12 papers · 1 filter
LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
Christoforos N. Spartalis, Theodoros Semertzidis, Petros Daras +1
We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from scratch. LoTUS smooths…
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou +4
Vision-language alignment is crucial for various downstream tasks such as cross-modal generation and retrieval. Previous multimodal approaches like CLIP utilize InfoNCE to maximize…
Towards Uniformity and Alignment for Multimodal Representation Learning
Wenzhe Yin, Pan Zhou, Zehao Xiao +4
Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-…
Superposition as Lossy Compression: Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability
Leonard Bereska, Zoe Tzifa-Kratira, Reza Samavi +1
Neural networks achieve remarkable performance through superposition: encoding multiple features as overlapping directions in activation space rather than dedicating individual neu…
Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
Christoforos N. Spartalis, Theodoros Semertzidis, Petros Daras +1
We introduce SAFEMax, a novel method for Machine Unlearning in diffusion models. Grounded in information-theoretic principles, SAFEMax maximizes the entropy in generated images, ca…
Mechanistic PDE Networks for Discovery of Governing Equations
Adeel Pervez, Efstratios Gavves, Francesco Locatello
We present Mechanistic PDE Networks -- a model for discovery of governing partial differential equations from data. Mechanistic PDE Networks represent spatiotemporal data as space-…