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GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts
30 May

GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts

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[Submitted on 8 Apr 2024] View a PDF of the paper titled GHOST: Grounded Human Motion Generation with Open Vocabulary Scene-and-Text Contexts, by Zolt'an 'A. Milacski and 4 other authors View PDF Abstract:The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes capturing this connection challenging with a fixed set of descriptors. Specifically, closed vocabulary scene encoders, which require learning text-scene associations from scratch, have been favored in the literature, often resulting in inaccurate motion…
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Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio
30 May

Multi-objective Representation for Numbers in Clinical Narratives Using CamemBERT-bio

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arXiv:2405.18448v1 Announce Type: new Abstract: This research aims to classify numerical values extracted from medical documents across seven distinct physiological categories, employing CamemBERT-bio. Previous studies suggested that transformer-based models might not perform as well as traditional NLP models in such tasks. To enhance CamemBERT-bio's performances, we introduce two main innovations: integrating keyword embeddings into the model and adopting a number-agnostic strategy by excluding all numerical data from the text. The implementation of label embedding techniques refines the attention mechanisms, while the technique of using a `numerical-blind' dataset aims to bolster context-centric learning. Another key component of our research is determining…
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No-code ETL for integration: best practices, trends and top tools
30 May

No-code ETL for integration: best practices, trends and top tools

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High-quality data integration is the cornerstone of informed decision-making.  Quality data is the bedrock of informed decision-making. Without it, enterprises fall prey to erroneous information, ultimately impacting their bottom line. In fact, in a groundbreaking 2018 report, Gartner claimed that businesses could be clocking losses of 15 million USD every year only because of poor data integration infrastructure. Exactly why no-code ETL tools have become increasingly popular for their ease of ability to empower non-tech users without compromising on data quality. They enable businesses to reduce traditional ETL costs and ensure timely data feeds through user-friendly automation.  In this article, we…
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Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
30 May

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes

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[Submitted on 28 May 2024] View a PDF of the paper titled Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes, by Jihao Andreas Lin and Shreyas Padhy and Bruno Mlodozeniec and Javier Antor'an and Jos'e Miguel Hern'andez-Lobato View PDF Abstract:Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community. This paper focuses on iterative methods, which use linear system solvers, like conjugate gradients, alternating projections or stochastic gradient descent, to construct an estimate of the marginal likelihood gradient. We discuss three key improvements which are applicable across solvers: (i) a pathwise…
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Transductive Zero-Shot and Few-Shot CLIP
30 May

Transductive Zero-Shot and Few-Shot CLIP

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arXiv:2405.18437v1 Announce Type: new Abstract: Transductive inference has been widely investigated in few-shot image classification, but completely overlooked in the recent, fast growing literature on adapting vision-langage models like CLIP. This paper addresses the transductive zero-shot and few-shot CLIP classification challenge, in which inference is performed jointly across a mini-batch of unlabeled query samples, rather than treating each instance independently. We initially construct informative vision-text probability features, leading to a classification problem on the unit simplex set. Inspired by Expectation-Maximization (EM), our optimization-based classification objective models the data probability distribution for each class using a Dirichlet law. The minimization problem…
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Foreign Payments and AI Tools – Transforming Global Transactions
30 May

Foreign Payments and AI Tools – Transforming Global Transactions

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In today's interconnected world, the ability to make and receive payments across borders is essential for businesses and individuals alike. Traditionally, foreign payments have been fraught with challenges, including high fees, long processing times, and complex regulatory requirements. However, the advent of artificial intelligence (AI) is revolutionizing this landscape, making international transactions faster, cheaper, and more secure. The Evolution of Foreign Payments Traditional Methods and Challenges Historically, foreign payments have relied on a network of banks and financial institutions to process transactions. Methods such as wire transfers and correspondent banking systems have been the mainstay. These methods, however, come with…
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Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis
30 May

Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis

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arXiv:2405.17459v1 Announce Type: new Abstract: In this paper, an innovative multi-modal deep learning model is proposed to deeply integrate heterogeneous information from medical images and clinical reports. First, for medical images, convolutional neural networks were used to extract high-dimensional features and capture key visual information such as focal details, texture and spatial distribution. Secondly, for clinical report text, a two-way long and short-term memory network combined with an attention mechanism is used for deep semantic understanding, and key statements related to the disease are accurately captured. The two features interact and integrate effectively through the designed multi-modal fusion layer to…
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WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets
30 May

WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets

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arXiv:2405.17455v1 Announce Type: new Abstract: This paper introduces WeatherFormer, a transformer encoder-based model designed to learn robust weather features from minimal observations. It addresses the challenge of modeling complex weather dynamics from small datasets, a bottleneck for many prediction tasks in agriculture, epidemiology, and climate science. WeatherFormer was pretrained on a large pretraining dataset comprised of 39 years of satellite measurements across the Americas. With a novel pretraining task and fine-tuning, WeatherFormer achieves state-of-the-art performance in county-level soybean yield prediction and influenza forecasting. Technical innovations include a unique spatiotemporal encoding that captures geographical, annual, and seasonal variations, adapting the transformer…
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CLAIM Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models
30 May

CLAIM Your Data: Enhancing Imputation Accuracy with Contextual Large Language Models

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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Evaluating Large Language Models with Giskard in MLflow
30 May

Evaluating Large Language Models with Giskard in MLflow

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Over the last few years, Large Language Models (LLMs) have been reshaping the field of natural language, thanks to their transformer-based architectures and their extensive training on massive datasets.In particular, Retrieval Augmented Generation (RAG) has experienced a notable rise, swiftly becoming the prevailing method for effectively exploring and retrieving enterprise data by combining vector databases and LLMs. Some of its common applications involve developing customer support bots, internal knowledge graphs, or Q&A systems.This tremendous progress, however, has also given rise to various challenges, with one of the most prominent being the complicated task of testing and validating their generated outputs.How…
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