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 10033 publications
RFC 9632 - Finding and Using Geofeed Data
RFC Editor, RFC Editor (2024), pp. 23
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This document specifies how to augment the Routing Policy Specification Language (RPSL) inetnum: class to refer specifically to geofeed comma-separated values (CSV) data files and describes an optional scheme that uses the Resource Public Key Infrastructure (RPKI) to authenticate the geofeed data files. This document obsoletes RFC 9092.
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Using Early Readouts to Mediate Featural Bias in Distillation
Rishabh Tiwari
Durga Sivasubramanian
Anmol Mekala
Ganesh Ramakrishnan
WACV 2024 (2024)
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Deep networks tend to learn spurious feature-label correlations in real-world supervised learning tasks. This vulnerability is aggravated in distillation, where a (student) model may have less representational capacity than the corresponding teacher model. Often, knowledge of specific problem features is used to reweight instances & rebalance the learning process. We propose a novel early readout mechanism whereby we attempt to predict the label using representations from earlier network layers. We show that these early readouts automatically identify problem instances or groups in the form of confident, incorrect predictions. We improve group fairness measures across benchmark datasets by leveraging these signals to mediate between teacher logits and supervised label. We extend our results to the closely related but distinct problem of domain generalization, which also critically depends on the quality of learned features. We provide secondary analyses that bring insight into the role of feature learning in supervision and distillation.
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The article summarizes the unique challenges and strategies required for a successful GTM (Go to market) strategy in enterprise world. We cover how enterprise PM function is unique from regular PM, and why enterprise PMs must look at distribution as an inherent product process. We also share a framework for thinking about various components of GTM strategy. Key aspects include customer segmentation, account acquisition strategies, product packaging, positionining and marketing; and technical enablement and content distribution.
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Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines
Yuchen Li
Alexandre Kirchmeyer
Aashay Mehta
Yilong Qin
Andrej Risteski
International Conference on Machine Learning (2024) (to appear)
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Autoregressive language models are the currently dominant paradigm for text generation, however they have some fundamental limitations that cannot be remedied by scale ---for example inherently sequential and unidirectional generation. While alternate classes of models have been explored, we have limited mathematical understanding of their fundamental power and limitations. In this paper we focus on Generative Masked Language Models (GMLMs), a non-autoregressive paradigm in which we train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. These models empirically strike a promising speed-quality trade-off as each step can be typically parallelized by decoding the entire sequence in parallel. We develop a mathematical framework for analyzing and improving such models which sheds light on questions of sample complexity and inference speed and quality. Empirically, we adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality compared with autoregressive models. We run careful ablation experiments to give recommendations on key design choices, and make fine-grained observations on the common error modes in connection with our theory. Our mathematical analyses and empirical observations characterize both potentials and limitations of this approach, and can be applied to future works on improving understanding and performance of GMLMs.
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Machine learning has a pseudoscience problem. An abundance of ethical issues arising from the use of machine learning (ML)-based technologies—by now, well documented—is inextricably entwined with the systematic epistemic misuse of these tools. We take a recent resurgence of deep learning-assisted physiognomic research as a case study in the relationship between ML-based pseudoscience and attendant social harms—the standard purview of “AI ethics.” In practice, the epistemic and ethical dimensions of ML misuse often arise from shared underlying reasons and are resolvable by the same pathways. Recent use of ML toward the ends of predicting protected attributes from photographs highlights the need for philosophical, historical, and domain-specific perspectives of particular sciences in the prevention and remediation of misused ML.
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Sharing is leaking: blocking transient-execution attacks with core-gapped confidential VMs
Charly Castes
29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4 (ASPLOS '24) (2024)
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Confidential VMs on platforms such as Intel TDX, AMD SEV and Arm CCA promise greater security for cloud users against even a hypervisor-level attacker, but this promise has been shattered by repeated transient-execution vulnerabilities and CPU bugs. At the root of this problem lies the need to multiplex CPU cores with all their complex microarchitectural state among distrusting entities, with an untrusted hypervisor in control of the multiplexing.
We propose core-gapped confidential VMs, a set of software-only modifications that ensure that no distrusting code shares a core, thus removing all same-core side-channels and transient-execution vulnerabilities from the guest’s TCB. We present an Arm-based prototype along with a performance evaluation showing that, not only does core-gapping offer performance competitive with non-confidential VMs, the greater locality achieved by avoiding shared cores can even improve performance for CPU-intensive workloads.
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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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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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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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Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction
Babak Behsaz
Zachary Ryan Mccaw
Davin Hill
Robert Luben
Dongbing Lai
John Bates
Howard Yang
Tae-Hwi Schwantes-An
Yuchen Zhou
Anthony Khawaja
Andrew Carroll
Brian Hobbs
Michael Cho
Nature Genetics (2024)
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Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and HDCD. REGLE leverages variational autoencoders to compute nonlinear disentangled embeddings of HDCD, which become the inputs to genome-wide association studies (GWAS). REGLE can uncover features not captured by existing expert-defined features and enables the creation of accurate disease-specific polygenic risk scores (PRSs) in datasets with very few labeled data. We apply REGLE to perform GWAS on respiratory and circulatory HDCD—spirograms measuring lung function and photoplethysmograms measuring blood volume changes. REGLE replicates known loci while identifying others not previously detected. REGLE are predictive of overall survival, and PRSs constructed from REGLE loci improve disease prediction across multiple biobanks. Overall, REGLE contain clinically relevant information beyond that captured by existing expert-defined features, leading to improved genetic discovery and disease prediction.
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Interruptions in digital services are a common occurrence for users. These disruptions, however, exact a cost in terms of attention, task completion rate, and, most importantly, emotional state. While several methods currently employed by service providers attempt to address this, the paper will argue that browser games or similar interactive interfaces should become a standard mechanism to ease the aforementioned effects.
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Graph Neural Networks (GNNs) are popular models for machine learning on graphs that typically follow the message-passing paradigm, whereby the feature of a node is updated recursively upon aggregating information over its neighbors. While exchanging messages over the input graph endows GNNs with a strong inductive bias, it can also make GNNs susceptible to \emph{over-squashing}, thereby preventing them from capturing long-range interactions in the given graph. To rectify this issue, {\em graph rewiring} techniques have been proposed as a means of improving information flow by altering the graph connectivity. In this work, we identify three desiderata for graph-rewiring: (i) reduce over-squashing, (ii) respect the locality of the graph, and (iii) preserve the sparsity of the graph. We highlight fundamental trade-offs that occur between {\em spatial} and {\em spectral} rewiring techniques; while the former often satisfy (i) and (ii) but not (iii), the latter generally satisfy (i) and (iii) at the expense of (ii). We propose a novel rewiring framework that satisfies all of (i)--(iii) through a locality-aware sequence of rewiring operations. We then discuss a specific instance of such rewiring framework and validate its effectiveness on several real-world benchmarks, showing that it either matches or significantly outperforms existing rewiring approaches.
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Instant 3D Human Avatar Generation using Image Diffusion Models
Enric Corona
European Conference on Computer Vision (ECCV) (2024)
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We present AvatarPopUp, a method for fast, high quality 3D human avatar generation from different input modalities, such as images and text prompts and with control over the generated pose and shape. The common theme is the use of diffusion-based image generation networks that are specialized for each particular task, followed by a 3D lifting network. We purposefully decouple the generation from the 3D modeling which allow us to leverage powerful image synthesis priors, trained on billions of text-image pairs. We fine-tune latent diffusion networks with additional image conditioning for image generation and back-view prediction, and to support qualitatively different multiple 3D hypotheses. Our partial fine-tuning approach allows to adapt the networks for each task without inducing catastrophic forgetting. In our experiments, we demonstrate that our method produces accurate, high-quality 3D avatars with diverse appearance that respect the multimodal text, image, and body control signals. Our approach can produce a 3D model in as few as 2 seconds, a four orders of magnitude speedup w.r.t. the vast majority of existing methods, most of which solve only a subset of our tasks, and with fewer controls. AvatarPopUp enables applications that require the controlled 3D generation of human avatars at scale.
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On the Benefits of Traffic “Reprofiling” The Single Hop Case
Henry Sariowan
Jiaming Qiu
Jiayi Song
Roch Guerin
IEEE/ACM Transactions on Networking (2024)
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Datacenters have become a significant source of traffic, much of which is carried over private networks. The operators of those networks commonly have access to detailed traffic profiles and performance goals, which they seek to meet as efficiently as possible. Of interest are solutions that guarantee latency while minimizing network bandwidth. The paper explores a basic building block towards realizing such solutions, namely, a single hop configuration. The main results are in the form of optimal solutions for meeting local deadlines under schedulers of varying complexity and therefore cost. The results demonstrate how judiciously modifying flows’ traffic profiles, i.e., reprofiling them, can help simple schedulers reduce the bandwidth they require, often performing nearly as well as more complex ones.
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Japanese text-to-pronunciation modelling is a notoriously data-intensive problem. Japanese data sources are often only partially annotated, and use different annotation standards for pronunciation and word segmentation. This talk introduces a set of techniques that enable ingesting data that may be partially annotated, use arbitrary word segmentations, and use a variety of pronunciation annotation standards.
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