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 10128 publications
Preview abstract
Cloud computing architectures are more scalable and economical which is the main reason that has contributed to its popularity. However, they bring their own set of challenges when it comes to workload scheduling and resource utilization because virtual machines (VM) and applications have to share different types of resources like servers, storage, etc. Historically, other strategies for workload balancing and resource management include manual configuration or simplistic heuristics that do not provide effective optimizations of resource usage and performance. In this technical brief, we propose an approach built on the use of unsupervised learning techniques to detect usage patterns perceptively and improve resource utilization, which corresponds to both optimal performance and automatically balanced workload among VMs. We are making use of clustering algorithms to cluster similar workloads and then resource allocation for each group based on demand. The point of this step is to use the resources more effectively so we do not run into resource exhaustion. We also integrate anomaly detection methods within our system for identifying and handling abnormal behavior by both monitoring and placing resources. We experiment with region traces from production workloads to demonstrate the benefits of our approach, showing marked improvements in workload balancing and resource utilization over current practices.
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Making Images from Images: Tightly Constrained Parallel Denoising
Ashwin Baluja
European Conference on Computer Vision, AI for Visual Arts Workshop and Challenges (2024)
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We present methods to transform an image into a novel one of any subject matter simply by rearranging the image’s tiles. Our method extends and improves recent work in the generation of optical illusions by discovering the optimal arrangement of the image’s tiles simultaneously with the image generation. In addition to producing images that more accurately represent the subject matter, this technique allows us to address a much broader class of problems than previously possible. By learning the image transforms, we allow any source image to be pre- specified; any existing image (e.g. the Mona Lisa) can be transformed to a novel subject. We formulate this as a tightly constrained optimization problem and address it through alternating the steps of image diffusion and energy minimization using optimal matching. Under our formulation, a simple method to extend this to infinite copies of the source image is also given. Unlike previous methods, as the number of tiles grows the problem becomes easier and the results become better.
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This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
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We present a new task and dataset, ScreenQA, for screen content understanding via question answering. The existing screen datasets are focused either on structure and component-level understanding, or on a much higher-level composite task such as navigation and task completion. We attempt to bridge the gap between these two by annotating 86K question-answer pairs over the RICO dataset in hope to benchmark the screen reading comprehension capacity.
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Experiencing Thing2Reality: Transforming 2D Content into Conditioned Multiviews and 3D Gaussian Objects for XR Communication
Erzhen Hu
Mingyi Li
Seongkook Heo
Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, ACM (2024)
Preview abstract
During remote communication, participants share both digital and physical content, such as product designs, digital assets, and environments, to enhance mutual understanding. Recent advances in augmented communication have facilitated users to swiftly create and share digital 2D copies of physical objects from video feeds into a shared space. However, the conventional 2D representation of digital objects restricts users’ ability to spatially reference items in a shared immersive environment. To address these challenges, we propose Thing2Reality, an Extended Reality (XR) communication platform designed to enhance spontaneous discussions regard-ing both digital and physical items during remote sessions. WithThing2Reality, users can quickly materialize ideas or physical objects in immersive environments and share them as conditioned multiview renderings or 3D Gaussians. Our system enables users to interact with remote objects or discuss concepts in a collaborative manner.
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DynaMITE-RL: A Dynamic Model for Improved Temporal Meta Reinforcement Learning
Anthony Liang
Erdem Biyik
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
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We introduce a meta-reinforcement learning (meta-RL) approach, called DynaMITE-RL, to perform approximate inference in environments where the latent information evolves slowly between subtrajectories called sessions.
We identify three key modifications to contemporary meta-RL methods: consistency of latent information during sessions, session masking, and prior latent conditioning.
We demonstrate the necessity of these modifications on various downstream applications from discrete Gridworld environments to continuous control and simulated robot assistive tasks and find that our approach significantly outperforms contemporary baselines.
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PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks
Marina Neseem
Conor McCullough
Randy Hsin
Chas Leichner
Shan Li
In Suk Chong
Andrew Howard
Lukasz Lew
Sherief Reda
Ville-Mikko Rautio
Daniele Moro
Conference on Computer Vision and Pattern Recognition (2024) (to appear)
Preview abstract
Low-precision quantization is recognized for its efficacy in neural network optimization. Our analysis reveals that non-quantized elementwise operations which are prevalent in layers such as parameterized activation functions, batch normalization, and quantization scaling dominate the inference cost of low-precision models. These non-quantized elementwise operations are commonly overlooked in SOTA efficiency metrics such as Arithmetic Computation Effort (ACE). In this paper, we propose ACEv2 - an extended version of ACE which offers a better alignment with the inference cost of quantized models and their energy consumption on ML hardware. Moreover, we introduce PikeLPN, a model that addresses these efficiency issues by applying quantization to both elementwise operations and multiply-accumulate operations. In particular, we present a novel quantization technique for batch normalization layers named QuantNorm which allows for quantizing the batch normalization parameters without compromising the model performance. Additionally, we propose applying Double Quantization where the quantization scaling parameters are quantized. Furthermore, we recognize and resolve the issue of distribution mismatch in Separable Convolution layers by introducing Distribution-Heterogeneous Quantization which enables quantizing them to low-precision. PikeLPN achieves Pareto-optimality in efficiency-accuracy trade-off with up to 3X efficiency improvement compared to SOTA low-precision models.
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Computational Methodologies for Understanding, Automating, and Evaluating User Interfaces
Yuwen Lu
Yue Jiang
Christof Lutteroth
Toby Jia-Jun Li
Jeffery Nichols
Wolfgang Stuerzlinger
Preview abstract
Building on the success of the first two workshops on user interfaces (UIs) at CHI 2022 and CHI 2023, this workshop aims to advance the research field by further exploring current research trends, such as applying large language models and visual language models. Previous work has explored computational approaches to understanding and adapting UIs using constraint-based optimization models and machine learning-based data-driven approaches. In addition to further delving into these established UI research areas, we aim to trigger the exploration into the application of the latest advancements in general-purpose large language and vision-language models within the UI domain. We will encourage participants to explore novel methods for understanding, automating, and evaluating UIs. The proposed workshop seeks to bring together academic researchers and industry practitioners interested in computational approaches for UIs to discuss the needs and opportunities for future user interface algorithms, models, and applications.
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Historically, much of machine learning research has focused on the performance of the algorithm alone, but recently more attention has been focused on optimizing joint human-algorithm performance. Here, we analyze a specific type of human-algorithm collaboration where the algorithm has access to a set of $n$ items, and presents a subset of size $k$ to the human, who selects a final item from among those $k$. This scenario could model content recommendation, route planning, or any type of labeling task. Because both the human and algorithm have imperfect, noisy information about the true ordering of items, the key question is: which value of $k$ maximizes the probability that the best item will be ultimately selected? For $k=1$, performance is optimized by the algorithm acting alone, and for $k=n$ it is optimized by the human acting alone.
Surprisingly, we show that for multiple of noise models, it is optimal to set $k \in [2, n-1]$ - that is, there are strict benefits to collaborating, even when the human and algorithm have equal accuracy separately. We demonstrate this theoretically for the Mallows model and experimentally for the Random Utilities models of noisy permutations. However, we show this pattern is \emph{reversed} when the human is anchored on the algorithm's presented ordering - the joint system always has strictly worse performance. We extend these results to the case where the human and algorithm differ in their accuracy levels, showing that there always exist regimes where a more accurate agent would strictly benefit from collaborating with a less accurate one, but these regimes are asymmetric between the human and the algorithm's accuracy.
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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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AI-assisted Assessment of Coding Practices in Industrial Code Review
Ivan Budiselic
Malgorzata (Gosia) Salawa
Juanjo Carin
Jovan Andonov
Mateusz Lewko
Marko Ivanković
Rene Just
Preview abstract
Modern code review is a process in which incremental code contributions made by one software developer are reviewed by one or more peers before it is committed to the version control system. An important element of modern code review is verifying that the code under review adheres to style guidelines and best practices of the corresponding programming language. Some of these rules are universal and can be checked automatically or enforced via code formatters. Other rules, however, are context-dependent and the corresponding checks are commonly left to developers who are experts in the given programming language and whose time is expensive. Many automated systems have been developed that attempt to detect various rule violations without any human intervention. Historically, such systems implement targeted analyses and were themselves expensive to develop. This paper presents AutoCommenter, a system that uses a state of the art large language model to automatically learn and enforce programming language best practices. We implemented AutoCommenter for four programming languages: C++, Java, Python and Go. We evaluated its performance and adoption in a large industrial setting. Our evaluation shows that a model that automatically learns language best practices is feasible and has a measurable positive impact on the developer workflow. Additionally, we present the challenges we faced when deploying such a model to tens of thousands of developers and provide lessons we learned for any practitioners that would like to replicate the work or build on top of it.
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Large Language Models as a Proxy For Human Evaluation in Assessing the Comprehensibility of Disordered Speech Transcription
Richard Cave
Katie Seaver
Jordan Green
Rus Heywood
Proceedings of ICASSP, IEEE (2024)
Preview abstract
Automatic Speech Recognition (ASR) systems, despite significant advances in recent years, still have much room for improvement particularly in the recognition of disordered speech. Even so, erroneous transcripts from ASR models can help people with disordered speech be better understood, especially if the transcription doesn’t significantly change the intended meaning. Evaluating the efficacy of ASR for this use case requires a methodology for measuring the impact of transcription errors on the intended meaning and comprehensibility. Human evaluation is the gold standard for this, but it can be laborious, slow, and expensive. In this work, we tune and evaluate large language models for this task and find them to be a much better proxy for human evaluators than other metrics commonly used. We further present a case-study using the presented approach to assess the quality of personalized ASR models to make model deployment decisions and correctly set user expectations for model quality as part of our trusted tester program.
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PRISM: A New Lens for Improved Color Understanding
Garima Pruthi
Inderjit Dhillon
Varun Jampani
EMNLP (2024)
Preview abstract
While image-text pre-trained models, such as CLIP, have demonstrated impressive capabilities in learning robust text and image representations, a critical area for substantial improvement remains—precise color understanding. In this paper, we address this limitation by introducing PRISM, a simple yet highly effective method that extends CLIP's capability to grasp the nuances of precise colors. PRISM seamlessly adapts to both recognized HTML colors and out-of-vocabulary RGB inputs through the utilization of our curated dataset of 100 image-text pairs, which can be effortlessly repurposed for fine-tuning with any desired color. Importantly, PRISM achieves these enhancements without compromising CLIP's performance on established benchmarks. During the fine-tuning process, PRISM encourages the disentanglement of color-relevant information from color-irrelevant details. Furthermore, we introduce a novel evaluation framework, ColorLens, featuring both seen and unseen test sets that can be readily repurposed to assess a model's precision in understanding precise colors. Our comprehensive evaluation and results demonstrate significant improvements over baseline models.
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Recent efforts to address hallucinations in Large Language Models (LLMs) have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. Yet, these citations often point to entire documents or paragraphs, burdening users with extensive verification work. In this paper, we introduce a locally-attributable text generation approach, prioritizing concise attributions. Our method, named ``Attribute First, then Generate'', breaks down the conventional end-to-end generation process into three intuitive steps: content selection, sentence planning, and sequential sentence generation. By initially identifying relevant source segments (``select first'') and then conditioning the generation process on them (``then generate''), we ensure these segments also act as the output's fine-grained attributions (``select'' becomes ``attribute''). Tested on Multi-document Summarization and Long-form Question-answering, our method not only yields more concise citations than the baselines but also maintains - and in some cases enhances - both generation quality and attribution accuracy. Furthermore, it significantly reduces the time required for fact verification by human assessors.
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ASTRA-5G: Automated Over-the-Air Security Testing and Research Architecture for 5G SA Devices
Aanjhan Ranganathan
Christina Pöpper
Evangelos Bitsikas
Michele Guerra
Roger Piqueras Jover
Syed Khandker
WiSec '24: Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks, ACM (2024)
Preview abstract
Despite the widespread deployment of 5G technologies, there exists a critical gap in security testing for 5G Standalone (SA) devices. Existing methods, largely manual and labor-intensive, are ill-equipped to fully uncover the state of security in the implementations of 5G-SA protocols and standards on devices, severely limiting the ability to conduct comprehensive evaluations. To address this issue, in this work, we introduce an novel, open-source framework that auto-
mates the security testing process for 5G SA devices. By leveraging enhanced functionalities of 5G SA core and Radio Access Network (RAN) software, our framework offers a streamlined approach to generating, executing, and evaluating test cases, specifically focusing on the Non-Access Stratum (NAS) layer. Our application of this framework across multiple 5G SA devices provides in-depth security insights, significantly improving testing efficiency and breadth.
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