation in Answer Set Programming
The distinction between strong negation and default negation has been useful in answer set programming.
