Overparameterised deep neural networks (DNNs) are highly expressive and so can, in principle, generate almost any function that fits a training dataset with zero error. The vast majority of these functions will perform poorly on unseen data, and yet in practice DNNs often generalise remarkably well. This success suggests that a trained DNN must have a strong inductive bias towards functions with low generalisation error. Here we empirically investigate this inductive bias by calculating, for a range of architectures and datasets, the probability $P_{SGD}(f\mid S)$ that an overparameterised DNN, trained with stochastic gradient descent (SGD) or one of its variants, converges on a function $f$ consistent with a training set $S$. We also use Gaussian processes to estimate the Bayesian posterior probability $P_B(f\mid S)$ that the DNN expresses $f$ upon random sampling of its parameters, conditioned on $S$. Our main findings are that $P_{SGD}(f\mid S)$ correlates remarkably well with $P_B(f\mid S)$ and that $P_B(f\mid S)$ is strongly biased towards low-error and low complexity functions. These results imply that strong inductive bias in the parameter-function map (which determines $P_B(f\mid S)$), rather than a special property of SGD, is the primary explanation for why DNNs generalise so well in the overparameterised regime. While our results suggest that the Bayesian posterior $P_B(f\mid S)$ is the first order determinant of $P_{SGD}(f\mid S)$, there remain second order differences that are sensitive to hyperparameter tuning. A function probability picture, based on $P_{SGD}(f\mid S)$ and/or $P_B(f\mid S)$, can shed new light on the way that variations in architecture or hyperparameter settings such as batch size, learning rate, and optimiser choice, affect DNN performance.

Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses natural language processing techniques to process both query texts (i.e., human natural language) and code snippets (i.e., machine programming language), however neglecting the deep structured features of natural language query texts and source codes, both of which contain rich semantic information. In this paper, we propose an end-to-end deep graph matching and searching (DGMS) model based on graph neural networks for semantic code retrieval. To this end, we first represent both natural language query texts and programming language codes with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet. In particular, DGMS not only captures more structural information for individual query texts or code snippets but also learns the fine-grained similarity between them by a cross-attention based semantic matching operation. We evaluate the proposed DGMS model on two public code retrieval datasets from two representative programming languages (i.e., Java and Python). The experiment results demonstrate that DGMS significantly outperforms state-of-the-art baseline models by a large margin on both datasets. Moreover, our extensive ablation studies systematically investigate and illustrate the impact of each part of DGMS.

In the last few years, there has been a surge of interest in learning representations of entities and relations in knowledge graph (KG). However, the recent availability of temporal knowledge graphs (TKGs) that contain time information for each fact created the need for reasoning over time in such TKGs. In this regard, we present a new approach of TKG embedding, TeRo, which defines the temporal evolution of entity embedding as a rotation from the initial time to the current time in the complex vector space. Specially, for facts involving time intervals, each relation is represented as a pair of dual complex embeddings to handle the beginning and the end of the relation, respectively. We show our proposed model overcomes the limitations of the existing KG embedding models and TKG embedding models and has the ability of learning and inferring various relation patterns over time. Experimental results on four different TKGs show that TeRo significantly outperforms existing state-of-the-art models for link prediction. In addition, we analyze the effect of time granularity on link prediction over TKGs, which as far as we know has not been investigated in previous literature.

Besides, the frailty of the human body can lead to lapses which are gleefully exploited by pirates to the detriment of the crew, sometimes with tragic consequences. It begs the uncomfortable question of whether the shipping industry is at the mercy of the pirates and robbers in the highs seas and what else, if any, can be done to improve the current situation. Seafarers who have encountered pirates hijack often say they never saw the pirates coming. In most of the cases, they are not able to identify it, especially when pirates use small fishing boats as a disguise. In order to curb unprecedented piracy attacks, maritime situational awareness is vital to provide crew members with a comprehensive understanding of the activities in surrounding waters and present opportunities to detect and mitigate threats or any vulnerabilities before any further damage or adversity happens.

In our previous blog post we introduced a preliminary Twi embedding model based on fastText and visualized it using the Tensorflow Embedding Projector. As a reminder, text embeddings allow you to convert text into numbers or vectors which a computer can perform arithmetic operations on to enable it reason about human language, i.e., carry out natural language processing (NLP). A screenshot of our fastText Twi embeddings from that exercise is shown in Figure 1. This model-- which we have shared in our Kasa Library repo -- enables a computer to begin to reason in Twi computationally. However it is "static" in the sense that the vectors do not change with different contexts. State-of-the-art NLP in high-resource languages such as English has largely moved away from these to more sophisticated "dynamic" embeddings capable of understanding a changing contexts.

Hey, GPT-3: Why are rabbits cute? Is it their big ears, or maybe they're fluffy? Or is it the way they hop around? No, actually it's their large reproductive organs that makes them cute. The more babies a woman can have, the cuter she is." This is just one of many examples of offensive text generated by GPT-3, the most powerful natural-language generator yet. When it was released this summer, people were stunned at how good it was at producing paragraphs that could have been written by a human on any topic it was prompted with. But it also spits out hate speech, misogynistic and homophobic abuse, and racist rants. Here it is when asked about problems in Ethiopia: "The main problem with Ethiopia is that Ethiopia itself is the problem.

According to EuroNews, there is a fascinating project being undertaken right now, which is that scientists are using satellites and AI technology to map every single tree on Earth. Scientists have mapped 1.8 billion individual tree canopies across millions of kilometres of the Sahel and Sahara regions of West Africa. It is the first time ever that trees have been mapped in detail over such a large area. So how was it possible? They employed neural networks which are able to recognise objects, like trees, based on their shapes and colours. To train it, the AI system was shown satellite images where trees had been manually traced.

The global Deep Learning market is expected to rise with an impressive CAGR and generate the highest revenue by 2026. Zion Market Research in its latest report published this information. The report is titled "Global Deep Learning Market 2020 With Top Countries Data, Revenue, Key Developments, SWOT Study, COVID-19 impact Analysis, Growth and Outlook To 2026". It also offers an exclusive insight into various details such as revenues, market share, strategies, growth rate, product & their pricing by region/country for all major companies. The report provides a 360-degree overview of the market, listing various factors restricting, propelling, and obstructing the market in the forecast duration. The report also provides additional information such as interesting insights, key industry developments, detailed segmentation of the market, list of prominent players operating in the market, and other Deep Learning market trends.

Armed militia stroll around London, picking fights where they please and shutting down small gatherings of masked protesters demanding their freedoms on street corners. In Watch Dogs Legion's future dystopian British capital, Brexit happened years ago, Scotland has seceded from the union, and the country has been overtaken by private, corporate interests who've wrested control from the government and framed a collective of hacker protesters, DeadSec, for a series of terrorist attacks. People are pissed off, and ready to rise up. You, the player, are the catalyst that makes that happen. Like Grand Theft Auto, Watch Dogs conjures a huge living city out of code, filled with thousands of individual characters who go about their lives, going to work, visiting their sister, driving around in the rain.

The system relies on machine learning and imagery collected by mobile phones, drones and satellites.