Related papers: Semantic Communication of Learnable Concepts

Semantic Communication of Learnable Concepts

URL: http://arxiv.org/abs/2305.08126v1
Date: Sun, 14 May 2023 11:16:17 GMT
Title: Semantic Communication of Learnable Concepts
Authors: Francesco Pase, Szymon Kobus, Deniz Gunduz, Michele Zorzi
Abstract summary: We consider the problem of communicating a sequence of concepts, i.e., unknown and potentially maps, which can be observed only through examples. The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data. The transmitter then needs to communicate the learned models to a remote receiver through a rate-limited channel.
Score: 16.373044313375782
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We consider the problem of communicating a sequence of concepts, i.e., unknown and potentially stochastic maps, which can be observed only through examples, i.e., the mapping rules are unknown. The transmitter applies a learning algorithm to the available examples, and extracts knowledge from the data by optimizing a probability distribution over a set of models, i.e., known functions, which can better describe the observed data, and so potentially the underlying concepts. The transmitter then needs to communicate the learned models to a remote receiver through a rate-limited channel, to allow the receiver to decode the models that can describe the underlying sampled concepts as accurately as possible in their semantic space. After motivating our analysis, we propose the formal problem of communicating concepts, and provide its rate-distortion characterization, pointing out its connection with the concepts of empirical and strong coordination in a network. We also provide a bound for the distortion-rate function.

Related papers

Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
arXiv Detail & Related papers (2024-08-29T08:53:26Z)
Gaussian Mixture Models for Affordance Learning using Bayesian Networks [50.18477618198277]
Affordances are fundamental descriptors of relationships between actions, objects and effects. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences.
arXiv Detail & Related papers (2024-02-08T22:05:45Z)
Estimation of Concept Explanations Should be Uncertainty Aware [39.598213804572396]
We study a specific kind called Concept Explanations, where the goal is to interpret a model using human-understandable concepts. Although popular for their easy interpretation, concept explanations are known to be noisy. We propose an uncertainty-aware Bayesian estimation method to address these issues, which readily improved the quality of explanations.
arXiv Detail & Related papers (2023-12-13T11:17:27Z)
Rate-Distortion-Perception Theory for Semantic Communication [73.04341519955223]
We study the achievable data rate of semantic communication under the symbol distortion and semantic perception constraints. We observe that there exists cases that the receiver can directly infer the semantic information source satisfying certain distortion and perception constraints.
arXiv Detail & Related papers (2023-12-09T02:04:32Z)
Reasoning with the Theory of Mind for Pragmatic Semantic Communication [62.87895431431273]
A pragmatic semantic communication framework is proposed in this paper. It enables effective goal-oriented information sharing between two-intelligent agents. Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
arXiv Detail & Related papers (2023-11-30T03:36:19Z)
Attributing Learned Concepts in Neural Networks to Training Data [5.930268338525991]
We find evidence for convergence, where removing the 10,000 top attributing images for a concept and retraining the model does not change the location of the concept in the network. This suggests that the features that inform the development of a concept are spread in a more diffuse manner across its exemplars, implying robustness in concept formation.
arXiv Detail & Related papers (2023-10-04T20:26:59Z)
Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation [74.38561925376996]
Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph. For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
arXiv Detail & Related papers (2023-03-15T12:01:43Z)
FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
arXiv Detail & Related papers (2020-11-14T11:29:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.