Modern neural language models widely used in tasks across NLP risk memorizing sensitive information from their training data. As models continue to scale up in parameters, training data, and compute, understanding memorization in language models is both important from a learning-theoretical point of view, and is practically crucial in real world applications. An open question in previous studies of memorization in language models is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing "common" memorization such as familiar phrases, public knowledge or templated texts. In this paper, we provide a principled perspective inspired by a taxonomy of human memory in Psychology. From this perspective, we formulate a notion of counterfactual memorization, which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We further estimate the influence of each training example on the validation set and on generated texts, and show that this can provide direct evidence of the source of memorization at test time.
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.
The Robotic Process Automation (RPA) Market report includes overview, which interprets value chain structure, industrial environment, regional analysis, applications, market size, and forecast. This is a latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report.
As neural networks revolutionize many applications, significant privacy concerns emerge. Owners of private data wish to use remote neural network services while ensuring their data cannot be interpreted by others. Service providers wish to keep their model private to safeguard its intellectual property. Such privacy conflicts may slow down the adoption of neural networks in sensitive domains such as healthcare. Privacy issues have been addressed in the cryptography community in the context of secure computation. However, secure computation protocols have known performance issues. E.g., runtime of secure inference in deep neural networks is three orders of magnitude longer comparing to non-secure inference. Therefore, much research efforts address the optimization of cryptographic protocols for secure inference. We take a complementary approach, and provide design principles for optimizing the crypto-oriented neural network architectures to reduce the runtime of secure inference. The principles are evaluated on three state-of-the-art architectures: SqueezeNet, ShuffleNetV2, and MobileNetV2. Our novel method significantly improves the efficiency of secure inference on common evaluation metrics.
Israel is a country full of history, which is why they have more museums per capita than any other country. They also have the oldest continuously used cemetery in the world and the oldest continuously inhabited city in the world. Hearing that, you'd think that not a whole lot has changed over the years, but one thing that has constantly been evolving is their ability to innovate and be productive. Next to the U.S. and Canada, Israel has the largest number of publicly traded companies, which shows that they can also build successful businesses. Our recent article on "The Top-10 Biggest Startups in Israel by Funding" proved to be quite popular so we decided to do another article on the top 10 Israeli artificial intelligence (AI) startups.
Europe is a hotbed of AI innovation. Here are 25 AI start-ups to watch out for in 2017 and beyond. There are literally hundreds of promising companies pushing the boundaries of artificial intelligence and machine learning in Europe. We've included a number of Israel-based start-ups because they too fall into the sphere of influence of European investors. And, judging by recent acquisitions of Israel-based machine vision and AI companies by players like Apple and Intel, they are definitely producing the goods.