The paradigm of pre-training followed by fine-tuning on downstream tasks has become the mainstream method in natural language processing tasks. Although pre-trained models have the advantage of generalization, their performance may still vary significantly across different domain tasks. This is because the data distribution in different domains varies. For example, the different parts of the sentence 'He married Smt. Dipali Ghosh in 1947 and led a very happy married life' may have different impact for downstream tasks. For similarity calculations, words such as 'led' and 'life' are more important. On the other hand, for sentiment analysis, the word 'happy' is crucial. This indicates that different downstream tasks have different levels of sensitivity to sentence components. Our starting point is to scale information of the model and data according to the specifics of downstream tasks, enhancing domain information of relevant parts for these tasks and reducing irrelevant elements for different domain tasks, called SIFTER. In the experimental part, we use the SIFTER to improve SimCSE by constructing positive sample pairs based on enhancing the sentence stem and reducing the unimportant components in the sentence, and maximize the similarity between three sentences. Similarly, SIFTER can improve the gate mechanism of the LSTM model by short-circuiting the input gate of important words so that the LSTM model remembers the important parts of the sentence. Our experiments demonstrate that SIFTER outperforms the SimCSE and LSTM baselines.

Given the volume of data needed to train modern machine learning models, external suppliers are increasingly used. However, incorporating external data poses data poisoning risks, wherein attackers manipulate their data to degrade model utility or integrity. Most poisoning defenses presume access to a set of clean data (or base set). While this assumption has been taken for granted, given the fast-growing research on stealthy poisoning attacks, a question arises: can defenders really identify a clean subset within a contaminated dataset to support defenses? This paper starts by examining the impact of poisoned samples on defenses when they are mistakenly mixed into the base set. We analyze five defenses and find that their performance deteriorates dramatically with less than 1% poisoned points in the base set. These findings suggest that sifting out a base set with high precision is key to these defenses' performance. Motivated by these observations, we study how precise existing automated tools and human inspection are at identifying clean data in the presence of data poisoning. Unfortunately, neither effort achieves the precision needed. Worse yet, many of the outcomes are worse than random selection. In addition to uncovering the challenge, we propose a practical countermeasure, Meta-Sift. Our method is based on the insight that existing attacks' poisoned samples shifts from clean data distributions. Hence, training on the clean portion of a dataset and testing on the corrupted portion will result in high prediction loss. Leveraging the insight, we formulate a bilevel optimization to identify clean data and further introduce a suite of techniques to improve efficiency and precision. Our evaluation shows that Meta-Sift can sift a clean base set with 100% precision under a wide range of poisoning attacks. The selected base set is large enough to give rise to successful defenses.

Data scientists working on text analytics know cleaning data can be time consuming. Users of DiscoverText build reusable custom machine classifiers or "sifters" to find the most (or least) relevant items before using other classifiers for sorting items into topic, sentiment, and other categories. DiscoverText combines hybrid data science methods (measurement, adjudication, iteration, replication) along with established e-discovery text analytics tools, to shorten a process that used to last weeks or months when words get sorted in spreadsheets. Our machine-learning sifters are created in hours or just a few minutes using crowdsourcing. We offer an API and support technical integrations with Twitter and SurveyMonkey.

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.

No other technology was more important over the past decade than artificial intelligence. Stanford's Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become "AI-first" companies. In the next decade, all technology will be considered "AI technology." And we can thank deep learning for that. Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brain's neural network.

No other technology was more important over the past decade than artificial intelligence. Stanford's Andrew Ng called it the new electricity, and both Microsoft and Google changed their business strategies to become "AI-first" companies. In the next decade, all technology will be considered "AI technology." And we can thank deep learning for that. Deep learning is a friendly facet of machine learning that lets AI sort through data and information in a manner that emulates the human brain's neural network.