4,419 research outputs found
Task-Oriented Active Sensing via Action Entropy Minimization
This work is licensed under a Creative Commons Attribution 4.0 International License.In active sensing, sensing actions are typically chosen to minimize the uncertainty of the state according to some information-theoretic measure such as entropy, conditional entropy, mutual information, etc. This is reasonable for applications where the goal is to obtain information. However, when the information about the state is used to perform a task, minimizing state uncertainty may not lead to sensing actions that provide the information that is most useful to the task. This is because the uncertainty in some subspace of the state space could have more impact on the performance of the task than others, and this dependence can vary at different stages of the task. One way to combine task, uncertainty, and sensing, is to model the problem as a sequential decision making problem under uncertainty. Unfortunately, the solutions to these problems are computationally expensive. This paper presents a new task-oriented active sensing scheme, where the task is taken into account in sensing action selection by choosing sensing actions that minimize the uncertainty in future task-related actions instead of state uncertainty. The proposed method is validated via simulations
Learning action-oriented models through active inference
Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms
Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future
Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Timely and Massive Communication in 6G: Pragmatics, Learning, and Inference
5G has expanded the traditional focus of wireless systems to embrace two new
connectivity types: ultra-reliable low latency and massive communication. The
technology context at the dawn of 6G is different from the past one for 5G,
primarily due to the growing intelligence at the communicating nodes. This has
driven the set of relevant communication problems beyond reliable transmission
towards semantic and pragmatic communication. This paper puts the evolution of
low-latency and massive communication towards 6G in the perspective of these
new developments. At first, semantic/pragmatic communication problems are
presented by drawing parallels to linguistics. We elaborate upon the relation
of semantic communication to the information-theoretic problems of
source/channel coding, while generalized real-time communication is put in the
context of cyber-physical systems and real-time inference. The evolution of
massive access towards massive closed-loop communication is elaborated upon,
enabling interactive communication, learning, and cooperation among wireless
sensors and actuators.Comment: Submitted for publication to IEEE BITS (revised version preprint
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
Machine learning methods strive to acquire a robust model during training
that can generalize well to test samples, even under distribution shifts.
However, these methods often suffer from a performance drop due to unknown test
distributions. Test-time adaptation (TTA), an emerging paradigm, has the
potential to adapt a pre-trained model to unlabeled data during testing, before
making predictions. Recent progress in this paradigm highlights the significant
benefits of utilizing unlabeled data for training self-adapted models prior to
inference. In this survey, we divide TTA into several distinct categories,
namely, test-time (source-free) domain adaptation, test-time batch adaptation,
online test-time adaptation, and test-time prior adaptation. For each category,
we provide a comprehensive taxonomy of advanced algorithms, followed by a
discussion of different learning scenarios. Furthermore, we analyze relevant
applications of TTA and discuss open challenges and promising areas for future
research. A comprehensive list of TTA methods can be found at
\url{https://github.com/tim-learn/awesome-test-time-adaptation}.Comment: Discussions, comments, and questions are all welcomed in
\url{https://github.com/tim-learn/awesome-test-time-adaptation
A Survey on Semantic Communications for Intelligent Wireless Networks
With deployment of 6G technology, it is envisioned that competitive edge of
wireless networks will be sustained and next decade's communication
requirements will be stratified. Also 6G will aim to aid development of a human
society which is ubiquitous and mobile, simultaneously providing solutions to
key challenges such as, coverage, capacity, etc. In addition, 6G will focus on
providing intelligent use-cases and applications using higher data-rates over
mill-meter waves and Tera-Hertz frequency. However, at higher frequencies
multiple non-desired phenomena such as atmospheric absorption, blocking, etc.,
occur which create a bottleneck owing to resource (spectrum and energy)
scarcity. Hence, following same trend of making efforts towards reproducing at
receiver, exact information which was sent by transmitter, will result in a
never ending need for higher bandwidth. A possible solution to such a challenge
lies in semantic communications which focuses on meaning (context) of received
data as opposed to only reproducing correct transmitted data. This in turn will
require less bandwidth, and will reduce bottleneck due to various undesired
phenomenon. In this respect, current article presents a detailed survey on
recent technological trends in regard to semantic communications for
intelligent wireless networks. We focus on semantic communications architecture
including model, and source and channel coding. Next, we detail cross-layer
interaction, and various goal-oriented communication applications. We also
present overall semantic communications trends in detail, and identify
challenges which need timely solutions before practical implementation of
semantic communications within 6G wireless technology. Our survey article is an
attempt to significantly contribute towards initiating future research
directions in area of semantic communications for intelligent 6G wireless
networks
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