Publications

The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables greatly diminishes the applicability of estimation theory in many practical implementations. The Holevo Cramér-Rao bound (HCRB) provides the most fundamental, simultaneously attainable

Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters

In membership/subscriber acquisition and retention, we sometimes need to recommend marketing content for multiple pages in sequence. Different from general sequential decision making process, the use cases have a simpler flow where customers per seeing recommended content on each page can only return feedback as moving forward in the process or dropping from it until a termination state. We refer to this

Query rewriting (QR) is an increasingly important technique for reducing user friction in a conversational AI system. User friction is caused by various reasons, including errors in automatic speech recognition (ASR), natural language understanding (NLU), entity resolution (ER) component, or users’ slip of the tongue. In this work, we propose a search-based self-learning QR framework: User Feedback Search

We consider the problem of topic-focused abstractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization

Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coherence in the literature. Maintaining hierarchical coherence while producing accurate forecasts

We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. It follows the previous methods that generate two views of an input graph through data augmentation. However, unlike contrastive methods that focus on instance-level discrimination, we optimize an innovative feature-level objective inspired by classical Canonical Correlation Analysis. Compared

A key application of conversational search is reining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it infeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation

The problem of confidential information leak can be addressed by using automatic tools that take a set of annotated inputs (the source) and track their flow to public sinks. Unfortunately, manually annotating the code with labels specifying the secret sources is one of the main obstacles in the adoption of such trackers. In this work, we present an approach for the automatic generation of labels for confidential

Natural language understanding (NLU) is one of the most critical components in goal-oriented dialog systems and enables innovative Big Data applications such as intelligent voice assistants (IVA) and chatbots. While recent advances in deep learning-based NLU models have achieved significant improvements in terms of accuracy, most existing works are monolingual or bilingual. In this work, we propose and

We devise a coreset selection method based on the idea of gradient matching: the gradients induced by the coreset should match, as closely as possible, those induced by the original training dataset. We evaluate the method in the context of continual learning, where it can be used to curate a rehearsal memory. Our method performs strong competitors such as reservoir sampling across a range of memory sizes

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data. However, given the increasing size of industrial datasets, in many practical situations the message passing computations required for sharing information across GNN layers are no longer scalable. Although various sampling methods have been introduced to approximate full-graph

Fine-tuning self-supervised pre-trained language models such as BERT has significantly improved state-of-the-art performance on natural language processing tasks. Similar finetuning setups can also be used in commercial large scale Spoken Language Understanding (SLU) systems to perform intent classification and slot tagging on user queries. Finetuning such powerful models for use in commercial systems requires

Building machine learning models for low resource languages is extremely challenging due to the lack of available training data (either un-annotated or annotated). To support such scenarios, zero-shot cross lingual transfer is used where the machine learning model is trained on a resource rich language and is directly tested on the resource poor language. In this paper, we present a technique which improves

This report explains why cloud computing supports a variety of power system businesses and summarizes the latest cloud adoption use cases in the power industry. It includes the benefits and risks of moving to the cloud while suggesting risk mitigation strategies at t he same time. It also provides valuable guidelines and suggestions for power industry professionals who are considering cloud solutions yet