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Learn about the science behind the brand-new NHL EDGE IQ stat that debuted in April 2023.

Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being overly reductive. For instance, epidemiological forecasts, cost estimates, and revenue predictions all benefit from being able to quantify the range of possible values accurately. As such, many models have been developed for this

Information explosion has brought us a wide range of data formats and machine learning keeps in constant evolution to develop mechanisms to extract knowledge from them. Modern models in the Deep Learning space have proven to be very successful in multiple applications, yet in the tabular space they fail to provide consistent competitive performance. However, in this work we claim model selection can become

Automatic song writing is a topic of significant practical interest. However, its research is largely hindered by the lack of training data due to copyright concerns and challenged by its creative nature. Most noticeably, prior works often fall short of modeling the cross-modal correlation between melody and lyrics due to limited parallel data, hence generating lyrics that are less singable. Existing works

Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually annotating 3D data for supervised learning is time-consuming. In this work, we explore zero-shot instance segmentation

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In Brown et al. [PRX Quantum, TBA, TBA (2023)], we discuss how holographic quantum gravity may be simulated using quantum devices and we give a specific proposal—teleportation by size and the phenomenon of size winding. Here, we elaborate on what it means to do quantum gravity in the lab and how size winding connects to bulk gravitational physics and traversable wormholes. Perfect size winding is a remarkable

With the long-term goal of studying models of quantum gravity in the lab, we propose holographic teleportation protocols that can be readily executed in table-top experiments. These protocols exhibit similar behavior to that seen in the recent traversable-wormhole constructions of Gao et al. [J. High Energy Phys., 2017, 151 (2017)] and Maldacena et al. [Fortschr. Phys., 65, 1700034 (2017)]: information

Coherent transduction of quantum states from the microwave to the optical domain can play a key role in quantum networking and distributed quantum computing. We present the design of a piezo-optomechanical device formed in a hybrid lithium niobate on silicon platform, that is suitable for microwave-to-optical quantum transduction. Our design is based on acoustic hybridization of an ultra-low mode volume

Applied Scientist

In a plenary talk, the Berkeley professor and Distinguished Amazon Scholar will argue that AI research should borrow concepts from economics and focus on social collectives.

Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small label spaces and ignored eXtreme Multilabel Classification (XMC), which is an essential task in the era of big data for web-scale machine learning applications. Moreover, enormous label spaces

This paper proposes a new Electromagnetic BandGap (EBG) Structure based filter design. This on-PCB filter imitates the functionality of a discrete low-pass filter without any of the associated Bill of Materials (BOM) costs. The methodology of constructing the EBG filter is explained in detail, making use of classical filter theory including tapering and multiple elements that enable tradeoffs between roll-off

Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be warranted in practice. In this work, we propose to leverage

We present a new approach for synthesizing novel views of people in new poses. Our novel differentiable renderer enables the synthesis of highly realistic images from any viewpoint. Rather than operating over mesh-based structures, our renderer makes use of diffuse Gaussian primitives that directly represent the underlying skeletal structure of a human. Rendering these primitives gives results in a high-dimensional

Learning generative object models from unlabelled videos is a long standing problem relevant for causal scene modeling. We decompose this task into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained

This paper demonstrates how to discover the whole causal graph from the second derivative of the log-likelihood in observational nonlinear additive Gaussian noise models. Leveraging scalable machine learning approaches to approximate the score function ∇ log p(X), we extend the work of Rolland et al. (2022) that only recovers the topological order from the score and requires an expensive pruning step removing

Conventional de-sense mitigation techniques mainly tackle aggressor and coupling path to the antenna. However, very little work has been done to realize antennas with minimal reverse fields. This paper highlights a novel noise immune antenna design that can meet wireless specifications while being immune to noise leading to cost reduction by effectively removing shield can.