#self-supervised learning

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35 papers match

cs.CL2026

Training Skills Like Parameters via Self-Supervised Semantic Diffusion

Mo Li, Zixin Yin, Ting Cao +1

The paper introduces a self‑supervised framework that lets a language model acquire and store textual skills in an external library using diffusion‑style reconstruction loss, witho…

#self-supervised learning#diffusion models#skill extraction#language model adaptation
cs.CV2026

PhiZero: A World Model Built Around Physical Language

Shuyao Shang, Yuqi Wang, Ruopeng Gao +4

PhiZero is a physical world model that learns a compact discrete "physical language" from videos to predict future world states as language sequences before rendering them into rea…

#physical world modeling#language-based representation#video prediction#self-supervised learning
cs.SD2026

Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances

Dmitrii Gavrilev, Ilya Borovik, Vladimir Viro

The paper proposes using contextual embeddings from self‑supervised symbolic music models to evaluate expressive MIDI piano performances, showing that these embeddings align with h…

#expressive performance evaluation#symbolic music#contextual embeddings#self-supervised learning
cs.CV2026

TARS: Timestep-Aware Data Scaling for 3D-Free Video Re-Shooting

Jiwen Liu, Shujuan Li, Xiaohan Li +5

The paper introduces TARS, a 3D‑free video re‑shooting framework that uses text‑driven semantic viewpoint specifications and self‑supervised training to control camera motion and p…

#video re-shooting#camera control#text-driven viewpoint#self-supervised learning
cs.CV2026

Beyond Classification: Pathology Foundation Models as Detection Encoders for Mitotic Figures

Sweta Banerjee, Alireza Teimoury, Nils Porsche +11

The paper evaluates whether pathology foundation models can serve as effective backbones for dense detection of mitotic figures, comparing several self‑supervised models to a ResNe…

#pathology foundation models#mitotic figure detection#dense object detection#self-supervised learning
cs.LG2026

Building a User Foundation Model for the Open Web

Solal Vernier, Ivan Can Arisoy, Merwan Barlier +1

The paper introduces a self‑supervised transformer model trained on fragmented web browsing histories to create user representations that improve click prediction and bidding perfo…

#user modeling#self-supervised learning#transformer encoder#real-time bidding