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18 Nov

EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records

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[Submitted on 23 May 2024 (v1), last revised 15 Nov 2024 (this version, v3)] View a PDF of the paper titled EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records, by Adibvafa Fallahpour and 5 other authors View PDF HTML (experimental) Abstract:Transformers have significantly advanced the modeling of Electronic Health Records (EHR), yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the quadratic computational cost and insufficient context length of these models hinder hospitals' ability in processing the extensive medical histories typical in EHR data. Additionally, existing models employ separate finetuning for each clinical…

18 Nov

BOP-Distrib: Revisiting 6D Pose Estimation Benchmark for Better Evaluation under Visual Ambiguities

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[Submitted on 30 Aug 2024 (v1), last revised 15 Nov 2024 (this version, v2)] View a PDF of the paper titled BOP-Distrib: Revisiting 6D Pose Estimation Benchmark for Better Evaluation under Visual Ambiguities, by Boris Meden and 3 other authors View PDF HTML (experimental) Abstract:6D pose estimation aims at determining the pose of the object that best explains the camera observation. The unique solution for a non-symmetrical object can turn into a multi-modal pose distribution for a symmetrical object or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that…

18 Nov

MANTIS: Interleaved Multi-Image Instruction Tuning

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[Submitted on 2 May 2024 (v1), last revised 15 Nov 2024 (this version, v3)] View a PDF of the paper titled MANTIS: Interleaved Multi-Image Instruction Tuning, by Dongfu Jiang and 6 other authors View PDF HTML (experimental) Abstract:Large multimodal models (LMMs) have shown great results in single-image vision language tasks. However, their abilities to solve multi-image visual language tasks is yet to be improved. The existing LMMs like OpenFlamingo, Emu2, and Idefics gain their multi-image ability through pre-training on hundreds of millions of noisy interleaved image-text data from the web, which is neither efficient nor effective. In this paper, we…

18 Nov

Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits

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[Submitted on 23 Feb 2024 (v1), last revised 15 Nov 2024 (this version, v4)] View a PDF of the paper titled Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits, by Julien Zhou (Thoth and 6 other authors View PDF Abstract:We address the problem of stochastic combinatorial semi-bandits, where a player selects among P actions from the power set of a set containing d base items. Adaptivity to the problem's structure is essential in order to obtain optimal regret upper bounds. As estimating the coefficients of a covariance matrix can be manageable in practice, leveraging them should improve the regret.…

18 Nov

SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection

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[Submitted on 4 Jul 2024 (v1), last revised 15 Nov 2024 (this version, v3)] View a PDF of the paper titled SOWA: Adapting Hierarchical Frozen Window Self-Attention to Visual-Language Models for Better Anomaly Detection, by Zongxiang Hu and 2 other authors View PDF HTML (experimental) Abstract:Visual anomaly detection is essential in industrial manufacturing, yet traditional methods often rely heavily on extensive normal datasets and task-specific models, limiting their scalability. Recent advancements in large-scale vision-language models have significantly enhanced zero- and few-shot anomaly detection. However, these approaches may not fully leverage hierarchical features, potentially overlooking nuanced details crucial for accurate detection.…

18 Nov

Molmo Walkthough and Inference with Pointing Demo Code

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Allen AI is doing great applied research in the area of generative AI. With their release of Molmo, a family of multimodal models based on Olmo and Qwen2, they proved that good quality data can often triumph over larger models when it comes to results and human preference. Diving into the models first-hand is the best way to judge how good the models are. This article will cover the Molmo technical report and multimodal inference using the Molmo-1B model. Figure 1. Molmo image description and pointing demo. Primarily, we will cover the following topics while discussing about Molmo What was…

16 Nov

In-demand Data Science Skills to Learn In 2025

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As businesses are adopting a data-driven culture rapidly, the demand for skilled data science professionals is soaring. Data science is an incredible technology that helps organizations get actionable insights from their data that they can use to innovate products and services, boost productivity, improve operational efficiency, or enhance customer experience. However, implementing an efficient data science project requires a lot of tasks, from data collection to data visualization and training data models to deploying and maintaining them. Therefore, data science professionals must be proficient in several core data science skills to succeed in their careers. If you are also looking to make a career in…

16 Nov

Monte Carlo Brings GenAI to Data Observability

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(Treecha/Shutterstock) Monte Carlo has made a name for itself in the field of data observability, where it uses machine learning and other statistical methods to identify quality and reliability issues hiding in big data. With this week’s update, which it made during its IMPACT 2024 event, the company is adopting generative AI to help it take its data observability capabilities to a new level. When it comes to data observability, or any type of IT observability discipline for that matter, there is no magic bullet (or ML model) that can detect all of the potential ways data can go bad.…

16 Nov

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization

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16 Nov

Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecasting

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arXiv:2411.09251v1 Announce Type: cross Abstract: Predicting spatio-temporal traffic flow presents significant challenges due to complex interactions between spatial and temporal factors. Existing approaches often address these dimensions in isolation, neglecting their critical interdependencies. In this paper, we introduce the Spatio-Temporal Unitized Model (STUM), a unified framework designed to capture both spatial and temporal dependencies while addressing spatio-temporal heterogeneity through techniques such as distribution alignment and feature fusion. It also ensures both predictive accuracy and computational efficiency. Central to STUM is the Adaptive Spatio-temporal Unitized Cell (ASTUC), which utilizes low-rank matrices to seamlessly store, update, and interact with space, time, as…