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 10100 publications
Visual Program Tuning: Training Large Multimodal Models to Reason like Programs
Yushi Hu
Krishna Viswanathan
Kenji Hata
Enming Luo
Ranjay Krishna
Ariel Fuxman
Conference on Computer Vision and Pattern Recognition (2024)
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Solving complex visual tasks (e.g., “Who invented the musical instrument on the right?”) involves back-and-forth between visual processing and reasoning. Visual programming is a recent multimodal framework that has shown promise in conducting visual reasoning in an interpretable and compositional manner. However, this framework is error-prone—it can lead to a wrong answer whenever the program itself is wrong, or when any of the steps of the program are solved incorrectly, thus leading to worse overall performance than end-to-end systems trained with labeled data. Moreover, it is inefficient to involve multiple steps (i.e., generating and then running programs) during inference. Ideally, a single large multimodal model (LMM) should directly conduct similar reasoning and yield the correct answer.
In this work, we propose Visual Program Tuning (VPT), which leverages visual programs for teaching LLMs to reason via instruction tuning. VPT rewrites the execution traces of visual programs as chain-of-thought reasoning steps, and tunes an LMM to output not only the label but its reasoning as well. Extensive experiments on complex vision tasks show that models trained with VPT achieve state-of-the-art accuracy while being able to produce interpretable and faithful reasoning steps. PaLI-X + VPT outperforms all existing LMMs on a wide range of visual tasks, improving performance on counting, spatial relations, and compositional reasoning tasks. VPT is also helpful for quick adaptation on new tasks. Our experiments on content moderation show that fine-tuning LMMs with program-augmented examples is more sample efficient than traditional supervised training.
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This paper discusses a method to inject text when training an ASR system without the need for up sampling the text sequence to match the length of the speech sequence.
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Optimizing quantum gates towards the scale of logical qubits
Alexandre Bourassa
Andrew Dunsworth
Will Livingston
Vlad Sivak
Trond Andersen
Yaxing Zhang
Desmond Chik
Jimmy Chen
Charles Neill
Alejo Grajales Dau
Anthony Megrant
Alexander Korotkov
Vadim Smelyanskiy
Yu Chen
Nature Communications, 15 (2024), pp. 2442
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A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.
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Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines
Aditya U Kale
Alastair Dennison
Alexander Martindale
An Wen Chan
Andrew Beam
Benjamin Ng
Cecilia S. Lee
Christopher Yau
David Moher
Gary Collins
Lauren Oakden-Rayner
Lavinia Ferrante di Ruffano
Melanie Calvert
Melissa D McCradden
Pearse Keane
Robert Golub
Samantha Cruz Rivera
Victoria Ngai
Xiaoxuan Liu
Nature Communications (2024)
Preview abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Optimization by Decoded Quantum Interferometry
Stephen Jordan
Noah Shutty
Mary Wootters
Alexander Schmidhuber
Robbie King
Sergei Isakov
arXiv:2408.08292 (2024)
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We introduce Decoded Quantum Interferometry (DQI), a quantum algorithm for reducing classical optimization problems to classical decoding problems by exploiting structure in the Fourier spectrum of the objective function. DQI reduces sparse max-XORSAT to decoding LDPC codes, which can be decoded using powerful classical algorithms such as belief propagation. As an initial benchmark, we compare DQI using belief propagation decoding against classical optimization via simulated annealing. In this setting we identify a family of max-XORSAT instances where DQI achieves a better approximation ratio on average than simulated annealing, although not better than specialized classical algorithms tailored to those instances. We also analyze a combinatorial optimization problem corresponding to finding polynomials that intersect the maximum number of points. There, DQI efficiently achieves a better approximation ratio than any polynomial-time classical algorithm known to us, thus realizing an apparent exponential quantum speedup. Finally, we show that the problem defined by Yamakawa and Zhandry in order to prove an exponential separation between quantum and classical query complexity is a special case of the optimization problem efficiently solved by DQI.
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ConSmax: Hardware-Friendly Alternative Softmax with Learnable Parameters
Shiwei Liu
Guanchen Tao
Yifei Zou
Derek Chow
Zichen Fan
Kauna Lei
Bangfei Pan
Dennis Sylvester
Mehdi Saligane
Arxiv (2024)
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The self-attention mechanism sets transformer-based large language model (LLM) apart from the convolutional and recurrent neural networks. Despite the performance improvement, achieving real-time LLM inference on silicon is challenging due to the extensively used Softmax in self-attention. Apart from the non-linearity, the low arithmetic intensity greatly reduces the processing parallelism, which becomes the bottleneck especially when dealing with a longer context. To address this challenge, we propose Constant Softmax (ConSmax), a software-hardware co-design as an efficient Softmax alternative. ConSmax employs differentiable normalization parameters to remove the maximum searching and denominator summation in Softmax. It allows for massive parallelization while performing the critical tasks of Softmax. In addition, a scalable ConSmax hardware utilizing a bitwidth-split look-up table (LUT) can produce lossless non-linear operation and support mix-precision computing. It further facilitates efficient LLM inference. Experimental results show that ConSmax achieves a minuscule power consumption of 0.2 mW and area of 0.0008 mm^2 at 1250-MHz working frequency and 16-nm CMOS technology. Compared to state-of-the-art Softmax hardware, ConSmax results in 3.35x power and 2.75x area savings with a comparable accuracy on a GPT-2 model and the WikiText103 dataset.
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Understanding metric-related pitfalls in image analysis validation
Annika Reinke
Lena Maier-Hein
Paul Jager
Shravya Shetty
Understanding Metrics Workgroup
Nature Methods (2024)
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Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.
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ChatDirector: Enhancing Video Conferencing with Space-Aware Scene Rendering and Speech-Driven Layout Transition
Brian Moreno Collins
Karthik Ramani
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 16 (to appear)
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Remote video conferencing systems (RVCS) are widely adopted in personal and professional communication. However, they often lack the co-presence experience of in-person meetings. This is largely due to the absence of intuitive visual cues and clear spatial relationships among remote participants, which can lead to speech interruptions and loss of attention. This paper presents ChatDirector, a novel RVCS that overcomes these limitations by incorporating space-aware visual presence and speech-aware attention transition assistance. ChatDirector employs a real-time pipeline that converts participants' RGB video streams into 3D portrait avatars and renders them in a virtual 3D scene. We also contribute a decision tree algorithm that directs the avatar layouts and behaviors based on participants' speech states. We report on results from a user study (N=16) where we evaluated ChatDirector. The satisfactory algorithm performance and complimentary subject user feedback imply that ChatDirector significantly enhances communication efficacy and user engagement.
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Scalable Learning of Segment-Level Traffic Congestion Functions
Shushman Choudhury
Aboudy Kreidieh
Alexandre Bayen
IEEE Intelligent Transportation Systems Conference (2024)
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We propose and study a data-driven framework for identifying traffic congestion functions (numerical relationships between observations of traffic variables) at global scale and segment-level granularity. In contrast to methods that estimate a separate set of parameters for each roadway, ours learns a single black-box function over all roadways in a metropolitan area. First, we pool traffic data from all segments into one dataset, combining static attributes with dynamic time-dependent features. Second, we train a feed-forward neural network on this dataset, which we can then use on any segment in the area. We evaluate how well our framework identifies congestion functions on observed segments and how it generalizes to unobserved segments and predicts segment attributes on a large dataset covering multiple cities worldwide. For identification error on observed segments, our single data-driven congestion function compares favorably to segment-specific model-based functions on highway roads, but has room to improve on arterial roads. For generalization, our approach shows strong performance across cities and road types: both on unobserved segments in the same city and on zero-shot transfer learning between cities. Finally, for predicting segment attributes, we find that our approach can approximate critical densities for individual segments using their static properties.
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Multimodal Modeling for Spoken Language Identification
Shikhar Bharadwaj
Sriram (Sri) Ganapathy
Sid Dalmia
Wei Han
Yu Zhang
Proceedings of 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024) (2024)
Preview abstract
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance. Conventionally, it is modeled as a speech-based language identification task. Prior techniques have been constrained to a single modality; however in the case of video data there is a wealth of other metadata that may be beneficial for this task. In this work, we propose MuSeLI, a Multimodal Spoken Language Identification method, which delves into the use of various metadata sources to enhance language identification. Our study reveals that metadata such as video title, description and geographic location provide substantial information to identify the spoken language of the multimedia recording. We conduct experiments using two diverse public datasets of YouTube videos, and obtain state-of-the-art results on the language identification task. We additionally conduct an ablation study that describes the distinct contribution of each modality for language recognition.
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Knowledge-grounded dialogue generation is a challenging task because it requires satisfying two fundamental yet often competing constraints: being responsive in a manner that is specific to what the conversation partner has said while also being attributable to an underlying source document. In this work, we bring this trade-off between these two objectives (specificity and attribution) to light and ask the question: Can explicit content planning before the response generation help the model to address this challenge? To answer this question, we design a framework called PLEDGE, which allows us to experiment with various plan variables explored in prior work, supporting both metric-agnostic and metric-aware approaches. While content planning shows promise, our results on whether it can actually help to navigate this trade-off are mixed -- planning mechanisms that are metric-aware (use automatic metrics during training) are better at automatic evaluations but underperform in human judgment compared to metric-agnostic mechanisms. We discuss how this may be caused by over-fitting to automatic metrics and the need for future work to better calibrate these metrics towards human judgment. We hope the observations from our analysis will inform future work that aims to apply content planning in this context.
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Community search signatures as foundation features for human-centered geospatial modeling
Chaitanya Kamath
Mohit Agarwal
Arbaaz Muslim
David Schottlander
Shailesh Bavadekar
Niv Efron
Shravya Shetty
ICML 2024 Workshop on Data-Centric Machine Learning Research
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Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found in other readily available datasets. Temporal search trends have been successfully used to perform nowcasting across a variety of domains such as infectious diseases, unemployment rates, and retail sales. However, most existing applications require curating specialized datasets of individual keywords, queries, or query clusters, and the search data need to be temporally aligned with the outcome variable of interest. We propose a novel approach for generating an aggregated and anonymized representation of search interest as foundation features at the community level for geospatial modeling. We benchmark these features using spatial datasets across multiple domains. In regions with a population greater than 3000 that cover over 95% of the contiguous US population, our models achieve an average R-squared score of 0.74 across 21 health variables, and 0.80 across 6 demographic and environmental variables. Our results demonstrate that these search features can be used for spatial predictions without strict temporal alignment, and that the resulting models outperform spatial interpolation and state of the art methods using satellite imagery features.
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API Governance at Scale
Mak Ahmad
JJ Geewax
David R Karger
Kwan-Liu Ma
ICSE 2024 Software Engineering in Practice (2024)
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API Governance, the process of applying standardized sets of policies and guardrails to the design and development of APIs, has only grown in importance and prominence given the continued growth in APIs being produced. In this paper, we present an Action Research style approach to investigate and understand the utility of a multi-faceted API Governance process being adopted inside Google. We first reflect on past research around API Governance, and then introduce three new components, 1. API Improvement Proposals (AIPs) the documented source of truth for API design rules, 2. API Linter, an automated analysis tool which checks for adherence to / violations of AIPs, and 3. API Readability, a program to educate and certify API design experts. These three components are designed to build upon pre-existing processes to scale and improve API design. Through a mixed-methods research strategy, containing both a survey and a series of interviews, we evaluate the utility of these approaches in supporting API Producers. Our research shows that API Producers have positive sentiment towards API Governance, validating the general direction of the program. Specifically, our study participants highlighted the positive impact of API Governance on the quality of the APIs they produced, via consistency in both the outcome and approach. This paper also discusses future research opportunities to enhance API Governance, specifically with regards to newer API Producers, who reported worse sentiment towards the program than their more experienced peers.
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Model-Free Preference Elicitation
Carlos Martin
Tuomas Sandholm
Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, South Korea (2024), pp. 3493-3503
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Elicitation of user preferences is becoming an important approach for improving the qualityof recommendations, especially when there is little or no user history. In this setting, arecommender system interacts with the user by iteratively presenting elicitation questionsand recording their responses. Various criteria have been proposed for optimizing thesequence of queries in order to improve user understanding and thereby the quality ofdownstream recommendations. A compelling approach for preference elicitation is theExpected Value of Information (EVOI), a Bayesian approach which computes the expectedgain in user utility for possible queries. Previous work on EVOI has focused on probabilisticmodels of users for computing posterior utilities. In contrast, in this work we exploremodel-free variants of EVOI which rely on function approximations in order to avoid strongmodeling assumptions. Specifically, we propose to learn a user response model and a userutility model from data which is often available in real-world systems, and to use thesemodels in EVOI in place of the probabilistic models. We show that our approach leads toimproved elicitation performance.
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An intentional approach to managing bias in embedding models
Atilla P. Kiraly
Jungyeon Park
Rory Pilgrim
Charles Lau
Heather Cole-Lewis
Shravya Shetty
Krish Eswaran
Leo Anthony Celi
The Lancet Digital Health, 6 (2024), E126-E130
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Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components—GPPEs—from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended.
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