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.
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- 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.
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