We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing.

Neuron interpretation offers valuable insights into how knowledge is structured within a deep neural network model.

They are 300 Poisson neurons, where the first 100 encode the whisker stimulus, the next 100 encode the auditory cue and the last 100 act as an extra noise source for our model. Out of the 300 neurons, 60 of them are inhibitory (red). The input neurons project unrestrictedly to the whole RSNN. The baseline firing rate of all input neurons is 5 Hz. The whisker stimulus and auditory cue are encoded with an increase of the firing rate for 10 ms, starting 4 ms after the onset of the actual stimuli.

Combined, these features provide both theoretical and practical simplicity.

Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].

In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples.

In this work, we provide a general framework for studying these problems, which we refer to as Responsible AI (RAI) games .