1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Neither hype nor gloom do DNNs justice
Felix A. Wichmann, Simon Kornblith, Robert Geirhos
Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNN…
cs.CV2023
Fixing the problems of deep neural networks will require better training data and learning algorithms
Drew Linsley, Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those…
cs.CL2023★ 1 cited
Beyond the limitations of any imaginable mechanism: large language models and psycholinguistics
Conor Houghton, Nina Kazanina, Priyanka Sukumaran
Large language models are not detailed models of human linguistic processing. They are, however, extremely successful at their primary task: providing a model for language. For thi…