Research
Papers & preprints
Original papers and technical reports from our work on GIDE — alongside the foundational third-party research it builds on.
What these topics mean
- Distributed Training
- Sharding computation and memory across many devices to scale beyond a single accelerator.
- Efficient Attention
- Methods that cut attention's compute and memory cost without changing its output.
- GPU Kernels & Systems
- Hardware-aware implementations that map attention efficiently onto GPU memory and compute.
- Long-Context
- Extending models to very long sequences — hundreds of thousands to billions of tokens.
- Low-Precision & Quantization
- Trading numerical precision for speed and memory while bounding the accuracy cost.
- Safety & Control
- Methods that keep a learning system provably within specified safety limits while it acts.
- Self-Supervised Learning
- Learning useful representations from unlabeled data by predicting parts of the input from other parts.
- State-Space Models
- Linear-time sequence models that replace attention with a recurrent state, scaling to long sequences without the quadratic cost.
- Transformer Architecture
- The attention-based neural network architecture that underlies modern sequence models.
- World Models
- Models that learn how a system evolves in a latent representation space, for prediction and planning rather than generation.
Topic
Self-Supervised Learning
Learning useful representations from unlabeled data by predicting parts of the input from other parts.
Showing 3 of 18 papers.
Foundational reading
Notable third-party research we build on.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
Preprint Third-party DarcStar commentary arXiv·June 11, 2025By Mido Assran, Adrien Bardes, David Fan, Quentin Garrido, Russell Howes, Mojtaba Komeili, Matthew Muckley, Ammar Rizvi, Claire Roberts, Koustuv Sinha, Artem Zholus, Sergio Arnaud, Abha Gejji, Ada Martin, Francois Robert Hogan, Daniel Dugas, Piotr Bojanowski, Vasil Khalidov, Patrick Labatut, Francisco Massa, Marc Szafraniec, Kapil Krishnakumar, Yong Li, Xiaodong Ma, Sarath Chandar, Franziska Meier, Yann LeCun, Michael Rabbat, Nicolas Ballas
This is third-party work — not authored by or affiliated with DarcStar Technologies.
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data with a small amount of interaction data (robot trajectories), to develop models capable of understanding, predicting, and planning in…
Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Published Third-party DarcStar commentary ICCV·January 19, 2023By Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas
This is third-party work — not authored by or affiliated with DarcStar Technologies.
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks…
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Published Third-party DarcStar commentary ICLR·April 25, 2022By Adrien Bardes, Jean Ponce, Yann LeCun
This is third-party work — not authored by or affiliated with DarcStar Technologies.
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learning architecture, that often lack…