Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging since the pretraining and downstream tasks can be very different.

Identifying interpretable patterns in multi-electrode recordings is a longstanding and increasingly pressing challenge in neuroscience.

One key difference between reinforcement learning in simulated vs. real-world environments is that, in most simulated environments, the agent can fully observe

This result is well-known [29] but we include a proof here for completeness. First, let's define the conditional risk of h at x, denoted by R Next, suppose q ( x) null= c and h( x) null= h( x). From the proof of Lemma 4.1, we know that The proof of Lemma 4.5 depends on another lemma, which will also be useful in the unknown hypothesis class setting. This is the first claim of the lemma. Thus, ( null) must be false!

Multi-agent active perception is a task where a team of agents cooperatively gathers observations to compute a joint estimate of a hidden variable.