We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these results. Code URL: https://github.com/google-research/google-research/blob/master/repnet/

The problem of modeling ropes arises in many applications, including providing haptic feedback to surgeons who are using surgical robots to realign the distal and proximal ends of split bones. Here, we consider a simplified, 2D variant of the haptic feedback estimation problem and discuss how visibility decompositions greatly simplify the problem. Then, we introduce an efficient, concise algorithm for modeling the dynamics of 2D ropes around polygonal obstacles in O(n) time, where n is the number of line segment obstacles. We start by providing a brief definition of our 2D rope problem. The open line segment obstacles constitute C entirely.

In this note, we first recall the nonconvex problem setting and introduce the optimal PAGE algorithm (Li et al., 2021). Then we provide a simple and clean convergence analysis of PAGE for achieving optimal convergence rates. Moreover, PAGE and its analysis can be easily adopted and generalized to other works. We hope that this note provides the insights and is helpful for future works. The nonconvex function f has the following two forms: 1. Finite-sum form Now we define the following standard assumptions.

Turing machine and decision tree have developed independently for a long time. With the recent development of differentiable models, there is an intersection between them. Neural turing machine(NTM) opens door for the memory network. It use differentiable attention mechanism to read/write external memory bank. Differentiable forest brings differentiable properties to classical decision tree. In this short note, we show the deep connection between these two models. That is: differentiable forest is a special case of NTM. Differentiable forest is actually decision tree based neural turing machine. Based on this deep connection, we propose a response augmented differential forest (RaDF). The controller of RaDF is differentiable forest, the external memory of RaDF are response vectors which would be read/write by leaf nodes.

This is a short communication on a Lyapunov function argument for softmax in bandit problems. There are a number of excellent papers coming out using differential equations for policy gradient algorithms in reinforcement learning \cite{agarwal2019optimality,bhandari2019global,mei2020global}. We give a short argument that gives a regret bound for the soft-max ordinary differential equation for bandit problems. We derive a similar result for a different policy gradient algorithm, again for bandit problems. For this second algorithm, it is possible to prove regret bounds in the stochastic case \cite{DW20}. At the end, we summarize some ideas and issues on deriving stochastic regret bounds for policy gradients.

"Behave rationally" does not always achieve the goals successfully. In the early 1980s, Lecture notes cover much of the course material and will be available online before class. Note: the text is on-line.

AI the revolutionary concept that every tech company wants to crack has come far in a short period of time and we thought.When people think about AI they associate it to sci-fi concept but the reality is AI is playing an important part in out day to day life from phone calculator to the dream of self driving cars. But what would be even more useful is the AI's ability to understand language like we do.Researchers struggle to get machine to understand language like humans.We humans have the ability to transform words into 3D model but machines don't.I think we are solving the problem the wrong way rather than teaching the machine to recognise the words alone we can give the the ability to transform word into 3D models like we humans do.But this ability is not easy to master.So, we'll have to wait few more years to really get the most out of AI.Until then we can be optimistic about it.

In this note, we argue that the axiomatic requirement of range to the measure of aggregated total uncertainty (ATU) in Dempster-Shafer theory is not reasonable. Keywords: Dempster-Shafer theory, Uncertainty measure Dempster-Shafer theory [1, 2] is widely applied to uncertainty modeling [3, 4]. Two types of uncertainty, namely nonspecificity and discord, are coexisting in the Dempster-Shafer theory [5, 6]. A justifiable measure to these uncertainty is necessary to describe the essential characters of basic probability assignment function(BPA). To be justifiable, for a measure called as aggregated total uncertainty (ATU), some requirements are necessary.