Zobrazeno 1 - 6
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pro vyhledávání: '"Bhalla, Usha"'
With the growing complexity and capability of large language models, a need to understand model reasoning has emerged, often motivated by an underlying goal of controlling and aligning models. While numerous interpretability and steering methods have
Externí odkaz:
http://arxiv.org/abs/2411.04430
Do different generative image models secretly learn similar underlying representations? We investigate this by measuring the latent space similarity of four different models: VAEs, GANs, Normalizing Flows (NFs), and Diffusion Models (DMs). Our method
Externí odkaz:
http://arxiv.org/abs/2407.13449
As Artificial Intelligence (AI) tools are increasingly employed in diverse real-world applications, there has been significant interest in regulating these tools. To this end, several regulatory frameworks have been introduced by different countries
Externí odkaz:
http://arxiv.org/abs/2407.08689
CLIP embeddings have demonstrated remarkable performance across a wide range of multimodal applications. However, these high-dimensional, dense vector representations are not easily interpretable, limiting our understanding of the rich structure of C
Externí odkaz:
http://arxiv.org/abs/2402.10376
Publikováno v:
NeurIPS 2023 (Thirty-seventh Conference on Neural Information Processing Systems)
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in pri
Externí odkaz:
http://arxiv.org/abs/2307.15007
Vision-language (VL) pretrained models have achieved impressive performance on multimodal reasoning and zero-shot recognition tasks. Many of these VL models are pretrained on unlabeled image and caption pairs from the internet. In this paper, we stud
Externí odkaz:
http://arxiv.org/abs/2203.17271