From df1a52f02afc9eb52a268c3c55cd64d9dd688e10 Mon Sep 17 00:00:00 2001 From: Massimo Pavan Date: Fri, 8 Dec 2023 14:52:49 +0100 Subject: [PATCH] Update index.html --- index.html | 43 ++++++++++++++++--------------------------- 1 file changed, 16 insertions(+), 27 deletions(-) diff --git a/index.html b/index.html index bb024b0..0b779bb 100644 --- a/index.html +++ b/index.html @@ -33,8 +33,8 @@
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Knowledge-Guided Machine learning

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1st European Knowledge-Guided Machine learning workshop - September 22 2023, Turin, Italy

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Tiny Machine Learning

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Special Session on Tiny Machine Learning - June 30 2024, Yokohama, Japan

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Kno

About this Workshop

- We are glad to announce the 1st european Knowledge-Guided Machine learning workshop, to be held in Turin, Italy in ECML-PKDD 2023. + We are glad to announce the Special Session on Tiny Machine Learning, to be held in Yokohama, Japan in WCCI 2024.
- All the information about the conference can be found at the official website. + All the information about the conference can be found at the official website.

- Even though machine learning (ML) and deep learning (DL) algorithms have achieved amazing results in - many commercial and business applications, data-driven models have so far met with limited success in - many scientific domains. Limitations of data-driven models arise due to their intrinsic black-box nature, - their large data requirements, their inability to produce physically consistent results, and the lack of - generalizability to out-of-sample scenarios. More generally, the popularity and success of ML-based systems - has put the spotlight on issues such as explainability, bias, fairness, and sustainability. There is thus a - growing interest in developing Knowledge Guided Machine Learning (KGML) approaches that can leverage - decades (sometimes centuries) of accumulated scientific knowledge and combine them with ML techniques - to reap the benefits of both approaches. Addressing these issues naturally leads to systems in which - representation of prior and domain knowledge in various forms, from physical and simulation models to - symbolic and logical representations, plays a central role and must be integrated into ML models and - pipelines. On the other hand, ML also offers solutions for long-standing challenges in the field of Knowledge - Representation (KR), for instance related to efficient, neurally-guided, noise-tolerant and ampliative - inference, knowledge acquisition, efficient reasoning, and limitations of existing physical and symbolic - models. The synergy between ML and KR has the potential to lead to new advancements in fundamental AI - challenges including, but not limited to, learning symbolic generalizations from raw (multi-modal) data, - using knowledge to facilitate data-efficient learning, speeding up inference, supporting interpretability of - learned outcomes and integration of symbolic planning and reinforcement learning. + The computing everywhere paradigm is paving the way for the pervasive diffusion of tiny devices + (such as Internet-of-things or edge computing devices) endowed with intelligent abilities. + Achieving this goal requires machine and deep learning solutions to be completely redesigned to + fit the severe technological constraints on computation, memory, and power consumption typically + characterizing these tiny devices. This is exactly where Tiny Machine Learning (TinyML) comes + into play. TinyML is a new and promising area of Machine Learning aimed at designing and developing + Machine and Deep Learning solutions that can be executed on tiny devices. +

- The workshop aims to provide researchers and practitioners with a dedicated forum for discussing new - ideas and research results at the intersection of machine learning and knowledge representation, both - from a theoretical and application standpoint. + This special session aims at exploring the latest advancements, models, algorithms, methodologies, and applications in TinyML. +

- - ECML-PKDD Logo + + WCCI Logo