3 papers
cs.LG2026
Do Sparse Autoencoders Capture Concept Manifolds?
Usha Bhalla, Thomas Fel, Can Rager +9
Sparse autoencoders (SAEs) are widely used to extract interpretable features from neural network representations, often under the implicit assumption that concepts correspond to in…
cs.CL2026
Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference
Vasu Shyam, Anna Golubeva, Quentin Anthony
We present tensor and sequence parallelism (TSP), a parallel execution strategy that folds tensor parallelism and sequence parallelism onto a single device axis. In conventional mu…
cs.CL2025
Training Foundation Models on a Full-Stack AMD Platform: Compute, Networking, and System Design
Quentin Anthony, Yury Tokpanov, Skyler Szot +18
We report on the first large-scale mixture-of-experts (MoE) pretraining study on pure AMD hardware, utilizing both MI300X GPUs and Pollara networking. We distill practical guidance…