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Found 33 papers, 8 papers with code
Compact and De-biased Negative Instance Embedding for Multi-Instance Learning on Whole-Slide Image Classification
Using this free annotation, we introduce a semi-supervision signal to de-bias the inter-slide variability and to capture the common factors of variation within normal patches.
Exploring the Privacy-Energy Consumption Tradeoff for Split Federated Learning
However, since the model is split at a specific layer, known as a cut layer, into both client-side and server-side models for the SFL, the choice of the cut layer in SFL can have a substantial impact on the energy consumption of clients and their privacy, as it influences the training burden and the output of the client-side models.
First-Order Stable Model Semantics with Intensional Functions
We extend the first-order stable model semantics by Ferraris, Lee, and Lifschitz to allow intensional functions -- functions that are specified by a logic program just like predicates are specified.
On Loop Formulas with Variables
Recently Ferraris, Lee and Lifschitz proposed a new definition of stable models that does not refer to grounding, which applies to the syntax of arbitrary first-order sentences.
Causal Laws and Multi-Valued Fluents
This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz.
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning
The more new features that are being added to smartphones, the harder it becomes for users to find them.
Elementary Sets for Logic Programs
We propose the notion of an elementary set, which is almost equivalent to the notion of an elementary loop for nondisjunctive programs, but is simpler, and, unlike elementary loops, can be extended to disjunctive programs without producing unintuitive results.
Safe Formulas in the General Theory of Stable Models
Safe first-order formulas generalize the concept of a safe rule, which plays an important role in the design of answer set solvers.
NeurASP: Embracing Neural Networks into Answer Set Programming
We present NeurASP, a simple extension of answer set programs by embracing neural networks.
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
It only needs a few examples to guide the LLM's adaptation to a specific task, along with reusable ASP knowledge modules that can be applied to multiple tasks.
Leveraging Large Language Models to Generate Answer Set Programs
Specifically, we employ an LLM to transform natural language descriptions of logic puzzles into answer set programs.
Injecting Logical Constraints into Neural Networks via Straight-Through Estimators
Injecting discrete logical constraints into neural network learning is one of the main challenges in neuro-symbolic AI.
Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer
Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints.
Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention
Electronic Health Record (EHR) provides abundant information through various modalities.
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution
Accurate time prediction of patients' critical events is crucial in urgent scenarios where timely decision-making is important.
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.
A Probabilistic Extension of Action Language BC+
We present a probabilistic extension of action language BC+.
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.
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.
