9 citations · 11 across the 5 of their papers we have counts for
12 papers
Relational Knowledge Distillation Brings DNN Representations Close Enough to Humans to Be Aligned Without Supervision
Yuria Shimizu, Soh Takahashi, Takato Horii +1
Linking the internal representations of deep neural networks (DNNs) to human mental representations is important for using DNNs as computational models of human vision. Existing DN…
Continual Learning in Modern Hopfield Networks with an Application to Diffusion Models
Ken Takeda, Masafumi Oizumi, Ryo Karakida
Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual learning an essential problem…
Koopman Mode Decomposition of Thermodynamic Dissipation in Nonlinear Langevin Dynamics
Daiki Sekizawa, Sosuke Ito, Masafumi Oizumi
Nonlinear oscillations are commonly observed in complex systems far from equilibrium, such as living organisms. These oscillations are essential for sustaining vital processes, lik…
Correspondence of high-dimensional emotion structures elicited by video clips between humans and Multimodal LLMs
Haruka Asanuma, Naoko Koide-Majima, Ken Nakamura +3
Recent studies have revealed that human emotions exhibit a high-dimensional, complex structure. A full capturing of this complexity requires new approaches, as conventional models…
Investigating Fine- and Coarse-grained Structural Correspondences Between Deep Neural Networks and Human Object Image Similarity Judgments Using Unsupervised Alignment
Soh Takahashi, Masaru Sasaki, Ken Takeda +1
The learning mechanisms by which humans acquire internal representations of objects are not fully understood. Deep neural networks (DNNs) have emerged as a useful tool for investig…
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects
Asaki Kataoka, Yoshihiro Nagano, Masafumi Oizumi
Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do n…