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id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.10070">arXiv:2502.10070</a> <span> [<a href="https://arxiv.org/pdf/2502.10070">pdf</a>, <a href="https://arxiv.org/format/2502.10070">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/ICASSP48485.2024.10446693">10.1109/ICASSP48485.2024.10446693 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Topological Neural Networks over the Air </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fiorellino%2C+S">Simone Fiorellino</a>, <a href="/search/cs?searchtype=author&query=Battiloro%2C+C">Claudio Battiloro</a>, <a href="/search/cs?searchtype=author&query=Di+Lorenzo%2C+P">Paolo Di Lorenzo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.10070v1-abstract-short" style="display: inline;"> Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes) and allow for decentralized implementation through localized communications over different neighborhoods. Existing TNN architectures have not yet been considered in realistic communication scenarios, where channel effect… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10070v1-abstract-full').style.display = 'inline'; document.getElementById('2502.10070v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.10070v1-abstract-full" style="display: none;"> Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes) and allow for decentralized implementation through localized communications over different neighborhoods. Existing TNN architectures have not yet been considered in realistic communication scenarios, where channel effects typically introduce disturbances such as fading and noise. This paper aims to propose a novel TNN design, operating on regular cell complexes, that performs over-the-air computation, incorporating the wireless communication model into its architecture. Specifically, during training and inference, the proposed method considers channel impairments such as fading and noise in the topological convolutional filtering operation, which takes place over different signal orders and neighborhoods. Numerical results illustrate the architecture's robustness to channel impairments during testing and the superior performance with respect to existing architectures, which are either communication-agnostic or graph-based. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.10070v1-abstract-full').style.display = 'none'; document.getElementById('2502.10070v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.19719">arXiv:2411.19719</a> <span> [<a href="https://arxiv.org/pdf/2411.19719">pdf</a>, <a href="https://arxiv.org/format/2411.19719">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Relative Representations of Latent Spaces enable Efficient Semantic Channel Equalization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=H%C3%BCttebr%C3%A4ucker%2C+T">Tom谩s H眉ttebr盲ucker</a>, <a href="/search/cs?searchtype=author&query=Fiorellino%2C+S">Simone Fiorellino</a>, <a href="/search/cs?searchtype=author&query=Sana%2C+M">Mohamed Sana</a>, <a href="/search/cs?searchtype=author&query=Di+Lorenzo%2C+P">Paolo Di Lorenzo</a>, <a href="/search/cs?searchtype=author&query=Strinati%2C+E+C">Emilio Calvanese Strinati</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.19719v1-abstract-short" style="display: inline;"> In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables different agents employing different neural… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19719v1-abstract-full').style.display = 'inline'; document.getElementById('2411.19719v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.19719v1-abstract-full" style="display: none;"> In multi-user semantic communication, language mismatche poses a significant challenge when independently trained agents interact. We present a novel semantic equalization algorithm that enables communication between agents with different languages without additional retraining. Our algorithm is based on relative representations, a framework that enables different agents employing different neural network models to have unified representation. It proceeds by projecting the latent vectors of different models into a common space defined relative to a set of data samples called \textit{anchors}, whose number equals the dimension of the resulting space. A communication between different agents translates to a communication of semantic symbols sampled from this relative space. This approach, in addition to aligning the semantic representations of different agents, allows compressing the amount of information being exchanged, by appropriately selecting the number of anchors. Eventually, we introduce a novel anchor selection strategy, which advantageously determines prototypical anchors, capturing the most relevant information for the downstream task. Our numerical results show the effectiveness of the proposed approach allowing seamless communication between agents with radically different models, including differences in terms of neural network architecture and datasets used for initial training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.19719v1-abstract-full').style.display = 'none'; document.getElementById('2411.19719v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.16986">arXiv:2403.16986</a> <span> [<a href="https://arxiv.org/pdf/2403.16986">pdf</a>, <a href="https://arxiv.org/format/2403.16986">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Relative Representations for Goal-Oriented Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Fiorellino%2C+S">Simone Fiorellino</a>, <a href="/search/cs?searchtype=author&query=Battiloro%2C+C">Claudio Battiloro</a>, <a href="/search/cs?searchtype=author&query=Strinati%2C+E+C">Emilio Calvanese Strinati</a>, <a href="/search/cs?searchtype=author&query=Di+Lorenzo%2C+P">Paolo Di Lorenzo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.16986v2-abstract-short" style="display: inline;"> In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as mi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16986v2-abstract-full').style.display = 'inline'; document.getElementById('2403.16986v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.16986v2-abstract-full" style="display: none;"> In future 6G wireless networks, semantic and effectiveness aspects of communications will play a fundamental role, incorporating meaning and relevance into transmissions. However, obstacles arise when devices employ diverse languages, logic, or internal representations, leading to semantic mismatches that might jeopardize understanding. In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data. This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches via latent space alignment. We propose a dynamic optimization strategy that adapts relative representations, communication parameters, and computation resources for energy-efficient, low-latency, goal-oriented semantic communications. Numerical results demonstrate our methodology's effectiveness in mitigating mismatches among devices, while optimizing energy consumption, delay, and effectiveness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16986v2-abstract-full').style.display = 'none'; document.getElementById('2403.16986v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02441">arXiv:2402.02441</a> <span> [<a href="https://arxiv.org/pdf/2402.02441">pdf</a>, <a href="https://arxiv.org/format/2402.02441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Mathematical Software">cs.MS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation">stat.CO</span> </div> </div> <p class="title is-5 mathjax"> TopoX: A Suite of Python Packages for Machine Learning on Topological Domains </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hajij%2C+M">Mustafa Hajij</a>, <a href="/search/cs?searchtype=author&query=Papillon%2C+M">Mathilde Papillon</a>, <a href="/search/cs?searchtype=author&query=Frantzen%2C+F">Florian Frantzen</a>, <a href="/search/cs?searchtype=author&query=Agerberg%2C+J">Jens Agerberg</a>, <a href="/search/cs?searchtype=author&query=AlJabea%2C+I">Ibrahem AlJabea</a>, <a href="/search/cs?searchtype=author&query=Ballester%2C+R">Rub茅n Ballester</a>, <a href="/search/cs?searchtype=author&query=Battiloro%2C+C">Claudio Battiloro</a>, <a href="/search/cs?searchtype=author&query=Bern%C3%A1rdez%2C+G">Guillermo Bern谩rdez</a>, <a href="/search/cs?searchtype=author&query=Birdal%2C+T">Tolga Birdal</a>, <a href="/search/cs?searchtype=author&query=Brent%2C+A">Aiden Brent</a>, <a href="/search/cs?searchtype=author&query=Chin%2C+P">Peter Chin</a>, <a href="/search/cs?searchtype=author&query=Escalera%2C+S">Sergio Escalera</a>, <a href="/search/cs?searchtype=author&query=Fiorellino%2C+S">Simone Fiorellino</a>, <a href="/search/cs?searchtype=author&query=Gardaa%2C+O+H">Odin Hoff Gardaa</a>, <a href="/search/cs?searchtype=author&query=Gopalakrishnan%2C+G">Gurusankar Gopalakrishnan</a>, <a href="/search/cs?searchtype=author&query=Govil%2C+D">Devendra Govil</a>, <a href="/search/cs?searchtype=author&query=Hoppe%2C+J">Josef Hoppe</a>, <a href="/search/cs?searchtype=author&query=Karri%2C+M+R">Maneel Reddy Karri</a>, <a href="/search/cs?searchtype=author&query=Khouja%2C+J">Jude Khouja</a>, <a href="/search/cs?searchtype=author&query=Lecha%2C+M">Manuel Lecha</a>, <a href="/search/cs?searchtype=author&query=Livesay%2C+N">Neal Livesay</a>, <a href="/search/cs?searchtype=author&query=Mei%C3%9Fner%2C+J">Jan Mei脽ner</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Soham Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Nikitin%2C+A">Alexander Nikitin</a>, <a href="/search/cs?searchtype=author&query=Papamarkou%2C+T">Theodore Papamarkou</a> , et al. (18 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.02441v5-abstract-short" style="display: inline;"> We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02441v5-abstract-full').style.display = 'inline'; document.getElementById('2402.02441v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02441v5-abstract-full" style="display: none;"> We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelX is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at https://pyt-team.github.io/}{https://pyt-team.github.io/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02441v5-abstract-full').style.display = 'none'; document.getElementById('2402.02441v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.15188">arXiv:2309.15188</a> <span> [<a href="https://arxiv.org/pdf/2309.15188">pdf</a>, <a href="https://arxiv.org/format/2309.15188">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.5281/zenodo.7958513">10.5281/zenodo.7958513 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> ICML 2023 Topological Deep Learning Challenge : Design and Results </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Papillon%2C+M">Mathilde Papillon</a>, <a href="/search/cs?searchtype=author&query=Hajij%2C+M">Mustafa Hajij</a>, <a href="/search/cs?searchtype=author&query=Jenne%2C+H">Helen Jenne</a>, <a href="/search/cs?searchtype=author&query=Mathe%2C+J">Johan Mathe</a>, <a href="/search/cs?searchtype=author&query=Myers%2C+A">Audun Myers</a>, <a href="/search/cs?searchtype=author&query=Papamarkou%2C+T">Theodore Papamarkou</a>, <a href="/search/cs?searchtype=author&query=Birdal%2C+T">Tolga Birdal</a>, <a href="/search/cs?searchtype=author&query=Dey%2C+T">Tamal Dey</a>, <a href="/search/cs?searchtype=author&query=Doster%2C+T">Tim Doster</a>, <a href="/search/cs?searchtype=author&query=Emerson%2C+T">Tegan Emerson</a>, <a href="/search/cs?searchtype=author&query=Gopalakrishnan%2C+G">Gurusankar Gopalakrishnan</a>, <a href="/search/cs?searchtype=author&query=Govil%2C+D">Devendra Govil</a>, <a href="/search/cs?searchtype=author&query=Guzm%C3%A1n-S%C3%A1enz%2C+A">Aldo Guzm谩n-S谩enz</a>, <a href="/search/cs?searchtype=author&query=Kvinge%2C+H">Henry Kvinge</a>, <a href="/search/cs?searchtype=author&query=Livesay%2C+N">Neal Livesay</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Soham Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Samaga%2C+S+N">Shreyas N. Samaga</a>, <a href="/search/cs?searchtype=author&query=Ramamurthy%2C+K+N">Karthikeyan Natesan Ramamurthy</a>, <a href="/search/cs?searchtype=author&query=Karri%2C+M+R">Maneel Reddy Karri</a>, <a href="/search/cs?searchtype=author&query=Rosen%2C+P">Paul Rosen</a>, <a href="/search/cs?searchtype=author&query=Sanborn%2C+S">Sophia Sanborn</a>, <a href="/search/cs?searchtype=author&query=Walters%2C+R">Robin Walters</a>, <a href="/search/cs?searchtype=author&query=Agerberg%2C+J">Jens Agerberg</a>, <a href="/search/cs?searchtype=author&query=Barikbin%2C+S">Sadrodin Barikbin</a>, <a href="/search/cs?searchtype=author&query=Battiloro%2C+C">Claudio Battiloro</a> , et al. (31 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.15188v4-abstract-short" style="display: inline;"> This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The chal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15188v4-abstract-full').style.display = 'inline'; document.getElementById('2309.15188v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.15188v4-abstract-full" style="display: none;"> This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competition asked participants to provide open-source implementations of topological neural networks from the literature by contributing to the python packages TopoNetX (data processing) and TopoModelX (deep learning). The challenge attracted twenty-eight qualifying submissions in its two-month duration. This paper describes the design of the challenge and summarizes its main findings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.15188v4-abstract-full').style.display = 'none'; document.getElementById('2309.15188v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> </ol> <div class="is-hidden-tablet"> <!-- feedback for mobile only --> <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a> </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary"> <!-- MetaColumn 1 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