[PDF][PDF] Exploring deep learning for multimodal understanding

M Lao - 2023 - scholarlypublications …
[14] Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T.,
Louf, R., Funtowicz, M., et al.: Transformers: State-of-the-art natural language processing. In …

Disagreement and confusion over the status of DNNs as models of vision

JS Bowers, G Malhotra, M Dujmović, ML Montero… - 2023 - europepmc.org
We are pleased there is widespread agreement that psychology has an important role to
play in building better models of vision. But there are important confusions and …

Dynamics of learning and generalization in neural networks

M Pezeshki - 2022 - papyrus.bib.umontreal.ca
Neural networks perform remarkably well in a wide variety of machine learning tasks and
have had a profound impact on the very definition of artificial intelligence (AI). However …

Deep diversity learning for better generalization to unseen domains

T Duboudin - 2022 - theses.hal.science
A growing number of embedded applications, confronted with diversified, shifting, and
uncontrolled environments, require an increased degree of adaptability and analysis …

Environment Partitioning For Invariant Learning By Decorrelation

Y Liao, WU Qi, YAN Xing - openreview.net
Invariant learning methods try to find an invariant predictor across several environments and
have become popular in OOD generalization. However, in situations where environments do …

Unraveling the Complexities of Simplicity Bias: Mitigating and Amplifying Factors

X Gong, T Fu - NeurIPS 2023 Workshop on Mathematics of Modern … - openreview.net
The success of neural networks depends on the generalization ability, while Shah et al.
conclude that the inherent bias towards simplistic features, a phenomenon called* Simplicity …