Recent progress in the development of voltage indicators [1-8] has brought us closer to a longstanding goal incellular neuroscience: imaging the full spatiotemporal voltageonadendritic tree. These recordings have the potential (pun not intended) to resolve fundamental questions about the computations performed by dendrites -- questions that have remained open for more than a century[9,10].

Eq. (15) has been derived for perturbations of thetrainingdata. At this point we have a choice of how to adversarially perturb the classifier to achieve the largest effectonthenetworkoutput. Then, with similar reasoning that led to Eq.(12)wenowobtain: When we measure quantities from the neural net, we subtract the initialpredictionf0,since the NTK expression Eq.(3) does not take the initialization of the network into account. D.2 AdditionalPlots Complementing Figure 1 in the main text, we show (the first 100) NTK features in Robustness UsefulnessspacedefinedinSec.

Abstract: We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs. We equip the search process with an interpreter or a read-eval-print-loop (REPL), which immediately executes partially written programs, exposing their semantics. The REPL addresses a basic challenge of program synthesis: tiny changes in syntax can lead to huge changes in semantics. We train a pair of models, a policy that proposes the new piece of code to write, and a value function that assesses the prospects of the code written so-far. At test time we can combine these models with a Sequential Monte Carlo algorithm.

This workshop builds on our AI for Social Good workshop at NeurIPS 2018, ICLR 2019 and ICML 2019. The accelerating pace of intelligent system research and real world deployment presents three clear challenges for producing "good" intelligent systems: (1) the research community lacks incentives and venues for results centered on social impact, (2) deployed systems often produce unintended negative consequences, and (3) there is little consensus for public policy that maximizes "good" social impacts, while minimizing the likelihood of harm. As a result, researchers often find themselves without a clear path to positive real world impact. The Workshop on AI for Social Good addresses these challenges by bringing together machine learning researchers, social impact leaders, ethicists, and public policy leaders to present their ideas and applications for maximizing the social good. This workshop is a collaboration of three formerly separate lines of research (i.e., this is a "joint" workshop), including researchers in applications-driven AI research, applied ethics, and AI policy.