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Randall Balestriero

23 papers hereh-index 371 citations18 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author10
  • last author10

Across the 20 of 23 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.CV4
  • cs.RO3
  • cs.AI2
  • math.AG1
  • stat.ML1
same name
  • Randall Balestriero — 44 papers, h 22
  • Randall Balestriero — 42 papers, h 16
  • Randall Balestriero — 24 papers, h 6
  • Randall Balestriero — 12 papers, h 3
  • Randall Balestriero — 3 papers, h 1
  • Randall Balestriero — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedCausal-JEPA: Learning World Models through Object-Level Latent Masking

5 citations · 6 across the 18 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

MotionJEPA: Preventing Temporal Feature Collapse by Capturing Visual Changes in Latent Space

Markus Karmann, Shile Li, Christian Internò +10

Joint Embedding Predictive Architectures (JEPAs) are a promising paradigm for learning task-agnostic latent world models without visual reconstruction. However, standard JEPA train…

cs.CV2026

LeVJEPA: Efficient & Scalable Video Pretraining without the Heuristics

Lukas Kuhn, Lucas Maes, Giuseppe Serra +4

Video carries the temporal structure of the physical world, yet learning representations from it has remained computationally expensive: prevailing self-supervised methods either p…

cs.CV2026

OctoSense: Self-Supervised Learning for Multimodal Robot Perception

Anthony Bisulco, Jeremy Wang, Kostas Daniilidis +2

We present OctoSense, an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corrected global positioning syst…

cs.CV2026

Dyna-DINO: Efficient ViT Distillation Via Adaptive Representation Anchoring

Jiaqi Zhang, Ashton Lee, Anthony Wong +3

Vision Foundation Models (VFMs) with Vision Transformer (ViT) backbones, such as DINOv2, have become essential for downstream tasks like object recognition and semantic segmentatio…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.