[1707.05320] Quantum Indistinguishability in Chemical Reactions — Read on arxiv.org/abs/1707.05320

# Category: News

## Quantum Physics May Be Even Spookier Than You Think – Scientific American

It is the central question in quantum mechanics, and no one knows the answer: What really happens in a superposition—the peculiar circumstance in which particles seem to be in two or more places or states at once? Now, in a new paper a team of researchers in Israel and Japan has proposed an experiment that could finally…

## Machine learning leads mathematicians to unsolvable problem

Simple artificial-intelligence problem puts researchers up against a logical paradox discovered by famed mathematician Kurt Gödel. — Read on http://www.nature.com/articles/d41586-019-00083-3

## A unified, mechanistic framework for developmental and evolutionary change

A unified, mechanistic framework for developmental and evolutionary change http://arxiv.org/abs/1809.02331v1 #lib_arXiv

## A quantum-inspired classical algorithm for recommendation systems

This preprint shows that Kerenidis and Prakash’s quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms. https://arxiv.org/pdf/1807.04271.pdf

## Variational Option Discovery Algorithms

Paper Abstract: “We explore methods for option discovery based on variational inference and make two algorithmic contributions. First: we highlight a tight connection between variational option discovery methods and variational autoencoders, and introduce Variational Autoencoding Learning of Options by Reinforcement (VALOR), a new method derived from the connection. In VALOR, the policy encodes contexts from…

## Mysterious ‘Jumping Gene’

The Mysterious ‘Jumping Gene’ That Appears 500,000 Times in Human DNA https://www.theatlantic.com/science/archive/2018/06/line1-jumping-gene/563354/ Shared from my Google feed

## Neural Ordinary Differential Equations

https://arxiv.org/abs/1806.07366 paper abstract: “We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost, adapt…