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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In-DRAM Stochastic and Approximate Counting (DSAC) is a recently published algorithm that aims to mitigate Rowhammer at low cost. Existing in-DRAM counter-based schemes keep track of row activations and issue Targeted Row Refresh (TRR) upon detecting a concerning pattern. However, due to insufficiency of the tracking ability they are vulnerable to attacks utilizing decoy rows. DSAC claims to improve upon existing TRR mitigation by filtering out decoy-row accesses, so they cannot saturate the limited number of counters available for detecting Rowhammer, promising a reliable mitigation without the area cost of deterministic and provable schemes such as per-row activation counting (PRAC).
In this paper, we analyze DSAC and discover some gaps that make it vulnerable to Rowhammer and Rowpress attacks. The main focus of this work is a novel attack named SoothSayer that targets the counter replacement policy in DSAC by cloning the random number generator. We describe and simulate this attack, and establish its efficacy. Finally, we discuss other weaknesses in DSAC.
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Generative models improve fairness of medical classifiers under distribution shifts
Ira Ktena
Olivia Wiles
Isabela Albuquerque
Sylvestre-Alvise Rebuffi
Ryutaro Tanno
Danielle Belgrave
Taylan Cemgil
Nature Medicine (2024)
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Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and ‘labeling’ by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.
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What is it to explain the outputs of an opaque machine learning model? Popular strategies in the literature are to develop
explainable machine learning techniques. These techniques approximate how the model works by providing local or global
information about the inner workings of a machine learning model. In this paper, we argue that, in some cases, explaining
machine learning outputs requires appealing to the third kind of explanation that we call socio-structural explanations.
The importance of socio-structural explanations is motivated by the observation that machine learning models are not
autonomous mathematico-computational entities. Instead, their very existence is intrinsically tied to the social context in
which they operate. Sometimes, the social structures are mirrored in the design and training of machine learning models
and hence appealing to the socio-structural explanations offers the relevant explanation for why the output is obtained.
By thoroughly examining a well-known case of racially biased algorithmic resource allocation in healthcare, we highlight
the significance of socio-structural explanations. One ramification of our proposal is that to understand how machine
learning models perpetuate unjust social harms, more is needed to interpret them by model interpretability methods.
Instead, providing socio-structural explanations adds explanatory adequacy as to how and why machine learning outputs
are obtained
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Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation
Susan Lin
Jeremy Warner
J.D. Zamfirescu-Pereira
Matthew G Lee
Sauhard Jain
Michael Xuelin Huang
Bjoern Hartmann
Can Liu
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA
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Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge, and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneously spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.
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We're roughly 10 years into the OpenConfig journey. We have implementations in hand from various vendors, and we've gained significant operational experience in the domains of Streaming Telemetry and in Developing Configuration Systems to leverage the developed models. What have we learned? Are the abstractions we've generated the right ones? If not, why? Were we too influenced by the tools and inertia of the time when we made some critical decisions? How do we need to evolve going forward? This discussion is part retrospective/introspective, a candid look at where we've been and what we need to think about as we evolve the next generation of our management (and control) planes. What should we be thinking about as network engineers who write software?
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FrameQuant: Flexible Low-Bit Quantization for Transformers
Harshavardhan Adepu
Zhanpeng Zeng
Vikas Singh
International Conference on Machine Learning (2024)
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Transformers are the backbone of powerful foundation models for many Vision and Natural Language Processing tasks. But their compute and memory/storage footprint is large, and so, serving such models is expensive often requiring high-end hardware. To mitigate this difficulty, Post-Training Quantization seeks to modify a pre-trained model and quantize it to eight bits or lower, significantly boosting compute/memory/latency efficiency. Such models have been successfully quantized to four bits with some performance loss. In this work, we outline a simple scheme to quantize Transformer-based models to just two bits (plus some overhead) with only a small drop in accuracy. Key to our formulation is a concept borrowed from Harmonic analysis called Fusion Frames. Our main finding is that the quantization must take place not in the original weight space, but instead in the Fusion Frame representations. If quantization is interpreted as the addition of noise, our casting of the problem allows invoking an extensive body of known consistent recovery and noise robustness guarantees. Further, if desired, denoising filters are known in closed form. We show empirically, via a variety of experiments, that (almost) two-bit quantization for Transformer models promises sizable efficiency gains.
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Building Recommendation Systems using Lambda Architecture
Vipul Bharat Marlecha
Sreyashi Das
International Research Journal of Engineering and Technology (IRJET), Volume: 11 Issue: 05 | May 2024 (2024)
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This paper studies the recommendation systems that are typical to content discovery and personalized services like Netflix and Amazon. The study includes typical components of recommendation systems, what data and inputs are required to serve depending on the machine learning models used. We share how the recommendations leverage a mix of batch processing and streaming databases, and end with trends and potential future developments for recommendation systems
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A Decade of Privacy-Relevant Android App Reviews: Large Scale Trends
Omer Akgul
Michelle Mazurek
Benoit Seguin
Preview abstract
We present an analysis of 12 million instances of privacy-relevant reviews publicly visible on the Google Play Store that span a 10 year period. By leveraging state of the art NLP techniques, we examine what users have been writing about privacy along multiple dimensions: time, countries, app types, diverse privacy topics, and even across a spectrum of emotions. We find consistent growth of privacy-relevant reviews, and explore topics that are trending (such as Data Deletion and Data Theft), as well as those on the decline (such as privacy-relevant reviews on sensitive permissions). We find that although privacy reviews come from more than 200 countries, 33 countries provide 90% of privacy reviews. We conduct a comparison across countries by examining the distribution of privacy topics a country’s users write about, and find that geographic proximity is not a reliable indicator that nearby countries have similar privacy perspectives. We uncover some countries with unique patterns and explore those herein. Surprisingly, we uncover that it is not uncommon for reviews that discuss privacy to be positive (32%); many users express pleasure about privacy features within apps or privacy-focused apps. We also uncover some unexpected behaviors, such as the use of reviews to deliver privacy disclaimers to developers. Finally, we demonstrate the value of analyzing app reviews with our approach as a complement to existing methods for understanding users' perspectives about privacy.
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Streamlining Workload Management in AI-Driven Cloud Architectures: A Comparative Algorithmic Approach
Pravallika Mannem
Kiran Kumar Patibandla
International Research Journal of Engineering and Technology, 11 (2024), pp. 113-121
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The use of artificial intelligence (AI) in cloud architectures has significantly increased processing efficiency and scale. However, with the development of complex algorithms and big data as well as surprisingly entered into our machine learning world; workload management becomes a significant issue in AI cloud computing. Existing workload management solutions are rule-based heuristics that may result in underutilization of resources and poor performance. For that, we present an algorithmic comparative approach to easing the burden of workload management for AI-driven cloud architectures. This is in contrast to executing a batch of tasks with different algorithms and comparing performance, cost, etc. We use ML methods to determine the best algorithm for our workload, and then deploy this in a self-contained binary that can switch between algorithms at runtime on an available resource. We validated our scheme with simulations, which demonstrates the capability of superior resource use and diminished completion time in comparison to rule-based schemes. When needed, flexibility and scalability allow you easier control over workloads that are subject to change or allocation. By simplifying AI-driven cloud workload management, the elasticity of their overall approach greatly enhances efficiency and scalability for those organizations looking to run even larger and take advantage of more complex workloads faster Tweet this Share on Facebook.
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Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning
Karel Král
Marina Zhang
Transactions on Cryptographic Hardware and Embedded Systems (TCHES), IACR (2024)
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To make cryptographic processors more resilient against side-channel attacks, engineers have developed various countermeasures. However, the effectiveness of these countermeasures is often uncertain, as it depends on the complex interplay between software and hardware. Assessing a countermeasure’s effectiveness using profiling techniques or machine learning so far requires significant expertise and effort to be adapted to new targets which makes those assessments expensive. We argue that including cost-effective automated attacks will help chip design teams to quickly evaluate their countermeasures during the development phase, paving the way to more secure chips.In this paper, we lay the foundations toward such automated system by proposing GPAM, the first deep-learning system for power side-channel analysis that generalizes across multiple cryptographic algorithms, implementations, and side-channel countermeasures without the need for manual tuning or trace preprocessing. We demonstrate GPAM’s capability by successfully attacking four hardened hardware-accelerated elliptic-curve digital-signature implementations. We showcase GPAM’s ability to generalize across multiple algorithms by attacking a protected AES implementation and achieving comparable performance to state-of-the-art attacks, but without manual trace curation and within a limited budget. We release our data and models as an open-source contribution to allow the community to independently replicate our results and build on them.
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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 Kelly
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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Specialized Large multi-modal models (LMMs) have exhibited remarkable performance across numerous tasks, however, generalist LMMs suffer from performance degradation when training with a large collection of tasks. Recent research suggests Mixture of Experts (MoE) Models help instruction tuning, however, for LMMs of parameter size around O(50-100B), the prohibitive cost of replicating and storing the expert models severely limits the number of experts we can use.
We propose Omni-SMoLA that softly mixes many multimodal low rank experts to large models without introducing significant new parameter count compared to conventional MoE models. The core idea is that the large model provides a foundational backbone and different lightweight experts learn specialized knowledge residually. Extensive experiments demonstrate that the SMoLA approach helps improve the generalist performance across a broad range of visual question answering and captioning tasks, achieving a new state-of-the-art generalist performance that matches or outperforms single specialized LMM baselines.
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A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Heather Cole-Lewis
Nenad Tomašev
Liam McCoy
Leo Anthony Celi
Alanna Walton
Akeiylah DeWitt
Philip Mansfield
Sushant Prakash
Joelle Barral
Ivor Horn
Karan Singhal
Nature Medicine (2024)
Preview abstract
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and our dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed by narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare.
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Generative AI (GAI) is proliferating, and among its many applications are to support creative work (e.g., generating text, images, music) and to enhance accessibility (e.g., captions of images and audio). As GAI evolves, creatives must consider how (or how not) to incorporate these tools into their practices. In this paper, we present interviews at the intersection of these applications. We learned from 10 creatives with disabilities who intentionally use and do not use GAI in and around their creative work. Their mediums ranged from audio engineering to leatherwork, and they collectively experienced a variety of disabilities, from sensory to motor to invisible disabilities. We share cross-cutting themes of their access hacks, how creative practice and access work become entangled, and their perspectives on how GAI should and should not fit into their workflows. In turn, we offer qualities of accessible creativity with responsible AI that can inform future research.
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Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
Yae Jee Cho
Aldi Fahrezi
Gauri Joshi
The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024) (2024)
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Foundation models (FMs) adapt well to specific domains or tasks with fine-tuning, and federated learning (FL) enables the potential for privacy-preserving fine-tuning of the FMs with on-device local data. For federated fine-tuning of FMs, we consider the FMs with small to medium parameter sizes of single digit billion at maximum, referred to as on-device FMs (ODFMs) that can be deployed on devices for inference but can only be fine-tuned with parameter efficient methods. In our work, we tackle the data and system heterogeneity problem of federated fine-tuning of ODFMs by proposing a novel method using heterogeneous low-rank approximations (LoRAs), namely HetLoRA. First, we show that the naive approach of using homogeneous LoRA ranks across devices face a trade-off between overfitting and slow convergence, and thus propose HetLoRA, which allows heterogeneous ranks across client devices and efficiently aggregates and distributes these heterogeneous LoRA modules. By applying rank self-pruning locally and sparsity-weighted aggregation at the server, HetLoRA combines the advantages of high and low-rank LoRAs, which achieves improved convergence speed and final performance compared to homogeneous LoRA. Furthermore, HetLoRA offers enhanced computation efficiency compared to full fine-tuning, making it suitable for federated fine-tuning across heterogeneous devices.
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