6 papers
Towards Viewpoint-Robust End-to-End Autonomous Driving with 3D Foundation Model Priors
Hiroki Hashimoto, Hiromichi Goto, Hiroyuki Sugai +2
Robust trajectory planning under camera viewpoint changes is important for scalable end-to-end autonomous driving. However, existing models often depend heavily on the camera viewp…
Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Adversarial training is one of the most effective defenses against adversarial attacks, but it incurs a high computational cost. In this study, we present the first theoretical ana…
Training on Plausible Counterfactuals Removes Spurious Correlations
Shpresim Sadiku, Kartikeya Chitranshi, Hiroshi Kera +1
Plausible counterfactual explanations (p-CFEs) are perturbations that minimally modify inputs to change classifier decisions while remaining plausible under the data distribution.…
Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms
Hiroshi Kera, Nico Pelleriti, Yuki Ishihara +2
Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields. Traditional methods like Gröbner and…
Adversarial Training from Mean Field Perspective
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Although adversarial training is known to be effective against adversarial examples, training dynamics are not well understood. In this study, we present the first theoretical anal…
Learning to Compute Gröbner Bases
Hiroshi Kera, Yuki Ishihara, Yuta Kambe +2
Solving a polynomial system, or computing an associated Gröbner basis, has been a fundamental task in computational algebra. However, it is also known for its notorious doubly exp…