Flow models transform data gradually from one modality (e.g. noise) onto another (e.g. images). Such models are parameterized by a time-dependent velocity field, trained to fit segments connecting pai...

Flow models parameterized as time-dependent velocity fields can generate data from noise by integrating an ODE. These models are often trained using flow matching, i.e. by sampling random pairs of noi...

A recent paper from Apple researchers,

Vision Language Models (VLMs) enable visual understanding alongside textual inputs. They are typically built by passing visual tokens from a…

The NeurIPS checklist corroborates the bureaucratic theory of statistics.

Shun-ichi Amari

甘利 俊一栄誉研究員（本務：帝京大学 先端総合研究機構 特任教授）は、人工ニューラルネットワーク、機械学習、情報幾何学分野での先駆的な研究が評価され、第40回（2025）京都賞（先端技術部門　受賞対象分野：情報科学）を受賞しました。

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Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the world’s understanding of this…

YouTube video by Senator Elizabeth Warren

Attention is a key part of the transformer architecture. It is a sequence-to-sequence mapping that transforms each sequence element into a weighted sum of values. The weights are typically obtained as...