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Conversational agents are consistently growing in popularity and many people interact with them every day. While many conversational agents act as personal assistants, they can have many different goals. Some are task-oriented, such as providing customer support for a bank or making a reservation. Others are designed to be empathetic and to form emotional connections with the user. The Alexa Prize Challenge

We study whether one can write a Matrix Product Density Operator (MPDO) as the Gibbs state of a quasi-local parent Hamiltonian. We conjecture this is the case for generic MPDO and give supporting evidences. To investigate the locality of the parent Hamiltonian, we take the approach of checking whether the quantum conditional mutual information decays exponentially. The MPDO we consider are constructed from

We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting the commutativity of Hamiltonian, the sparsity of interactions, and the prior knowledge of initial state. We achieve this using Trotterization for a class of interacting electrons that encompasses various physical systems, including the plane-wave-basis

Quantum state tomography is a powerful, but resource-intensive, general solution for numerous quantum information processing tasks. This motivates the design of robust tomography procedures that use relevant resources as sparingly as possible. Important cost factors include the number of state copies and measurement settings, as well as classical postprocessing time and memory. In this work, we present

We show that a variant of the surface code — the XZZX code — offers remarkable performance for fault-tolerant quantum computation. The error threshold of this code achieves the zero-rate hashing bound for every single-qubit Pauli noise channel; it is the first explicit code shown to have this universal property. We present numerical evidence that this threshold even exceeds the hashing bound for an experimentally

In object-oriented languages, constructors often have a combination of required and optional formal parameters. It is tedious and inconvenient for programmers to write a constructor by hand for each combination. The multitude of constructors is error-prone for clients, and client code is difficult to read due to the large number of constructor arguments. Therefore, programmers often use design patterns

When customers visit an ecommerce website, they will perform certain actions and will eventually either make a purchase or end their session without a purchase. Website operators can use the browsing behavior of their customers to build machine learning models that allow them to target customers that are more likely to convert with promotions. In this solution we will demonstrate how one can use SageMaker

Pretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens one by one. We show that PLLs outperform scores from autoregressive language models like GPT-2 in a variety of tasks. By rescoring ASR and NMT hypotheses, RoBERTa reduces an end-to-end LibriSpeech model

The Alexa Skills Kit SDK for Java helps you get a skill up and running quickly, letting you focus on skill logic instead of boilerplate code.

Amazon SageMaker Debugger is designed to be a debugger for machine learning models. It lets you go beyond just looking at scalars like losses and accuracies during training and gives you full visibility into all tensors 'flowing through the graph' during training or inference. Amazon SageMaker Debugger RulesConfig provides a mapping of builtin rules with default configurations. These configurations will

While models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this paper, we investigate if models are learning reading comprehension from QA datasets by evaluating BERT-based models across five datasets. We evaluate models on their generalizability to out-of-domain examples, responses

This project shows how to use Deep Graph Library (DGL) on Amazon SageMaker to train a graph neural network (GNN) model to perform entity resolution on customer identity graphs. See the project detail page to learn more about the techniques used.

Knowledge graphs have emerged as a key abstraction for organizing information in diverse domains and their embeddings are increasingly used to harness their information in various information retrieval and machine learning tasks. However, the ever growing size of knowledge graphs requires computationally efficient algorithms capable of scaling to graphs with millions of nodes and billions of edges. This

This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory. Second, a multi-task objective to maximize the average reward across the training

The Well-Architected framework has been developed to help cloud architects build the most secure, high-performing, resilient, and efficient infrastructure possible for their applications. This framework provides a consistent approach for customers and partners to evaluate architectures, and provides guidance to help implement designs that will scale with your application needs over time. This repository

This setup allows to train end-to-end neural models for spoken language understanding (SLU). It uses either the Snips SLU or the Fluent Speech dataset (FSC). This framework is built using pytorch with torchaudio and the transformer package from HuggingFace. We tested using pytorch 1.5.0 and torchaudio 0.5.0.

The SageMaker handwriting recognition solution applies deep learning techniques to transcribe text in images of passages into strings. If you have your own data, you can use this solution to label your own data and train a new network with it. Endpoints are then automatically deployed with the solution.

Virtual assistants (VAs) tend to be literal in their delivery of messages. Most likely, if you ask them to deliver a message, the VAs either send a recorded message or a literal transcription to the receiver. To make incremental improvement towards a virtual assistant that you may speak to conversationally and naturally, we have provided the data necessary to build AI systems that can convert the point

This solution provides a framework for Next Generation Sequencing (NGS) genomics secondary-analysis pipelines using AWS Step Functions and AWS Batch. It deploys AWS services to develop and run custom workflow pipelines, monitor pipeline status and performance, fail-over to on-demand, handle errors, optimize for cost, and secure data with least-privileges. The solution is designed to be starting point for

Slot-filling, Translation, Intent classification, and Language identification, or STIL, is a newly-proposed task for multilingual Natural Language Understanding (NLU). By performing simultaneous slot filling and translation into a single output language (English in this case), some portion of downstream system components can be monolingual, reducing development and maintenance cost. Results are given using