2 citations · 3 across the 11 of their papers we have counts for
4 papers · 1 filter
SQuAT: Spherical Lattice Vector Quantization-Aware Training
Sebastian Loeschcke, Ivan Koryakovskiy, Tycho F. A. van der Ouderaa +4
Vector quantization (VQ), in particular lattice-based methods, offer strong rate-distortion trade-offs for low-bit large language models (LLMs), yet their behavior under quantizati…
MobileWan: Closing the Quality Gap for Mobile Video Diffusion
Mohsen Ghafoorian, Denis Korzhenkov, Adil Karjauv +9
Recent advances in video diffusion have been driven by scaling transformer-based architectures to billions of parameters, substantially improving visual fidelity and motion coheren…
Dissecting Quantization Error: A Concentration-Alignment Perspective
Marco Federici, Boris van Breugel, Paul Whatmough +1
Quantization can drastically increase the efficiency of large language and vision models, but typically incurs an accuracy drop. Recently, function-preserving transforms (e.g. rota…
Leech Lattice Vector Quantization for Efficient LLM Compression
Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough +1
Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcomes these limits by encoding bloc…