6 citations · 10 across the 3 of their papers we have counts for
7 papers
Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
Duncan Soiffer, Chandler Squires, Yuan Guan +2
The task of compositional generation involves using a conditional generative model, trained only on a subset of the possible conditions, to produce samples from compositionally-def…
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw +2
Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biase…
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Thomas Jiralerspong, Berton Earnshaw, Jason Hartford +2
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks. In this work, we aim to build inductiv…
Properties from Mechanisms: An Equivariance Perspective on Identifiable Representation Learning
Kartik Ahuja, Jason Hartford, Yoshua Bengio
A key goal of unsupervised representation learning is "inverting" a data generating process to recover its latent properties. Existing work that provably achieves this goal relies…
Exemplar Guided Active Learning
Jason Hartford, Kevin Leyton-Brown, Hadas Raviv +3
We consider the problem of wisely using a limited budget to label a small subset of a large unlabeled dataset. We are motivated by the NLP problem of word sense disambiguation. For…
Valid Causal Inference with (Some) Invalid Instruments
Jason Hartford, Victor Veitch, Dhanya Sridhar +1
Instrumental variable methods provide a powerful approach to estimating causal effects in the presence of unobserved confounding. But a key challenge when applying them is the reli…