Neural intel Pod

This source evaluates and compares two reinforcement learning algorithms, GRPO and DPO, for their effectiveness in generating images from text descriptions. The research investigates how different re…

This document presents a research paper on a novel framework, Let Androids Dream (LAD), designed to enhance AI's ability to understand the implied meanings and metaphors in images, a significant chal…

This source introduces SmolVLM, a collection of small-scale multimodal models designed for efficiency on devices with limited computing power. The authors experiment with different architectural choi…

This academic survey provides a comprehensive overview of Federated Learning (FL), a distributed machine learning approach allowing collaborative model training without centralizing sensitive data. I…

This document details research into improving Federated Learning (FL) efficiency in autonomous mobile networks by incorporating tiny language models (TLMs) for predicting network performance features…

This technical report introduces Mobile-MMLU, a new benchmark designed to evaluate large language models (LLMs) specifically for mobile devices, addressing the limitations of existing benchmarks whic…

This document presents AI-RAN, a paradigm shift integrating Radio Access Network (RAN) and Artificial Intelligence (AI) workloads onto a unified platform. It outlines the evolution of RAN and categor…

These sources collectively explain that fine-tuning is a process of retraining a pre-trained Large Language Model on a specialized dataset to enhance its performance on particular tasks or domains. W…

This document describes the development of a Large Language Model (LLM) specifically tailored for explaining VHDL code within a high-performance processor design environment. Recognizing the unique r…

This document introduces and analyzes AWNN (Adaptively Weighted Nearest Neighbors), a novel matrix completion method. Traditional Nearest Neighbor (NN) methods struggle with selecting the appropriate…

This academic paper explores the training dynamics of neural networks, specifically focusing on gradient flow for fully connected feedforward networks with various smooth activation functions. The au…

This academic paper introduces WavReward, a novel evaluation system for end-to-end spoken dialogue models, which process speech input and output directly, unlike older systems that rely on text. Reco…

This academic paper introduces BLIP3-o, a suite of cutting-edge multimodal models designed for both understanding and generating images. The research investigates various architectural choices and tr…

This document introduces CodePDE, a new framework for using large language models (LLMs) to generate code that solves partial differential equations (PDEs). The authors frame PDE solving as a code ge…

This research investigates online learning for feedforward neural networks utilizing the sign activation function. The paper identifies a margin condition in the first hidden layer as crucial for lea…

This document introduces CityAVOS, a new benchmark dataset designed for Aerial Visual Object Search (AVOS)tasks using Unmanned Aerial Vehicles (UAVs) in realistic urban environments. The text describ…

This document introduces BAT (Benchmark for Auto-bidding Task), a new resource for researching autobidding algorithms in online advertising auctions. It provides a large-scale dataset from the Avito …

This collection of texts from Amazon Science highlights the company's extensive research and development efforts across various scientific and technical domains, including machine learning, artificia…

This document introduces T2I-R1, a novel text-to-image generation model that uses Reinforcement Learning (RL) and a bi-level Chain-of-Thought (CoT) process to improve image generation. Unlike traditi…

This document proposes pretraining strategies for estimating heterogeneous treatment effects (HTE), particularly the conditional average treatment effect (CATE), which varies based on individual char…