1 citations · 1 across the 4 of their papers we have counts for
5 papers
TORQUE: Optimizing What (not) to Quantize Before and After Rotation
Ran Ben Basat, Michael Mitzenmacher, Shay Vargaftik
Uniform random rotations are an effective preprocessing step for quantization: they make normalized coordinate distributions approximately Gaussian, enabling the use of codebooks o…
Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven
Ran Ben-Basat, William Kuszmaul, Michael Mitzenmacher +2
Uniform random rotations (URRs) are a common preprocessing step in modern quantization approaches used for gradient compression, inference acceleration, KV-cache compression, model…
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3
This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN exte…
Optimal and Near-Optimal Adaptive Vector Quantization
Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher +1
Quantization is a fundamental optimization for many machine-learning use cases, including compressing gradients, model weights and activations, and datasets. The most accurate form…
How to send a real number using a single bit (and some shared randomness)
Ran Ben-Basat, Michael Mitzenmacher, Shay Vargaftik
We consider the fundamental problem of communicating an estimate of a real number using a single bit. A sender that knows chooses a value to transmi…