Detecting and answering ambiguous questions has been a challenging task in open-domain question answering. Ambiguous questions have different answers depending on their interpretation and can take diverse forms. Temporally ambiguous questions are one of the most common types of such questions. In this paper, we introduce TEMPAMBIQA, a manually annotated temporally ambiguous QA dataset consisting of 8,162 open-domain questions derived from existing datasets. Our annotations focus on capturing temporal ambiguity to study the task of detecting temporally ambiguous questions. We propose a novel approach by using diverse search strategies based on disambiguated versions of the questions. We also introduce and test non-search, competitive baselines for detecting temporal ambiguity using zero-shot and few-shot approaches.

In this article we explain what is batch normalization (BN) and how all it variations works. We try to explain the difference conceptually and mathematically. At the end you will have a deeper understanding on how normalization it's done in modern neural network. Batch Normalization is a widely used method in deep learning to make training faster and more stable. The main idea is to normalize each values of each internal layer of a deep neural network.

A robot chef has been trained to'taste' food at different stages of the chewing process – just like humans do. The machine, created at the University of Cambridge, consists of a probe that can detect salt levels in food attached to the end of a robotic arm. Experts used the robot to taste scrambled eggs during different stages of mastication, including a runny liquid as it would appear just prior to swallowing. According to the scientists, robotic chefs that'taste test' dishes instead of humans could be a fixture of busy restaurant kitchens of the future. A robot'chef' has been trained to taste food at different stages of the chewing process to assess whether it's sufficiently seasoned. The perception of taste is a complex process in humans that has evolved over millions of years.

When solving machine learning problems, simply training a model based on a problem-specific training machine learning algorithm does not guarantee either that the resulting model fully captures the underlying concept hidden in the training data or that the optimum parameter values were chosen for model training. Failing to test a model's performance means an underperforming model could be deployed on the production system, resulting in incorrect predictions. Choosing one model from the many available options based on intuition alone is risky. By generating different metrics, the efficacy of the model can be assessed. Use of these metrics reveals how well the model fits the data on which it was trained.

The Idea of Neural Networks was first introduced way back in 1950s, but it wasn't until 2012 that they come to action. Even application of Optimization Algorithm(Gradient Descent) in 2006 by Hinton, wasn't giving good results, it was introduction and usage of Activation functions, which revolutionized Deep Learning Research. There are various kind of Activation Functions that exists, and Some Researchers are still working on finding better functions, which can help networks to converge faster or use less layers etc. Lets go through each of them: The Main Problem we face is because of Saturated Gradients, as the Function ranges between 0 to 1, the values might remain constant, thus the gradients will have very less values. It has all properties of ReLU, plus it will never have dead ReLU problem. We can consider different multiplication factor to form different variations of Leaky ReLU.

Abstract: This paper demonstrates emergence of computational creativity in the field of music. Different aspects of creativity such as producer, process, product and press are studied and formulated. Different notions of computational creativity such as novelty, quality and typicality of compositions as products are studied and evaluated. We formulate an algorithmic perception on human creativity and propose a prototype that is capable of demonstrating human-level creativity. We then validate the proposed prototype by applying various creativity benchmarks with the results obtained and compare the proposed prototype with the other existing computational creative systems. I. INTRODUCTION Computational creativity is the modeling or replicating human creativity computationally. Traditionally computational creativity has focused more on creative systems' products or processes, though this focus has widened recently. Research on creativity offers four Ps of creativity (Rhodes, 1961; MacKinnon, 1970; Jordanous, 2016). These four P's are: 1. Person/Producer: a creative agent 2. Process: an activity done by the creative agent 3. Product: the product of the creative process 4. Press/Environment: the overall environment of creativity 110 The proposed methodology addresses all the four P's of creativity unlike most of recent works, which focus on these individually (Saunders, 2012; Gervas & Leon, 2014; Misztal & Indurkhya, 2014; Sosa & Gero, 2015; Besold & Plaza, 2015; Harmon, 2015). Figure 1 gives a simplified view of proposed computational creative system in the context of four P's of creativity.

A/B or split testing has been the standard way to optimize marketing campaigns for years. Google first ran an A/B test in 2000 to identify the optimum number of searches to display on its result pages. Today A/B testing is common practice in many different digital marketing channels including display ads, landing pages, email marketing, and pretty much anywhere that copy, images, or placement can be adjusted. A basic example of A/B testing would be splitting visitors to a website into two groups (A and B) and showing each group a slightly different version of the homepage. Everything else might be the same on the page apart from the header image. Let's say group A sees an image of a group of smiling people and group B sees an image of a city skyline.

Let us narrow down the phrase "implementing from scratch" a bit further in context of the 6 points I mentioned above. When we talk about "implementing from scratch," we need to narrow down the scope to make this question really tangible. Let's talk about a particular algorithm, simple logistic regression, to address the different points using concrete examples. I'd claim that logistic regression has been implemented more than thousand times. One reason why we'd still want to implement logistic regression from scratch could be that we don't have the impression that we fully understand how it works; we read a bunch of papers, and kind of understood the core concept though.

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.