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Found 4 papers, 1 papers with code
DeepSpeech models show Human-like Performance and Processing of Cochlear Implant Inputs
In this work, we use the deep neural network (DNN) DeepSpeech2, as a paradigm to investigate how natural input and cochlear implant-based inputs are processed over time.
When does compositional structure yield compositional generalization? A kernel theory
Even for compositional tasks that kernel models can in principle learn, we identify novel failure modes in compositional generalization that arise from biases in the training data and affect important compositional building blocks such as symbolic addition and context dependence (memorization leak and shortcut bias).
Neuroscience needs Network Science
We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities.
Learned Simulators for Turbulence
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the same low resolutions across various scientifically relevant metrics.
