92 citations · 152 across the 10 of their papers we have counts for
5 papers · 1 filter
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
Nathan Ng, Roger Grosse, Marzyeh Ghassemi
Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts. A minimum description length…
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani +1
Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an…
Efficient Parametric Approximations of Neural Network Function Space Distance
Nikita Dhawan, Sicong Huang, Juhan Bae +1
It is often useful to compactly summarize important properties of model parameters and training data so that they can be used later without storing and/or iterating over the entire…
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Yuri Burda, Roger B. Grosse, Ruslan Salakhutdinov
Markov random fields (MRFs) are difficult to evaluate as generative models because computing the test log-probabilities requires the intractable partition function. Annealed import…
Shift-Invariance Sparse Coding for Audio Classification
Roger Grosse, Rajat Raina, Helen Kwong +1
Sparse coding is an unsupervised learning algorithm that learns a succinct high-level representation of the inputs given only unlabeled data; it represents each input as a sparse l…