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Applications

Researchers at Apple suggest LazyLLM: a unique AI strategy for productive LLM inference, specifically in situations with extended context.

Large Language Models (LLMs) have improved significantly, but challenges persist, particularly in the prefilling stage. This is because the cost of computing attention increases with the number of tokens in the prompts, leading to a slow time-to-first-token (TTFT). As such, optimizing TTFT is crucial for efficient LLM inference. Various methods have been proposed to improve…

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PILOT: An Innovative Machine Learning Procedure for Linear Model Trees that Offers Speed, Regularization, Stability, and Comprehensibility

Before the development of PILOT (PIecewise Linear Organic Tree), linear model trees were slow to fit and susceptible to overfitting, notably with large datasets. The traditional regression trees faced challenges capturing linear relationships efficiently. Linear model trees also encountered problems with interpretability when integrating linear models in leaf nodes. The research points out the need…

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LaMMOn: A Comprehensive Multi-Camera Tracking System utilizing Transformers and Graph Neural Networks for Improved Instant Traffic Control.

Multi-target multi-camera tracking (MTMCT) has become indispensable in intelligent transportation systems, yet real-world applications are complex due to a shortage of publicly available data and laborious manual annotation. MTMCT involves tracking vehicles across multiple camera lenses, detecting objects, carrying out multi-object tracking, and finally clustering trajectories to generate a comprehensive image of vehicle movement. MTMCT…

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Benchmark for Visual Haystacks: The Inaugural Image-Focused Needle-In-A-Haystack (NIAH) Benchmark for Evaluating LMMs’ Proficiency in Long-Context Visual Search and Analysis

In the domain of visual question answering (VQA), the Multi-Image Visual Question Answering (MIQA) remains a major hurdle. It entails generating pertinent and grounded responses to natural language prompts founded on a vast assortment of images. While large multimodal models (LMMs) have proven competent in single-image VQA, they falter when dealing with queries involving an…

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Agent Poison: A Unique Approach for Red Teaming and Backdoor Assaults on Generic and RAG-based LLM Agents by Contaminating their Persistent Memory or RAG Information Repository.

Large Language Models (LLMs) have shown vast potential in various critical sectors, such as finance, healthcare, and self-driving cars. Typically, these LLM agents use external tools and databases to carry out tasks. However, this reliance on external sources has raised concerns about their trustworthiness and vulnerability to attacks. Current methods of attack against LLMs often…

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Google AI Unveils NeuralGCM: A Fresh Machine Learning (ML) Oriented Method for Imitating Earth’s Atmosphere

General circulation models (GCMs) are crucial in weather and climate prediction. They work using numerical solvers for big scale dynamics and parameterizations for smaller processes like cloud formation. Despite continuous enhancements, difficulties still persist, including errors, biases, and uncertainties in long-term weather projections and severe weather events. Recently introduced machine-learning models have shown excellent results…

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OAK (Open Artificial Knowledge) Dataset: An Extensive Tool for AI Studies Sourced from Wikipedia’s Primary Sections

The significant progress in Artificial Intelligence (AI) and Machine Learning (ML) has underscored the crucial need for extensive, varied, and high-quality datasets to train and test basic models. Gathering such datasets is a challenging task due to issues like data scarcity, privacy considerations, and expensive data collection and annotation. Synthetic or artificial data has emerged…

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