Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Sato, Issei"'
Autor:
Yokoi, Soma, Sato, Issei
Sum-product networks (SPNs) are probabilistic models characterized by exact and fast evaluation of fundamental probabilistic operations. Its superior computational tractability has led to applications in many fields, such as machine learning with tim
Externí odkaz:
http://arxiv.org/abs/2406.12353
Autor:
Tomihari, Akiyoshi, Sato, Issei
The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of-distribution (OOD) data. This success is largely a
Externí odkaz:
http://arxiv.org/abs/2405.16747
Autor:
Sakamoto, Keitaro, Sato, Issei
End-to-end (E2E) training, optimizing the entire model through error backpropagation, fundamentally supports the advancements of deep learning. Despite its high performance, E2E training faces the problems of memory consumption, parallel computing, a
Externí odkaz:
http://arxiv.org/abs/2402.09050
Autor:
Wang, Shuo, Sato, Issei
Deep neural networks are being increasingly implemented throughout society in recent years. It is useful to identify which parameters trigger misclassification in diagnosing undesirable model behaviors. The concept of parameter saliency is proposed a
Externí odkaz:
http://arxiv.org/abs/2310.17951
This paper investigates the initialization bias of the Fourier neural operator (FNO). A mean-field theory for FNO is established, analyzing the behavior of the random FNO from an \emph{edge of chaos} perspective. We uncover that the forward and backw
Externí odkaz:
http://arxiv.org/abs/2310.06379
Autor:
Kajitsuka, Tokio, Sato, Issei
Existing analyses of the expressive capacity of Transformer models have required excessively deep layers for data memorization, leading to a discrepancy with the Transformers actually used in practice. This is primarily due to the interpretation of t
Externí odkaz:
http://arxiv.org/abs/2307.14023
Autor:
Hasegawa, Naoya, Sato, Issei
Recognition problems in long-tailed data, in which the sample size per class is heavily skewed, have gained importance because the distribution of the sample size per class in a dataset is generally exponential unless the sample size is intentionally
Externí odkaz:
http://arxiv.org/abs/2305.16573
Bayesian deep learning plays an important role especially for its ability evaluating epistemic uncertainty (EU). Due to computational complexity issues, approximation methods such as variational inference (VI) have been used in practice to obtain pos
Externí odkaz:
http://arxiv.org/abs/2206.01606
Autor:
Sakamoto, Keitaro, Sato, Issei
The lottery ticket hypothesis (LTH) has attracted attention because it can explain why over-parameterized models often show high generalization ability. It is known that when we use iterative magnitude pruning (IMP), which is an algorithm to find spa
Externí odkaz:
http://arxiv.org/abs/2205.07320
A computer-aided detection (CAD) system based on machine learning is expected to assist radiologists in making a diagnosis. It is desirable to build CAD systems for the various types of diseases accumulating daily in a hospital. An obstacle in develo
Externí odkaz:
http://arxiv.org/abs/2204.13849