Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
DPO Unchained: Your Training Algorithm is Secretly Disentangled in Human Choice Theory (and its Loss' Convexity is Dispensable)
Wenxuan Zhou, Shujian Zhang, Brice Magdalou +4
Normative theories allow one to elicit key parts of a ML algorithm from first principles, which is crucial at a time of championed scrutiny for ML work. Direct Preference Optimizat…
cs.LG2025
Benchmarking Neural Network Training Algorithms
George E. Dahl, Frank Schneider, Zachary Nado +22
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workl…
cs.LG2025
Restructuring Vector Quantization with the Rotation Trick
Christopher Fifty, Ronald G. Junkins, Dennis Duan +5
Vector Quantized Variational AutoEncoders (VQ-VAEs) are designed to compress a continuous input to a discrete latent space and reconstruct it with minimal distortion. They operate…