Publications

Motivated by estimation of quantum noise models, we study the problem of learning a Pauli channel, or more generally the Pauli error rates of an arbitrary channel. By employing a novel reduction to the “Population Recovery” problem, we give an extremely simple algorithm that learns the Pauli error rates of an n-qubit channel to precision є in l∞ using just O (1/ є 2 ) log( n/ є ) applications of the channel

Fault-tolerant quantum error correction is essential for implementing quantum algorithms of significant practical importance. In this work, we propose a highly eective use of the surface-GKP code, i.e., the surface code consisting of bosonic GKP qubits instead of bare two-dimensional qubits. In our proposal, we use error-corrected two-qubit gates between GKP qubits and introduce a maximum likelihood decoding

We introduce ACES, a method for scalable noise metrology of quantum circuits that stands for Averaged Circuit Eigenvalue Sampling. It simultaneously estimates the individual error rates of all the gates in collections of quantum circuits, and can even account for space and time correlations between these gates. ACES strictly generalizes randomized benchmarking (RB), interleaved RB, simultaneous RB, and

Many applications such as recommender systems (RecSys) are built upon streams of events, each associated with a type in a large-cardinality set and a timestamp in the continuous domain. To date, most applied work is focused on the prediction of the type of the next event, i.e., which exact item a user may visit when they arrive at the RecSys. Instead, we aim to predict when and how often an event of a certain

Background: Deep Learning (DL) has not been well-established as a method to identify high-risk patients among patients with heart failure (HF). Objectives: This study aimed to use DL modeling to predict hospitalizations, worsening HF events, and 30-day and 90-day readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Methods: We analyzed the data of adult HFrEF patients from

Since 2015, Amazon has reduced the weight of its outbound packaging by 36%, eliminating over 1,000,000 tons of packaging material worldwide, or the equivalent of over 2 billion shipping boxes, thereby reducing carbon footprint throughout its fulfillment supply chain. In this position paper, we share insights on using deep learning to identify the optimal packaging type best suited to ship each item in a

Wav2vec-C introduces a novel representation learning technique combining elements from wav2vec 2.0 and VQ-VAE. Our model learns to reproduce quantized representations from partially masked speech encoding using a contrastive loss in a way similar to wav2vec 2.0. However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec

While training highly overparameterized neural networks is common practice in deep learning, research into post-hoc weight-pruning suggests that more than 90% of parameters can be removed without loss in predictive performance. To save resources, zero-shot and one-shot pruning attempt to find such a sparse representation at initialization or at an early stage of training. Though efficient, there is no justification

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSAGAN, a generative adversarial network (GAN) that generates long time series samples of high quality using progressive growing of GANs and self-attention. We show that PSA-GAN can be used to reduce the error in two

Variational inference is a powerful framework, used to approximate intractable posteriors through variational distributions. The de facto standard is to rely on Gaussian variational families, which come with numerous advantages: they are easy to sample from, simple to parametrize, and many expectations are known in closed-form or readily computed by quadrature. In this paper, we view the Gaussian variational

Whilst recent neural text-to-speech (TTS) approaches produce high-quality speech, they typically require a large amount of recordings from the target speaker. In previous work [1], a 3-step method was proposed to generate high-quality TTS while greatly reducing the amount of data required for training. However, we have observed a ceiling effect in the level of naturalness achievable for highly expressive

Intelligent Voice Assistant (IVA) systems, such as Alexa, Google Assistant and Siri, allow us to interact with them using just the voice commands. IVAs can elicit voice feedback directly from the users and use their responses to improve the various components of IVAs. One concern with using such crowdsourced voice feedback (CVF) data is the reliability of feedback itself such as background noise or disingenuous

Automatically evaluating large scale dialogue systems’ response quality is a challenging task in dialogue research. Existing automated turn-level approaches train supervised models on Interaction Quality (IQ) labels or annotations provided by experts, which is costly and time-sensitive. Moreover, the small quantity of annotated data limits the trained model’s ability to generalize to the long tail and out

Quantile regression is an effective technique to quantify uncertainty, and fit challenging underlying distributions. Generating full probabilistic predictions requires multiple quantile regressions over multiple quantile levels. As a result, quantile crossing is a common drawback to these approaches since it violates the desirable monotone property of the conditional quantile function. In this work, we

While recent automatic data augmentation works lead to state-of-the-art results, their design spaces and the derived data augmentation strategies still incorporate strong human priors. In this work, instead of selecting a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fully automated approach for data augmentation search called Deep AutoAugment (DeepAA).