Whether, and to what extent, what is learned in the college classroom (if left unused) is likely to be mostly forgotten a “few” years after graduation. How this work can influence practical learning engineering at scale, with examples from Kaplan.

The focus of the workshop is on the computations and learning involved in human speech understanding that are required for speech-enabled machines, following CBMM’s mission to understand intelligence in brains and replicate it in machines.

Daniel Rockmore - Professor of Mathematics, William H. Neukom 1964 Distinguished Professor of Computational Science, Director of the Neukom Institute for Computational Science, Dartmouth College - discusses, how do we program moral philosophy and values?

The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. ...

We systematically explored a spectrum of normalization algorithms related to Batch Normalization (BN) and propose a generalized formulation that simultaneously solves two major limitations of BN: (1) online learning and (2) recurrent learning.

Why are human inferences sometimes remarkably close to the Bayesian ideal and other times systematically biased? One notable instance of this discrepancy is that tasks where the candidate hypotheses are explicitly available result ...

We present a study on two key characteristics of human syntactic annotations: anchoring and agreement.