4 citations · 7 across the 5 of their papers we have counts for
7 papers
Diffusion Model Alignment Using Direct Preference Optimization
Bram Wallace, Meihua Dang, Rafael Rafailov +7
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' prefe…
End-to-End Diffusion Latent Optimization Improves Classifier Guidance
Bram Wallace, Akash Gokul, Stefano Ermon +1
Classifier guidance -- using the gradients of an image classifier to steer the generations of a diffusion model -- has the potential to dramatically expand the creative control ove…
EDICT: Exact Diffusion Inversion via Coupled Transformations
Bram Wallace, Akash Gokul, Nikhil Naik
Finding an initial noise vector that produces an input image when fed into the diffusion process (known as inversion) is an important problem in denoising diffusion models (DDMs),…
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
Samar Khanna, Bram Wallace, Kavita Bala +1
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium re…
Learning Rich Nearest Neighbor Representations from Self-supervised Ensembles
Bram Wallace, Devansh Arpit, Huan Wang +1
Pretraining convolutional neural networks via self-supervision, and applying them in transfer learning, is an incredibly fast-growing field that is rapidly and iteratively improvin…
Extending and Analyzing Self-Supervised Learning Across Domains
Bram Wallace, Bharath Hariharan
Self-supervised representation learning has achieved impressive results in recent years, with experiments primarily coming on ImageNet or other similarly large internet imagery dat…
Few-Shot Generalization for Single-Image 3D Reconstruction via Priors
Bram Wallace, Bharath Hariharan
Recent work on single-view 3D reconstruction shows impressive results, but has been restricted to a few fixed categories where extensive training data is available. The problem of…