Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10129 publications
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Motivated by the necessity of guiding and monitoring students' progress in real-time when assembling circuits during in-class activities we propose BlinkBoard, an augmented breadboard to enhance offline as well as online physical computing classes. BlinkBoard uses LEDs placed on each row of the breadboard to guide, via four blinking patterns, how to place and connect components and wires. It also uses a set of Input/Output pins to sense voltage levels at user-specified rows or to generate voltage output. Our hardware uses an open JSON protocol of commands and responses that can be integrated with a graphical application hosted on a computer that ensures bidirectional communication between each of the students' BreadBoard and the instructor's dashboard and slides. The hardware is affordable and simple, partially due to a customized circuit configured via a hardware description language that handles the LEDs' patterns with minimal load on the Arduino micro-controller. Finally, we briefly show how this hardware made its way to a workshop with high-school students and an undergraduate class in a design department.
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Rethinking FID: Towards a Better Evaluation Metric for Image Generation
Sadeep Jayasumana
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2024)
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As with many machine learning problems, the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images, and those of images generated by the algorithm. We highlight important drawbacks of FID: Inception's poor representation of the rich and varied content generated by modern text-to-image models, incorrect normality assumptions, and poor sample complexity. We call for a reevaluation of FID's use as the primary quality metric for generated images. We empirically demonstrate that FID contradicts human raters, it does not reflect gradual improvement of iterative text-to-image models, it does not capture distortion levels, and that it produces inconsistent results when varying the sample size. We also propose an alternative new metric, CMMD, based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis, we demonstrate that FID-based evaluations of text-to-image models may be unreliable, and that CMMD offers a more robust and reliable assessment of image quality.
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Inter-sentence pauses are the silences that occur between sentences in a paragraph or a dialogue.
They are an important aspect of long-form speech prosody, as they can affect the naturalness, intelligibility, and effectiveness of communication.
However, the user perception of inter-sentence pauses in long-form speech synthesis is not well understood. Previous work often evaluates pause modelling in conjunction with other prosodic features making it hard to explicitly study how raters perceive differences in inter-sentence pause lengths.
In this paper, using multiple text-to-speech (TTS) datasets that cover different content types, domains, and settings, we investigate how sensitive raters are to changes to the durations of inter-sentence pauses in long-form speech by comparing ground truth audio samples with renditions that have manipulated pause durations.
This experimental design is meant to allow us to draw conclusions regarding the utility that can be expected from similar evaluations when applied to synthesized long-form speech.
We find that, using standard evaluation methodologies, raters are not sensitive to variations in pause lengths unless these deviate exceedingly from the norms or expectations of the speech context.
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Demystifying Embedding Spaces using Large Language Models
Jihwan Jeong
Lior Shani
Martin Mladenov
The Twelfth International Conference on Learning Representations (2024)
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Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing large language models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs.
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Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.
Observations: While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.
Conclusions and Relevance: Foundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.
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"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu
Frederick Liu
Alex Fiannaca
Terry Koo
In Extended Abstract in ACM CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM (2024), pp. 9 (to appear)
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Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool.
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Neural general circulation models for weather and climate
Dmitrii Kochkov
Janni Yuval
Jamie Smith
Griffin Mooers
Milan Kloewer
James Lottes
Peter Dueben
Samuel Hatfield
Peter Battaglia
Alvaro Sanchez
Matthew Willson
Nature, 632 (2024), pp. 1060-1066
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General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.
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Open Se Cura: First Silicon Results of an Auditable and Transparent Hardware Root of Trust System using Open EDA in 16-nm
Guanchen Tao
Ming-Hung Chen
Bangfei Pan
Kai Yick
Dennis Sylvester
Mehdi Saligane
IEEE Solid-State Circuits Magazine, 16(2024), pp. 58-66
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Hardware Root of Trust (HRoT) is essential for Internet-of-Things (IoT) devices as it provides critical user data protection. However, each novel use case significantly lengthens the development time for an HRoT system. Furthermore, most HRoT solutions are proprietary, and users lack permission to inspect and audit such systems [1-2]. This paper introduces Open Se Cura, which is an open-source framework designed to expedite the implementation of secure and transparent HRoT systems. It utilizes open-source Electronic Design Automation (EDA) tools like OpenROAD [3-4] and OpenFASOC [5-6], along with open-source Process Design Kits (PDKs), to present a transparent and auditable approach to hardware-software co-design platforms. This approach enables fast and trustworthy HRoT system implementation and is made openly available to reproduce its results and security efficacy [7]. Our reference design is showcased through FPGA emulation, and the first measurement results of a silicon implementation in 16nm of Open Se Cura security domain subsets integrated using open-source EDA are presented.
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Practical Performance Guarantees for Pipelined DNN Inference
Kuikui Liu
Proceedings of the 41st International Conference on Machine Learning (2024), pp. 1655-1671
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This work optimizes pipeline parallelism of machine learning model inference by
partitioning computation graphs into $k$ stages and minimizing the running time of the bottleneck stage.
We design practical algorithms for this NP-complete problem
and prove they are nearly optimal in practice by comparing against lower bounds
obtained from solving novel mixed-integer programming (MIP) formulations.
We apply these algorithms and lower-bound techniques
to production models to achieve substantial improvements in the approximation guarantees,
compared to simple combinatorial lower bounds.
For example, our new MIP formulations improve the lower bounds enough to
drop the geometric mean approximation ratio from $2.175$ to $1.082$ across
production data with $k=16$ pipeline stages.
This work shows that while bottleneck partitioning is theoretically hard,
in practice we have a handle on the algorithmic side of the problem and
much of the remaining challenge is in developing more accurate cost models
to give to the partitioning algorithms.
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Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization
Advances in Neural Information Processing Systems (NeurIPS) (2024) (to appear)
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Large language models have demonstrated remarkable capabilities, but their performance is heavily reliant on effective prompt engineering. Automatic prompt optimization (APO) methods are designed to automate this and can be broadly categorized into those targeting instructions (instruction optimization, IO) vs. those targeting exemplars (exemplar selection, ES). Despite their shared objective, these have evolved rather independently, with IO recently receiving more research attention. This paper seeks to bridge this gap by comprehensively comparing the performance of representative IO and ES techniques, both isolation and combination, on a diverse set of challenging tasks. Our findings reveal that intelligently reusing model-generated input-output pairs obtained from evaluating prompts on the validation set as exemplars consistently improves performance over IO methods but is currently under-investigated. We also find that despite the recent focus on IO, how we select exemplars can outweigh how we optimize instructions, with ES strategies as simple as random search outperforming state-of-the-art IO methods with seed instructions without any optimization. Moreover, we observe synergy between ES and IO, with optimal combinations surpassing individual contributions. We conclude that studying exemplar selection as a standalone method and its optimal combination with instruction optimization remains a crucial aspect of APO and deserves greater consideration in future research, even in the era of highly capable instruction-following models.
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Referring Image Segmentation is a comprehensive task to segment an object referred by a textual query from an image. In nature, the level of difficulty in this task is affected by the existence of similar objects and the complexity of the referring expression. Recent RIS models still show a significant performance gap between easy and hard scenarios. We pose that the bottleneck exists in the data, and propose a simple but powerful data augmentation method, Negative-mined Mosaic Augmentation (NeMo). This method augments a training image into a mosaic with three other negative images carefully curated by a pretrained multimodal alignment model, e.g., CLIP, to make the sample more challenging. We discover that it is critical to properly adjust the difficulty level, neither too ambiguous nor too trivial. The augmented training data encourages the RIS model to recognize subtle differences and relationships between similar visual entities and to concretely understand the whole expression to locate the right target better. Our approach shows consistent improvements on various datasets and models, verified by extensive experiments.
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Automatic Histograms: Leveraging Language Models for Text Dataset Exploration
Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM, Honolulu, HI, USA (2024), pp. 9
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Making sense of unstructured text datasets is perennially difficult, yet increasingly relevant with Large Language Models. Data practitioners often rely on dataset summaries, especially distributions of various derived features. Some features, like toxicity or topics, are relevant to many datasets, but many interesting features are domain specific, e.g., instruments and genres for a music dataset, or diseases and symptoms for a medical dataset. Accordingly, data practitioners often run custom analyses for each dataset, which is cumbersome and difficult, or use unsupervised methods. We present AutoHistograms, a visualization tool leveraging LLMs. AutoHistograms automatically identifies relevant entity-based features, visualizes their distributions, and allows the user to interactively query the dataset for new categories of entities. In a user study with (n=10) data practitioners, we observe that participants were able to quickly onboard to AutoHistograms, use the tool to identify actionable insights, and conceptualize a broad range of applicable use cases. We also describe a variety of usage scenarios from different types of users to highlight how this app can provide value in many different contexts. Finally, we present a quantitative evaluation of the tool. Together, this tool and user study contribute to the growing field of LLM-assisted sensemaking tools.
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Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data
Michael T Young
Swapnil Vispute
Stylianos Serghiou
Akim Kumok
Yash Shah
Kevin J. Lane
Flannery Black-Ingersoll
Paige Brochu
Monica Bharel
Sarah Skenazy
Shailesh Bavadekar
Mansi Kansal
Evgeniy Gabrilovich
Gregory A. Wellenius
Lancet Planetary Health (2024)
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Summary
Background A large body of evidence connects access to greenspace with substantial benefits to physical and mental
health. In urban settings where access to greenspace can be limited, park access and use have been associated with
higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the
potential health benefits of urban parks, little is known about how park usage varies across locations (between or
within cities) or over time.
Methods We estimated park usage among urban residents (identified as residents of urban census tracts) in
498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We
used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts
within cities, and used generalised linear models to estimate the associations between park usage and census tract
level descriptors.
Findings In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with
average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially
both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income,
and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In
spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6–14·3) lower average
weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring
2019 to spring 2020 was 38·0% (IQR 28·4–46·5), coincident with the onset of physical distancing policies across
much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those
living further from a park and those living in areas of higher social vulnerability.
Interpretation These estimates provide novel insights into the patterns and correlates of park use and could enable
new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric
that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such
activities.
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Auto-bidding and Auctions in Online Advertising: A Survey
Ashwinkumar Badanidiyuru Varadaraja
Christopher Liaw
Haihao (Sean) Lu
Andres Perlroth
Georgios Piliouras
Ariel Schvartzman
Kelly Spendlove
Hanrui Zhang
Mingfei Zhao
ACM SIGecom Exchanges, 22 (2024)
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In this survey, we summarize recent developments in research fueled by the growing adoption of automated bidding strategies in online advertising. We explore the challenges and opportunities that have arisen as markets embrace this autobidding and cover a range of topics in this area, including bidding algorithms, equilibrium analysis and efficiency of common auction formats, and optimal auction design.
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Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming
Zhongyi Zhou
Jing Jin
Xiuxiu Yuan
Jun Jiang
Jingtao Zhou
Yiyi Huang
Kristen Wright
Jason Mayes
Mark Sherwood
Ram Iyengar
Na Li
Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 5
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Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation.
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