Michael Spranger

CVPR, 2025 | Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu

As scaling laws in generative AI push performance, they simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to unlock this bottleneck by demonstrating very l...

CVPR, 2025 | Weiming Zhuang, Chen Chen, Zhizhong Li, Sina Sajadmanesh, Jingtao Li, Jiabo Huang, Vikash Sehwag, Vivek Sharma, Hirotaka Shinozaki, Felan Carlo Garcia, Yihao Zhan, Naohiro Adachi, Ryoji Eki, Michael Spranger, Peter Stone, Lingjuan Lyu

While existing vision and multi-modal foundation models can handle multiple computer vision tasks, they often suffer from significant limitations, including huge demand for data and computational resources during training and inconsistent performance across vision tasks at d...

SCIENCE, 2023 | Peter R. Wurman, Peter Stone, Michael Spranger

Games continue to drive progress in the development of artificial intelligence.

ICLR, 2023 | Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti*, Michael Spranger

Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe...

ICLR, 2023 | Junyuan Hong, Lingjuan Lyu, Jiayu Zhou*, Michael Spranger

Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient descents but meanwhile cost ...

ICDE, 2023 | Uchenna Akujuobi, Michael Spranger, Sucheendra Palaniappan*, Xiangliang Zhang*

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publi...

NEURIPS, 2022 | Junyuan Hong, Lingjuan Lyu, Jiayu Zhou*, Michael Spranger

As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training at a low-power and cost-effective end device. Traditional outsourcing requires uploading device...

ICCC, 2022 | Kana Maruyama, Michael Spranger

Supporting chefs with ingredient recommender systems to create new recipes is challenging, as good ingredient combinations depend on many factors like taste, smell, cuisine style, texture, chef’s preference and many more. Useful machine learning models do need to be accurate...

NATURE, 2022 | Pete Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandelwal, Varun Kompella, Hao Chih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Rory Douglas, Dion Whitehead Amago, Peter Dürr, Peter Stone, Michael Spranger, Hiroaki Kitano

Many potential applications of artificial intelligence involve making real-time decisions in physical systems while interacting with humans. Automobile racing represents an extreme example of these conditions; drivers must execute complex tactical manoeuvres to pass or block...

ARTIFICIAL INTELLIGENCE, 2022 | Samy Badreddine, Artur d'Avila Garcez*, Luciano Serafini*, Michael Spranger

Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists deep learning, which typically uses a sub-symbolic distributed representation, to learn and reason a...

NEURIPS, 2021 | Ryuji Imamura, Takuma Seno, Kenta Kawamoto, Michael Spranger

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environ...

IEEE ACCESS, 2021 | Michael Spranger, Kana Maruyama

Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests pos...

IJCLR, 2021 | Samy Badreddine, Michael Spranger

Real Logic is a recently introduced first-order language where formulas have fuzzy truth values in the interval [0, 1] and semantics are defined concretely with real domains. The Logic Tensor Networks (LTN) framework has applied Real Logic to many important AI tasks through ...

NEURIPS, 2020 | Michael Spranger, Oscar Chang*, Lampros Flokas*, Hod Lipson*

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achi...

NEURIPS, 2020 | Uchenna Akujuobi, Jun Chen*, Mohamed Elhoseiny*, Michael Spranger, Xiangliang Zhang*

Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers ...

IEEE TKDE, 2020 | Uchenna Akujuobi, Michael Spranger, Sucheendra K Palaniappan*, Xiangliang Zhang*

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningfulimplicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted fromdatabases of biomedical publica...