<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>Machine Learning Journal Club</title>
	<atom:link href="https://www.mljc.it/feed/" rel="self" type="application/rss+xml" />
	<link>https://www.mljc.it</link>
	<description></description>
	<lastBuildDate>Sat, 04 Nov 2023 09:12:12 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=5.5.15</generator>

<image>
	<url>https://www.mljc.it/wp-content/uploads/2021/04/cropped-new_logo-2-32x32.png</url>
	<title>Machine Learning Journal Club</title>
	<link>https://www.mljc.it</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Research Project: the flower paradigm and transformer classifier for EEG data</title>
		<link>https://www.mljc.it/2023/11/04/research-project-the-flower-paradigm-and-transformer-classifier-for-eeg-data/</link>
					<comments>https://www.mljc.it/2023/11/04/research-project-the-flower-paradigm-and-transformer-classifier-for-eeg-data/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Sat, 04 Nov 2023 09:12:12 +0000</pubDate>
				<category><![CDATA[Projects and technical updates]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2096</guid>

					<description><![CDATA[The flower paradigm and a transformer classifier for EEG dataThe primary objective of the project is the development of a transformer model for the classification of EEG-related data. Subsequently, our inquiry delves into the crucial aspect of preprocessing, seeking to elucidate its pivotal role in influencing the overall efficacy and performance of the aforementioned architecture. [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>The flower paradigm and a transformer classifier for EEG data<br>The primary objective of the project is the development of a transformer model for the classification of EEG-related data. Subsequently, our inquiry delves into the crucial aspect of preprocessing, seeking to elucidate its pivotal role in influencing the overall efficacy and performance of the aforementioned architecture. To rigorously examine this dimension, we intend to undertake a systematic comparative analysis. This entails the initial classification task on extensively preprocessed data, followed by a parallel evaluation on data subjected to minimal preprocessing, and ultimately, the assessment of the classifier&#8217;s effectiveness on raw, unprocessed data.</p>



<figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="617" src="https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-48.jpg" alt="" class="wp-image-2098" srcset="https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-48.jpg 1024w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-48-300x181.jpg 300w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-48-768x463.jpg 768w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<p>Looking beyond the immediate confines of our research focus, the broader vision of the work is oriented towards pioneering a fresh and innovative paradigm within the domain of assistive communication technology, particularly within the context of speller technology designed for individuals with communication impairments as ALS patients. This paradigm represents a pivotal shift, characterized by a fundamental reliance on the incorporation of Large Language Models (LLMs), which form the core computational foundation supporting the design and functionality of these assistive communication tools. Advanced LLMs, with their remarkable natural language processing capabilities, hold the potential to revolutionize the landscape of speller technology, offering enhanced communicative potential and accessibility for those individuals who rely on such systems for effective interaction and expression of their thoughts and needs.</p>



<figure class="wp-block-image size-large is-resized"><img loading="lazy" src="https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49-726x1024.jpg" alt="" class="wp-image-2097" width="320" height="451" srcset="https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49-726x1024.jpg 726w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49-213x300.jpg 213w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49-768x1083.jpg 768w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49-1090x1536.jpg 1090w, https://www.mljc.it/wp-content/uploads/2023/11/PHOTO-2023-10-31-11-35-49.jpg 1135w" sizes="(max-width: 320px) 100vw, 320px" /></figure>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/11/04/research-project-the-flower-paradigm-and-transformer-classifier-for-eeg-data/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Research Project on Continual Learning</title>
		<link>https://www.mljc.it/2023/10/13/research-project-on-continual-learning/</link>
					<comments>https://www.mljc.it/2023/10/13/research-project-on-continual-learning/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Fri, 13 Oct 2023 15:39:11 +0000</pubDate>
				<category><![CDATA[Projects and technical updates]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2088</guid>

					<description><![CDATA[by Luca Bottero Our interests center around the intriguing domain of Continual Learning, a subset of machine learning focused on training models to adapt and evolve over time. This involves enabling these models to learn from new data while retaining previously acquired knowledge. In today’s fast-paced world, characterized by constant changes in data and knowledge, [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>by Luca Bottero</p>



<p>Our interests center around the intriguing domain of Continual Learning, a subset of machine learning focused on training models to adapt and evolve over time. This involves enabling these models to learn from new data while retaining previously acquired knowledge. In today’s fast-paced world, characterized by constant changes in data and knowledge, Continual Learning assumes primary importance. Traditional machine learning models often encounter challenges when confronted with novel information, a phenomenon commonly referred to as catastrophic forgetting. Continual Learning presents a remedy to this issue by facilitating continuous learning, rendering models adaptable, efficient, and adept at handling evolving data environments.</p>



<p>Currently, we are working in a project that applies these Continual Learning techniques to gravitational wave interferometer data. We aspire to develop adaptive models that can learn from new experimental runs while retaining knowledge learned from prior detections.</p>



<p>In a world characterized by relentless technological advancements and cease- less data generation, the concept of Continual Learning remains pivotal. The Turin Machine Learning Journal Club could serves as your gateway to explore, study, and employ Continual Learning techniques, empowering you to have an impact on the continually evolving landscape of machine learning. We eagerly anticipate your participation in our journey through the realm of Continual Learning.</p>



<p><strong>Resources:</strong></p>



<p><a href="https://arxiv.org/abs/1909.08383">https://arxiv.org/abs/1909.08383</a></p>



<p><a href="https://avalanche.continualai.org/">https://avalanche.continualai.org/</a></p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/10/13/research-project-on-continual-learning/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Machine Learning for Anomaly detection in Prompt Gamma Spectra</title>
		<link>https://www.mljc.it/2023/10/10/machine-learning-for-anomaly-detection-in-prompt-gamma-spectra/</link>
					<comments>https://www.mljc.it/2023/10/10/machine-learning-for-anomaly-detection-in-prompt-gamma-spectra/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Tue, 10 Oct 2023 15:47:55 +0000</pubDate>
				<category><![CDATA[Projects and technical updates]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2090</guid>

					<description><![CDATA[by Beatrice Villata Tumor treatment can be achieved with radiotherapy techniques by irradiating the target volumes with ionizing radiation. Conventionally, photons are the selected particles for the treatment, but in recent years interest in the use of charged particles has been steadily growing in medical physics. The energy deposition of particle therapy allows to spare [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>by Beatrice Villata</p>



<p>Tumor treatment can be achieved with radiotherapy techniques by irradiating the target volumes with ionizing radiation. Conventionally, photons are the selected particles for the treatment, but in recent years interest in the use of charged particles has been steadily growing in medical physics. The energy deposition of particle therapy allows to spare the organ at risk around the target volume. At the same time, the energy deposition of particle therapy is greatly influenced by range uncertainties provoked by motion or variations in tissue density along the path, requiring precise treatment planning and monitoring techniques to ensure accurate and effective therapy delivery.<br>One way to achieve this is by analyzing the secondary particles created in the interactions between the charged particles and the tissues. Prompt gammas are candidates for this technique because their creation occurs instantaneously on the interaction site and can escape the patient. For these reasons, prompt gammas are not affected by washout effects and can be easily detected outside the patient.<br>Knowledge about the irradiated tissues can be recovered by analyzing Prompt Gamma emissions spectra. In the course of this project, we look for spectral abnormalities that could be related to the irradiated tissue and the range of irradiation. The dataset is created with a Geant4 Monte Carlo simulation, and an additional analysis is performed on the dataset presented in the paper “Towards Machine Learning aided<br>real‐time range imaging in proton therapy” [1].<br>The spectra are analyzed with a variational autoencoder (VAE). The final objective is to carry on this analysis online, and the final architecture will be coded on an FPGA to trigger in real-time a beam gating system.</p>



<p>[1] Lerendegui-Marco, J. et al. (2022) ‘Towards machine learning aided real-time range imaging in Proton therapy’, Scientific Reports, 12(1). doi:10.1038/s41598-022-06126-6.</p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/10/10/machine-learning-for-anomaly-detection-in-prompt-gamma-spectra/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>G.Tec BR41N Hackathon 2023</title>
		<link>https://www.mljc.it/2023/04/23/g-tec-br41n-hackathon-2023/</link>
					<comments>https://www.mljc.it/2023/04/23/g-tec-br41n-hackathon-2023/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Sun, 23 Apr 2023 18:47:30 +0000</pubDate>
				<category><![CDATA[Events, meetings and conferences]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2077</guid>

					<description><![CDATA[This weekend 5 of our members (Carola Caivano, Matteo Allione, Matteo Gallo, Marco Casari, Michele Romani) challenged themselves by participating in the hackathon https://www.br41n.io/ organised by g.tec medical engineering GmbH.It was a formative and motivating experience that allowed us to explore the application of CNNs in the field of Brain computer interface, in particular in [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>This weekend 5 of our members (Carola Caivano, Matteo Allione, Matteo Gallo, Marco Casari, Michele Romani) challenged themselves by participating in the hackathon <a href="https://www.br41n.io/">https://www.br41n.io/</a> organised by <a href="https://www.linkedin.com/company/gtec-medical-engineering/">g.tec medical engineering GmbH</a>.<br>It was a formative and motivating experience that allowed us to explore the application of CNNs in the field of Brain computer interface, in particular in P300 speller challenge.<br>The results we gained were very accurate and extremely significant and encourage us to carry out a line of research to exploit Transformer models for Causal Language generation, in order to improve the efficiency of spellers for patients who are unable to communicate autonomously.</p>



<p>Special thanks for the support to: <a href="https://www.linkedin.com/company/pompei-student-lab/">Pompei Student Lab</a><a href="https://www.linkedin.com/company/npo-torino/">NPO Torino s.r.l.</a><a href="https://www.linkedin.com/company/hpc4ai/">HPC4AI</a></p>



<p><a href="https://www.linkedin.com/feed/hashtag/?keywords=braincomputerinterfaces&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7055971439186747392">#braincomputerinterfaces</a><a href="https://www.linkedin.com/feed/hashtag/?keywords=deeplearning&amp;highlightedUpdateUrns=urn%3Ali%3Aactivity%3A7055971439186747392">#deeplearning</a></p>



<figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="578" src="https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-1024x578.jpeg" alt="" class="wp-image-2080" srcset="https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-1024x578.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-300x169.jpeg 300w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-768x434.jpeg 768w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-1536x867.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43.jpeg 1984w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<figure class="wp-block-image size-large"><img loading="lazy" width="970" height="543" src="https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-2.jpeg" alt="" class="wp-image-2078" srcset="https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-2.jpeg 970w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-2-300x168.jpeg 300w, https://www.mljc.it/wp-content/uploads/2023/04/WhatsApp-Image-2023-04-23-at-20.03.43-2-768x430.jpeg 768w" sizes="(max-width: 970px) 100vw, 970px" /></figure>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/04/23/g-tec-br41n-hackathon-2023/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Outreach event &#8220;Machine Learning, Spesa &#038; ChatGPT, cosa hanno in comune&#8221;</title>
		<link>https://www.mljc.it/2023/02/09/outreach-event-machine-learning-spesa-chatgpt-cosa-hanno-in-comune/</link>
					<comments>https://www.mljc.it/2023/02/09/outreach-event-machine-learning-spesa-chatgpt-cosa-hanno-in-comune/#respond</comments>
		
		<dc:creator><![CDATA[Valerio Pagliarino]]></dc:creator>
		<pubDate>Thu, 09 Feb 2023 13:09:27 +0000</pubDate>
				<category><![CDATA[Events, meetings and conferences]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2062</guid>

					<description><![CDATA[]]></description>
										<content:encoded><![CDATA[
<p></p>



<div class="wp-block-image"><figure class="aligncenter size-large"><img loading="lazy" width="1024" height="726" src="https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023-1024x726.jpeg" alt="" class="wp-image-2063" srcset="https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023-1024x726.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023-300x213.jpeg 300w, https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023-768x545.jpeg 768w, https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023-1536x1090.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2023/02/chatGPToutreachTesisquare2023.jpeg 1748w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></div>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/02/09/outreach-event-machine-learning-spesa-chatgpt-cosa-hanno-in-comune/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>New Machine Learning courses from the MLJC in A.Y. 2022-23</title>
		<link>https://www.mljc.it/2023/01/20/new-machine-learning-courses-from-the-mljc-in-a-y-2022-23/</link>
					<comments>https://www.mljc.it/2023/01/20/new-machine-learning-courses-from-the-mljc-in-a-y-2022-23/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Fri, 20 Jan 2023 11:37:10 +0000</pubDate>
				<category><![CDATA[Projects and technical updates]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2039</guid>

					<description><![CDATA[An outstanding opportunity to learn Machine Learning with an &#8220;hands-on&#8221; approach and get in touch with the Machine Learning Journal Club association. Introduction to Machine Learning Language: Italian Corso articolato in 10 lezioni di circa 2h tenuto presso il Dipartimento di Fisica in orario 16-18.Dopo una prima introduzione al Machine Learning, il corso si focalizzerà [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p><strong>An outstanding opportunity to learn Machine Learning with an &#8220;hands-on&#8221; approach and get in touch with the Machine Learning Journal Club association</strong>.</p>



<hr class="wp-block-separator"/>



<h4>Introduction to Machine Learning</h4>



<p><strong>Language: Italian</strong></p>



<p>Corso articolato in <strong>10 lezioni di circa 2h</strong> tenuto presso il <a href="https://www.df.unito.it/do/home.pl"><strong>Dipartimento di Fisica </strong></a>in<strong> orario 16-18</strong>.<br>Dopo una prima introduzione al Machine Learning, il corso si focalizzerà sulla comprensione e l&#8217;implementazione di algoritmi in linguaggio Python, dai più semplici quali Linear e Logistic Regression fino alle Reti Neurali. Nell&#8217;ultima parte del corso verrà introdotto il tema del Continual Learning.<br>Rendiamo disponibili le registrazioni delle lezioni sul nostro <a href="https://www.youtube.com/@machinelearningjournalclub" target="_blank" rel="noreferrer noopener">canale YouTube</a>.<br><strong>Per iscriversi inviare una email a </strong>info (at) mljc (dot) it <strong>, le date verranno comunicate man mano agli iscritti.</strong></p>



<hr class="wp-block-separator"/>



<h4>Introduction to ML for Brain Computer Interfaces</h4>



<p><strong>Language: Italian</strong></p>



<p>Corso articolato in <strong>5 lezioni di circa 2h</strong> tenuto presso il <a href="https://www.df.unito.it/do/home.pl"><strong>Dipartimento di Fisica</strong></a> in <strong>orario 17-19</strong>.<br>Si parte dalle basi dell&#8217;analisi di segnali EEG, illustrando varie tecniche e aspetti del preprocessing per arrivare a comprendere e implementare algoritmi di feature extraction e  machine learning utili in problemi legati alle Brain Computer Interface.<br>Un incontro sarà dedicato al montaggio dei nostri caschetti EEG e all&#8217;acquisizione di segnali.<br>Rendiamo disponibili le registrazioni delle lezioni sul nostro <a rel="noreferrer noopener" href="https://www.youtube.com/@machinelearningjournalclub" target="_blank">canale YouTube</a> mentre il codice è pubblicato su GitHub: <a rel="noreferrer noopener" href="https://github.com/MachineLearningJournalClub/Didattica-MedicAI" target="_blank">https://github.com/MachineLearningJournalClub/Didattica-MedicAI</a><br><strong>Per iscriversi inviare una email a </strong>info (at) mljc (dot) it <strong>, le date verranno comunicate man mano agli iscritti.</strong></p>



<h4>​</h4>



<div class="wp-block-buttons aligncenter">
<div class="wp-block-button"><a class="wp-block-button__link" href="https://www.mljc.it/learn-with-mljc/">More info&#8230;</a></div>
</div>



<p><strong>Recordings of the first lecture are already available on our Youtube Channel</strong>. Code will be available soon on our <a href="https://github.com/MachineLearningJournalClub" target="_blank" rel="noreferrer noopener">GitHub page</a></p>



<figure class="wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Lezione 1 Brain-Computer Interface - a cura del Machine Learning Journal Club" width="800" height="450" src="https://www.youtube.com/embed/84q-bjevPAQ?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p>We are grateful to our <a rel="noreferrer noopener" href="https://www.mljc.it/partners/" target="_blank">partners</a> for providing computational resources. </p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/01/20/new-machine-learning-courses-from-the-mljc-in-a-y-2022-23/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>MLJC mentioned in TG Regione Piemonte</title>
		<link>https://www.mljc.it/2023/01/09/mljc-mentioned-in-tg-regione-piemonte/</link>
					<comments>https://www.mljc.it/2023/01/09/mljc-mentioned-in-tg-regione-piemonte/#respond</comments>
		
		<dc:creator><![CDATA[Letizia Pizzini]]></dc:creator>
		<pubDate>Mon, 09 Jan 2023 14:00:05 +0000</pubDate>
				<category><![CDATA[Press]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=2030</guid>

					<description><![CDATA[On 7th January 2023, RAI TG Regione Piemonte mentioned the Machine Learning Journal Club, its activities and the future Centre for Artificial Intelligence that is expected to be hosted in Turin. https://www.rainews.it/tgr/piemonte/video/2023/01/fermo-al-palo-il-centro-per-lintelligenza-artificiale-f0c4f3cf-320c-462e-a5d5-2d0663710e49.html https://www.rainews.it/tgr/piemonte/notiziari/video/2023/01/TGR-Piemonte-del-07012023-ore-1930-165576ba-cdc6-4766-b5c0-27a1d9267e11.html]]></description>
										<content:encoded><![CDATA[
<p>On 7th January 2023, RAI TG Regione Piemonte mentioned the Machine Learning Journal Club, its activities and the future Centre for Artificial Intelligence that is expected to be hosted in Turin.</p>



<figure class="wp-block-image size-large"><a href="https://www.rainews.it/tgr/piemonte/video/2023/01/fermo-al-palo-il-centro-per-lintelligenza-artificiale-f0c4f3cf-320c-462e-a5d5-2d0663710e49.html" target="_blank" rel="noopener noreferrer"><img loading="lazy" width="1024" height="549" src="https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-1024x549.png" alt="" class="wp-image-2031" srcset="https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-1024x549.png 1024w, https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-300x161.png 300w, https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-768x411.png 768w, https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-1536x823.png 1536w, https://www.mljc.it/wp-content/uploads/2023/01/Screenshot-2023-01-09-at-14.57.33-2048x1097.png 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /></a></figure>



<p><a href="https://www.rainews.it/tgr/piemonte/video/2023/01/fermo-al-palo-il-centro-per-lintelligenza-artificiale-f0c4f3cf-320c-462e-a5d5-2d0663710e49.html">https://www.rainews.it/tgr/piemonte/video/2023/01/fermo-al-palo-il-centro-per-lintelligenza-artificiale-f0c4f3cf-320c-462e-a5d5-2d0663710e49.html</a></p>



<p><a href="https://www.rainews.it/tgr/piemonte/notiziari/video/2023/01/TGR-Piemonte-del-07012023-ore-1930-165576ba-cdc6-4766-b5c0-27a1d9267e11.html">https://www.rainews.it/tgr/piemonte/notiziari/video/2023/01/TGR-Piemonte-del-07012023-ore-1930-165576ba-cdc6-4766-b5c0-27a1d9267e11.html</a></p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2023/01/09/mljc-mentioned-in-tg-regione-piemonte/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Report from NeurIPS 2022, New Orleans, LA (USA)</title>
		<link>https://www.mljc.it/2022/12/10/report-from-neurips-2022-new-orleans-usa/</link>
					<comments>https://www.mljc.it/2022/12/10/report-from-neurips-2022-new-orleans-usa/#respond</comments>
		
		<dc:creator><![CDATA[Valerio Pagliarino]]></dc:creator>
		<pubDate>Sat, 10 Dec 2022 13:49:50 +0000</pubDate>
				<category><![CDATA[Events, meetings and conferences]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=1926</guid>

					<description><![CDATA[Presentation at NeurIPS 2022, &#8220;Symmetries and Geometry in Neural Representations&#8221; Workshop (NeurReps) Workshop website &#124; NeurIPS website Last week, some of our members went to New Orleans for the thirty-sixth Conference on Neural Information Processing Systems, one of the biggest gatherings of machine learning researchers from all over the world. Having the chance to bring [&#8230;]]]></description>
										<content:encoded><![CDATA[
<h2>Presentation at NeurIPS 2022, &#8220;Symmetries and Geometry in Neural Representations&#8221; Workshop (NeurReps)</h2>



<p><a rel="noreferrer noopener" href="https://www.neurreps.org/" data-type="URL" data-id="https://www.neurreps.org/" target="_blank">Workshop website</a>  |  <a href="https://nips.cc/virtual/2022/workshop/49975" data-type="URL" data-id="https://nips.cc/virtual/2022/workshop/49975" target="_blank" rel="noreferrer noopener">NeurIPS website</a></p>



<p>Last week, some of our members went to New Orleans for the thirty-sixth Conference on Neural Information Processing Systems, one of the biggest gatherings of machine learning researchers from all over the world. Having the chance to bring part of our association to NeurIPS 2022 was a great honour for us.</p>



<p>We took part in multiple events. Simone Azeglio and Arianna Di Bernardo co-organised with Sophia Sanborn, Nina Miolane and Christian Shewmake the wonderful Symmetry and Geometry in Neural Representations (NeurReps) workshop gathering together researchers at the intersection of geometric deep learning, applied geometry, and neuroscience (Taco Cohen, Irina Higgins and many more) to study the geometric principles underlying neural representations.</p>



<figure class="wp-block-gallery columns-3 is-cropped"><ul class="blocks-gallery-grid"><li class="blocks-gallery-item"><figure><img loading="lazy" width="768" height="1024" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2-768x1024.jpeg" alt="" data-id="2018" data-full-url="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2.jpeg" data-link="https://www.mljc.it/?attachment_id=2018" class="wp-image-2018" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2-768x1024.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2-225x300.jpeg 225w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2-1152x1536.jpeg 1152w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-2.jpeg 1200w" sizes="(max-width: 768px) 100vw, 768px" /></figure></li><li class="blocks-gallery-item"><figure><img loading="lazy" width="1024" height="768" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1-1024x768.jpeg" alt="" data-id="2019" data-full-url="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1.jpeg" data-link="https://www.mljc.it/?attachment_id=2019" class="wp-image-2019" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1-1024x768.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1-300x225.jpeg 300w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1-768x576.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1-1536x1152.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1.jpeg 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></li><li class="blocks-gallery-item"><figure><img loading="lazy" width="768" height="1024" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-768x1024.jpeg" alt="" data-id="2020" data-full-url="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09.jpeg" data-link="https://www.mljc.it/?attachment_id=2020" class="wp-image-2020" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-768x1024.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-225x300.jpeg 225w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09-1152x1536.jpeg 1152w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-10-at-16.32.09.jpeg 1500w" sizes="(max-width: 768px) 100vw, 768px" /></figure></li></ul></figure>



<p>Secondly, our members Luca Bottero, Francesco Calisto and Valerio Pagliarino presented their work on learning geometrical features from images through group action enforcement in the poster session (https://openreview.net/forum?id=sEn61s0M1hy).<br>They proposed an autoencoder architecture capable of automatically learning meaningful geometric features of objects in images, achieving a disentangled representation of 2D objects. It is made of a standard dense autoencoder that captures the deep features identifying the shapes and an additional encoder that extracts geometric latent variables regressed in an unsupervised manner.</p>



<p>Simone Azeglio also presented his work as part of the SENSORIUM competition on predicting large scale mouse primary visual cortex activity. </p>



<p>Conferences like NeurIPS are a unique stage where to find inspiring insights in other people’s work and to create connections in both the academic and industrial domains. That is why we are grateful to TesiSquare, Fondazione DIG421 and NeurIPS for their great support and to HPC4AI, NPO Sistemi for the help. Last but not least, we thank our institutions LMU, TUM, École Normale Supérieure and UniTO.</p>



<figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="368" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15-1024x368.jpeg" alt="" class="wp-image-1998" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15-1024x368.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15-300x108.jpeg 300w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15-768x276.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15-1536x551.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-11-30-at-22.00.15.jpeg 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<figure class="wp-block-gallery columns-2 is-cropped"><ul class="blocks-gallery-grid"><li class="blocks-gallery-item"><figure><img loading="lazy" width="1024" height="768" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17-1024x768.jpeg" alt="" data-id="2000" data-full-url="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17.jpeg" data-link="https://www.mljc.it/?attachment_id=2000" class="wp-image-2000" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17-1024x768.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17-300x225.jpeg 300w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17-768x576.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17-1536x1152.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.17.jpeg 2000w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></li><li class="blocks-gallery-item"><figure><img loading="lazy" width="1024" height="768" src="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19-1024x768.jpeg" alt="" data-id="1999" data-full-url="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19.jpeg" data-link="https://www.mljc.it/?attachment_id=1999" class="wp-image-1999" srcset="https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19-1024x768.jpeg 1024w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19-300x225.jpeg 300w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19-768x576.jpeg 768w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19-1536x1152.jpeg 1536w, https://www.mljc.it/wp-content/uploads/2022/10/WhatsApp-Image-2022-12-03-at-00.51.19.jpeg 2000w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></li></ul></figure>



<p><strong>Abstract:</strong> </p>



<p><a href="https://openreview.net/forum?id=sEn61s0M1hy">https://openreview.net/forum?id=sEn61s0M1hy</a></p>



<figure class="wp-block-image size-large"><a href="https://openreview.net/forum?id=sEn61s0M1hy"><img loading="lazy" width="1024" height="546" src="https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-1024x546.png" alt="" class="wp-image-2007" srcset="https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-1024x546.png 1024w, https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-300x160.png 300w, https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-768x409.png 768w, https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-1536x819.png 1536w, https://www.mljc.it/wp-content/uploads/2022/10/Screenshot-2022-12-08-at-15.52.08-2048x1091.png 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /></a></figure>



<p><strong>Code:</strong></p>



<figure class="wp-block-embed is-type-rich"><div class="wp-block-embed__wrapper">
<div class="github-embed github-embed-repository github-logo-mark">    <p>        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public" target="_blank">			<strong>				Proposal for Neur-Reps 2022 &#8211; Automatic geometric features extraction from 2D images			</strong>		</a>		<br>        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public" target="_blank">https://github.com/MachineLearningJournalClub/NeurReps-2022-public</a><br>        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/network" target="_blank">0</a> forks.<br>        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/stargazers" target="_blank">1</a> stars.<br>        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/issues" target="_blank">0</a> open issues.<br>        <details open>            <summary>Recent commits:</summary>            <ul class="github_commits">                                    <li class="github_commit">                        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/commit/563f8c5f331f2cfd9361663095268712f90de0ea" target="_blank">Update README.md</a>, GitHub                    </li>                                    <li class="github_commit">                        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/commit/9722fbdc6b32b449728732fa2bc91e7502bf8804" target="_blank">Update README.md</a>, GitHub                    </li>                                    <li class="github_commit">                        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/commit/5793fe2ac34ed1753d5b56155c10926463c07b03" target="_blank">code</a>, Valerio Pagliarino                    </li>                                    <li class="github_commit">                        <a href="https://github.com/MachineLearningJournalClub/NeurReps-2022-public/commit/007a1d36f025dfe4459a2f7e3bda7f190a9e64e8" target="_blank">Initial commit</a>, GitHub                    </li>                            </ul>        </details>    </p></div>
</div></figure>



<p><strong>Poster:</strong></p>



<div class="wp-block-file"><a href="https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2.pdf">Poster in PDF format:</a><a href="https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2.pdf" class="wp-block-file__button" download>Download</a></div>



<figure class="wp-block-image size-large is-resized"><img loading="lazy" src="https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-683x1024.jpg" alt="" class="wp-image-2006" width="232" height="347" srcset="https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-683x1024.jpg 683w, https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-200x300.jpg 200w, https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-768x1152.jpg 768w, https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-1024x1536.jpg 1024w, https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-1366x2048.jpg 1366w, https://www.mljc.it/wp-content/uploads/2022/10/Poster_53_NeurReps-2-1-scaled.jpg 1707w" sizes="(max-width: 232px) 100vw, 232px" /></figure>



<p></p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2022/12/10/report-from-neurips-2022-new-orleans-usa/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>MLJC project in Geometric DL accepted at NeurIPS 2022, New Orleans (USA)</title>
		<link>https://www.mljc.it/2022/10/30/mljc-project-in-geometric-ml-accepted-at-neurips-2022-new-orleans-usa/</link>
					<comments>https://www.mljc.it/2022/10/30/mljc-project-in-geometric-ml-accepted-at-neurips-2022-new-orleans-usa/#respond</comments>
		
		<dc:creator><![CDATA[Valerio Pagliarino]]></dc:creator>
		<pubDate>Sun, 30 Oct 2022 13:49:12 +0000</pubDate>
				<category><![CDATA[Achievements and publications]]></category>
		<category><![CDATA[Events, meetings and conferences]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=1924</guid>

					<description><![CDATA[We are delighted to announce that a group from MLJC including Luca Bottero, Francesco Calisto and Valerio Pagliarino will present their work at the world-leading conference NeurIPS 2022 (Neural Information Processing Systems) in the Neur-Reps (https://www.neurreps.org/) (Symmetries and Geometry in Neural Representations) workshop. The conference will take place in New Orleans, Louisiana (USA) from November, [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>We are delighted to announce that a group from <strong>MLJC</strong> including <strong>Luca Bottero, Francesco Calisto </strong>and<strong> Valerio Pagliarino</strong> will present their work at the world-leading conference <strong>NeurIPS 2022</strong> (Neural Information Processing Systems) in the <strong>Neur-Reps</strong> (<a href="https://www.neurreps.org" target="_blank" rel="noreferrer noopener">https://www.neurreps.org</a>/) (Symmetries and Geometry in Neural Representations) workshop. The conference will take place in New Orleans, Louisiana (USA) from November, 27th to December 3rd.</p>



<p>The project is inserted in the landscape of Geometric Deep Learning (GDL), a relatively recent branch of Machine Learning aiming at studying how to enforce geometrical structures and priors into ML models, whit the final goal of increasing performances, generalization capabilities and explainability.</p>



<p>This specific work, titled <em><strong>Unsupervised Learning of Geometrical Features from Images by explicit Group Action Enforcement</strong></em>, has the ability of automatically disentangling the geometric rototranslational and scaling features from the intrinsic ones, when creating a latent representation of a dataset of input images. This line of research may lead in future to the development of more powerful and efficient architectures for machine vision, with applications ranging from self-driving vehicles to medical imaging, with better generalization capabilities.</p>



<p></p>



<figure class="wp-block-image size-large"><img loading="lazy" width="1024" height="830" src="https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-1024x830.png" alt="" class="wp-image-1934" srcset="https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-1024x830.png 1024w, https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-300x243.png 300w, https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-768x623.png 768w, https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-1536x1245.png 1536w, https://www.mljc.it/wp-content/uploads/2022/10/Neur-reps-2048x1660.png 2048w" sizes="(max-width: 1024px) 100vw, 1024px" /><figcaption>NeurReps Workshop &#8211; NeurIPS 2022</figcaption></figure>



<p><strong>Abstract:</strong></p>



<p>In this work we propose an autoencoder architecture capable of automatically learning meaningful geometric features of objects in images, achieving a disentangled representation of 2D objects. It is made of a standard dense autoencoder that captures the <em>deep features</em> identifying the shapes and an additional encoder that extracts geometric latent variables regressed in an unsupervised manner. These are then used to apply a transformation on the output of the <em>deep features</em> decoder. The promising results show that this approach performs better than a non-constrained model having more degrees of freedom.</p>



<p><strong>Keep following our website for updates and reporting from the conference!</strong></p>



<p>Special thanks for the support to: University of Turin, HPC4AI, NPO Torino, Pompei Student Lab</p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2022/10/30/mljc-project-in-geometric-ml-accepted-at-neurips-2022-new-orleans-usa/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Presentation of MLJC Projects for A.Y.  2022/23  10th November, Physics Dept. of the University of Turin</title>
		<link>https://www.mljc.it/2022/10/29/presentation-of-mljc-projects-for-a-y-2022-23-11th-november-physics-dept-of-the-university-of-turin/</link>
					<comments>https://www.mljc.it/2022/10/29/presentation-of-mljc-projects-for-a-y-2022-23-11th-november-physics-dept-of-the-university-of-turin/#respond</comments>
		
		<dc:creator><![CDATA[Simone Azeglio]]></dc:creator>
		<pubDate>Sat, 29 Oct 2022 13:45:18 +0000</pubDate>
				<category><![CDATA[Events, meetings and conferences]]></category>
		<guid isPermaLink="false">https://www.mljc.it/?p=1920</guid>

					<description><![CDATA[The Machine Learning Journal Club is happy to invite you to the event &#8220;MLJC &#8211; Presentation of projects and activities for the Academic Year 2022-23&#8220;. MLJC members will present the key projects and activites currently under development and they will interact with students, researchers and AI enthusiasts for extending our network and opportunities to everyone [&#8230;]]]></description>
										<content:encoded><![CDATA[
<p>The Machine Learning Journal Club is happy to invite you to the event &#8220;<strong>MLJC &#8211; Presentation of projects and activities for the Academic Year 2022-23</strong>&#8220;. MLJC members will present the key projects and activites currently under development and they will interact with students, researchers and AI enthusiasts for extending our network and opportunities to everyone that is interested in collaborating with us. </p>



<p>A large number of highly interactive short talks will allow people to get insight on the technical activities of MLJC in the fields of theoretical and applied Machine Learning, Deep Learning and Artificial Intelligence.</p>



<p>Talks will range from Brain Computer Interfaces to ML applications in Physics to Natural Language Processing. </p>



<p><strong>Venue: Aula Magna, Physics Department, University of Turin,</strong> (Via Giuria 1, Torino)<br>November 10th, from 4 p.m to 7 p.m.</p>



<div class="wp-block-file"><a href="https://www.mljc.it/wp-content/uploads/2022/10/ProgrammaA5.pdf"><strong>Save the date</strong></a><a href="https://www.mljc.it/wp-content/uploads/2022/10/ProgrammaA5.pdf" class="wp-block-file__button" download>Open PDF</a></div>



<figure class="wp-block-image size-large"><img loading="lazy" width="724" height="1024" src="https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-724x1024.png" alt="" class="wp-image-1955" srcset="https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-724x1024.png 724w, https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-212x300.png 212w, https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-768x1086.png 768w, https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-1086x1536.png 1086w, https://www.mljc.it/wp-content/uploads/2022/10/programma_sito-1448x2048.png 1448w" sizes="(max-width: 724px) 100vw, 724px" /></figure>



<h2>Recordings</h2>



<div class="wp-block-columns">
<div class="wp-block-column">
<h4>Introduction and MedicAI</h4>



<figure class="wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Introduction and MedicAI - CONFERENZA 11/11/2022" width="800" height="450" src="https://www.youtube.com/embed/67B-AO4yuss?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<h4>Symmetries</h4>



<figure class="wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="Symmetries - CONFERENZA 11/11/2022" width="800" height="450" src="https://www.youtube.com/embed/Ixv3yzXZkj0?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>
</div>



<div class="wp-block-column">
<h4>Scientific ML</h4>



<figure class="wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="ScientificML - CONFERENZA 11/11/2022" width="800" height="450" src="https://www.youtube.com/embed/De2eoU3wYYU?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<h4>ML in HEP and more</h4>



<figure class="wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe title="ML in HEP and More - CONFERENZA 11/11/2022" width="800" height="450" src="https://www.youtube.com/embed/EdSde8p-YVM?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>
</div>
</div>



<p></p>
]]></content:encoded>
					
					<wfw:commentRss>https://www.mljc.it/2022/10/29/presentation-of-mljc-projects-for-a-y-2022-23-11th-november-physics-dept-of-the-university-of-turin/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
	</channel>
</rss>