Zobrazeno 1 - 10
of 70 755
pro vyhledávání: '"A, Yokota"'
Autor:
Cong, Bai, Daheim, Nico, Shen, Yuesong, Cremers, Daniel, Yokota, Rio, Khan, Mohammad Emtiyaz, Möllenhoff, Thomas
We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW by the Improved Variational Online Newton (IVON) algorithm to finetune
Externí odkaz:
http://arxiv.org/abs/2411.04421
Autor:
Nakano, Hiroyoshi, Yokota, Kazuma
In fluids under temperature gradients, long-range correlations (LRCs) emerge generically, leading to enhanced density fluctuations. This phenomenon, characterized by the $\boldsymbol{q}^{-4}$ divergence in the static structure factor (where $\boldsym
Externí odkaz:
http://arxiv.org/abs/2411.04416
Autor:
Tobaben, Marlon, Souibgui, Mohamed Ali, Tito, Rubèn, Nguyen, Khanh, Kerkouche, Raouf, Jung, Kangsoo, Jälkö, Joonas, Kang, Lei, Barsky, Andrey, d'Andecy, Vincent Poulain, Joseph, Aurélie, Muhamed, Aashiq, Kuo, Kevin, Smith, Virginia, Yamasaki, Yusuke, Fukami, Takumi, Niwa, Kenta, Tyou, Iifan, Ishii, Hiro, Yokota, Rio, N, Ragul, Kutum, Rintu, Llados, Josep, Valveny, Ernest, Honkela, Antti, Fritz, Mario, Karatzas, Dimosthenis
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The compet
Externí odkaz:
http://arxiv.org/abs/2411.03730
Local learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation. However, its algorithms often become more complex or require additional hyperparamet
Externí odkaz:
http://arxiv.org/abs/2411.02001
Autor:
Miyagawa, Taiki, Yokota, Takeru
We propose the first learning scheme for functional differential equations (FDEs). FDEs play a fundamental role in physics, mathematics, and optimal control. However, the numerical analysis of FDEs has faced challenges due to its unrealistic computat
Externí odkaz:
http://arxiv.org/abs/2410.18153
Autor:
Isomoto, Kosei, Mizutani, Akinobu, Matsuzaki, Fumiya, Sato, Hikaru, Matsumoto, Ikuya, Yamao, Kosei, Kawabata, Takuya, Shiba, Tomoya, Yano, Yuga, Yokota, Atsuki, Kanaoka, Daiju, Yamaguchi, Hiromasa, Murai, Kazuya, Minje, Kim, Shen, Lu, Suzuka, Mayo, Anraku, Moeno, Yamaguchi, Naoki, Fujimatsu, Satsuki, Tokuno, Shoshi, Mizo, Tadataka, Fujino, Tomoaki, Nakadera, Yuuki, Shishido, Yuka, Nakaoka, Yusuke, Tanaka, Yuichiro, Morie, Takashi, Tamukoh, Hakaru
This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team has developed a dataset generator for training a robot vision system and an open-sour
Externí odkaz:
http://arxiv.org/abs/2410.06192
Autor:
Matsui, Yusuke, Yokota, Tatsuya
We propose a new operator defined between two tensors, the broadcast product. The broadcast product calculates the Hadamard product after duplicating elements to align the shapes of the two tensors. Complex tensor operations in libraries like \texttt
Externí odkaz:
http://arxiv.org/abs/2409.17502
Autor:
Yamada, Ryosuke, Hara, Kensho, Kataoka, Hirokatsu, Makihara, Koshi, Inoue, Nakamasa, Yokota, Rio, Satoh, Yutaka
Throughout the history of computer vision, while research has explored the integration of images (visual) and point clouds (geometric), many advancements in image and 3D object recognition have tended to process these modalities separately. We aim to
Externí odkaz:
http://arxiv.org/abs/2409.13535
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles of odd number of negative edges. Laplacian of a balanced signed graph
Externí odkaz:
http://arxiv.org/abs/2409.07794
Autor:
Ohtani, Go, Tadokoro, Ryu, Yamada, Ryosuke, Asano, Yuki M., Laina, Iro, Rupprecht, Christian, Inoue, Nakamasa, Yokota, Rio, Kataoka, Hirokatsu, Aoki, Yoshimitsu
In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on common training datasets such as DIV2K and DF2K. However, we investigate
Externí odkaz:
http://arxiv.org/abs/2409.00768