In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt.

We present a flow-based approach to the optimal transport (OT) problem between two continuous distributions $\pi_0,\pi_1$ on $\mathbb{R}^d$, of minimizing a transport cost $\mathbb{E}[c(X_1-X_0)]$ in the set of couplings $(X_0,X_1)$ whose marginal distributions on $X_0,X_1$ equals $\pi_0,\pi_1$, respectively, where $c$ is a cost function. Our method iteratively constructs a sequence of neural ordinary differentiable equations (ODE), each learned by solving a simple unconstrained regression problem, which monotonically reduce the transport cost while automatically preserving the marginal constraints. This yields a monotonic interior approach that traverses inside the set of valid couplings to decrease the transport cost, which distinguishes itself from most existing approaches that enforce the coupling constraints from the outside. The main idea of the method draws from rectified flow, a recent approach that simultaneously decreases the whole family of transport costs induced by convex functions $c$ (and is hence multi-objective in nature), but is not tailored to minimize a specific transport cost. Our method is a single-object variant of rectified flow that guarantees to solve the OT problem for a fixed, user-specified convex cost function $c$.

You may have heard ETL getting thrown in sentences here and there when you're reading blogs or watching YouTube videos. So what does ETL have to do with machine learning? For those who don't already know, machine learning is a type of artificial intelligence that uses data analysis to predict accurate outcomes. It is the machine learning algorithms that produce these predicted outputs by learning on historical data and its features. It is the process of moving data from multiple sources to bring it to a centralized single database.

Print/display a couple of input and target output batches to ensure the results are fine. You can also try to input random data to see if the error continues. If it does, it means the neural network is turning data into garbage. So you have to start debugging layer by layer/op by op to locate the issue. Even if your data is fine, the code that pushes the input to the neural network might be broken. Hence, it's important to check the input of the first layer.

I don't want to explain it in the usual way of other experts, just come out of other definitions you read before, before reading this. Before that, why experts are calling it deep learning? Is it that much deep? Presumptions may differ from person to person. But what actually it is?

In the next release of adabelief-pytorch, we will modify the default of several arguments, in order to fit the needs of for general tasks such as GAN and Transformer. Please check if you specify these arguments or use the default when upgrade from version 0.0.5 to higher. AdaBelief uses a different denominator from Adam, and is orthogonal to other techniques such as recification, decoupled weight decay, weight averaging et.al. This implies when you use some techniques with Adam, to get a good result with AdaBelief you might still need those techniques. The default value epsilon 1e-8 is not a good option in many cases, will modify it later to 1e-12 or 1e-16 later. If you task needs a "non-adaptive" optimizer, which means SGD performs much better than Adam(W), such as on image recognition, you need to set a large epsilon(e.g.

AI-enabled cameras are helping the RTA to analyse the facial expressions of passengers which then allows decision-makers to take actions to rectify the situation. Smart cameras powered by artificial intelligence (AI) technology installed by Roads and Transport Authority (RTA) have screened the facial expressions of 26,476 customers at four service centres (Deira, Awir, Barsha and Um Ramool) during the first half of 2019 year. Cameras revealed that the overall customers' happiness rating ranged from 85.6 per cent to as much as 92.8 per cent. In explanation, Ahmed Mahboub, Executive Director of Customers Happiness at RTA's Corporate Administrative Support Services Sector, said: "The installation of smart cameras to measure customers satisfaction rating is part UAE's AI Strategy, Dubai's Smart City Initiative, and RTA's Strategic Goal Smart Dubai. He cautioned that variations in the happiness index are not attributed to the quality or speed of delivering services. The customer might be experiencing personal circumstances negatively impacting his or her facial expression, he noted. Instead, cameras provide instant and accurate feed about customers happiness rating. Accordingly, decision-makers will be in a better position to take actions to rectify the situation," he explained.

There's so much great TV out there these days, you're probably missing what could be your new favorite show without even realizing it. So let IGN help you out, as we list nine current shows you should binge. We're skipping over the shows that are among the most obvious – We don't think you need us to tell you to watch Game of Thrones and even a recent series like Stranger Things is on almost everyone's radar at this point. Below you'll find a mixture of critical darlings, buzzed about series and a couple IGN writer favorites. We're in the era of "Peak TV," and we know you can't watch everything, but these shows are worth your time! Yes, it's a series that got off to a slow start in its first season, but trust us, you want to stick with this one.