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Eighth International Workshop on Symbolic-Neural Learning (SNL2024)
June 26-27, 2024
Miraikan hall, Odaiba Miraikan 7F (Tokyo, Japan)
SNL2024 will be held at Miraikan hall, Tokyo, Japan.
Symbolic-neural learning involves deep learning methods in combination with symbolic structures. A "deep learning method" is taken to be a learning process based on gradient descent on real-valued model parameters. A "symbolic structure" is a data structure involving symbols drawn from a large vocabulary; for example, sentences of natural language, parse trees over such sentences, databases (with entities viewed as symbols), and the symbolic expressions of mathematical logic or computer programs. Natural applications of symbolic-neural learning include, but are not limited to, the following areas:
- Image caption generation and visual question answering
- Speech and natural language interactions in robotics
- Machine translation
- General knowledge question answering
- Reading comprehension
- Textual entailment
- Dialogue systems
Various architectural ideas are shared by deep learning systems across these areas. These include word and phrase embeddings, recurrent neural networks (LSTMs and GRUs) and various attention and memory mechanisms. Certain linguistic and semantic resources may also be relevant across these applications. For example dictionaries, thesauri, WordNet, FrameNet, FreeBase, DBPedia, parsers, named entity recognizers, coreference systems, knowledge graphs and encyclopedias. Deep learning approaches to the above application areas, with architectures and tools subjected to quantitative evaluation, loosely define the focus of the workshop.
The workshop consists of invited oral presentations and contributed poster presentations.
Organizing Committee:
| Kyoungsook Kim |
AIST AI Research Center, Tokyo, Japan |
| Tomoko Matsui |
The Institute of Statistical Mathematics, Tokyo, Japan |
| Yutaka Sasaki |
Toyota Technological Institute, Nagoya, Japan |
| Satoshi Sekine |
RIKEN Center for AIP, Tokyo, Japan |
| Koichi Shinoda |
Tokyo Institute of Technology, Tokyo, Japan |
| Jun'ichi Tsujii (Chair) |
AIST AI Research Center, Tokyo, Japan and the University of Manchester, Manchester, UK |
| Yasushi Yagi |
Osaka University, Osaka, Japan |
| Natsuki Yamanobe |
AIST Industrial Cyber-Physical Systems Research Center, Tokyo, Japan |
Program Committee:
| Satoru Fukayama |
AIST AI Research Center, Tokyo, Japan |
| Satoshi Ikehata |
National Institute of Informatics, Tokyo, Japan and Tokyo Institute of Technology, Tokyo, Japan |
| Michihiro Kawanishi |
Toyota Technological Institute, Nagoya, Japan |
| Shuhei Kurita |
RIKEN Center for AIP, Tokyo, Japan |
| Yuta Nakashima |
Osaka University, Osaka, Japan |
| Makoto Miwa (Co-chair) |
Toyota Technological Institute, Nagoya, Japan |
| Akifumi Okuno |
The Institute of Statistical Mathematics, Tokyo, Japan |
| Nathan Srebro |
Toyota Technological Institute, Chicago, USA |
| Natsuki Yamanobe (Co-chair) |
AIST Industrial Cyber-Physical Systems Research Center, Tokyo, Japan |
| Koichiro Yoshino |
RIKEN Guardian Robot Project, Kyoto, Japan |
Local Arrangements Committee:
| Satoru Fukayama (Chair) |
AIST AI Research Center, Tokyo, Japan |
| Michino Funatsu |
AIST AI Research Center, Tokyo, Japan |
| Kimiko Niwa |
AIST AI Research Center, Tokyo, Japan |
| Chihiro Sato |
AIST AI Research Center, Tokyo, Japan |
Previous Workshops:
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