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CHASE: A Query Engine that is Natively Designed to Support Efficient Hybrid Queries on Structured and Unstructured Data Afeerah Naseem Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Domains like social media analysis, e-commerce, and healthcare data management require querying through large chunks of structured and unstructured databases. In this modern world, there has been an ever-increasing requirement for the same in many other domains. However, current systems have been proven inefficient… Read More »CHASE: A Query Engine that is Natively Designed to Support Efficient Hybrid Queries on Structured and Unstructured Data Afeerah Naseem Artificial Intelligence Category – MarkTechPost

Delayed Fusion: Integrating Large Language Models into First-Pass Decoding in End-to-end Speech Recognition Apple Machine Learning Research

​This paper presents an efficient decoding approach for end-to-end automatic speech recognition (E2E-ASR) with large language models (LLMs). Although shallow fusion is the most common approach to incorporate language models into E2E-ASR decoding, we face two practical problems with LLMs. (1) LLM inference is computationally… Read More »Delayed Fusion: Integrating Large Language Models into First-Pass Decoding in End-to-end Speech Recognition Apple Machine Learning Research

DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models Apple Machine Learning Research

​Generating high-quality 3D content requires models capable of learning robust distributions of complex scenes and the real-world objects within them. Recent Gaussian-based 3D reconstruction techniques have achieved impressive results in recovering high-fidelity 3D assets from sparse input images by predicting 3D Gaussians in a feed-forward… Read More »DSplats: 3D Generation by Denoising Splats-Based Multiview Diffusion Models Apple Machine Learning Research

On the Modeling Capabilities of Large Language Models for Sequential Decision Making Apple Machine Learning Research

​Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems. In this paper, we investigate the capabilities of Large Language Models (LLMs) for reinforcement learning (RL) across… Read More »On the Modeling Capabilities of Large Language Models for Sequential Decision Making Apple Machine Learning Research

ChemAgent: Enhancing Large Language Models for Complex Chemical Reasoning with Dynamic Memory Frameworks Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Chemical reasoning involves intricate, multi-step processes requiring precise calculations, where small errors can lead to significant issues. LLMs often struggle with domain-specific challenges, such as accurately handling chemical formulas, reasoning through complex steps, and integrating code effectively. Despite advancements in scientific reasoning, benchmarks like… Read More »ChemAgent: Enhancing Large Language Models for Complex Chemical Reasoning with Dynamic Memory Frameworks Sana Hassan Artificial Intelligence Category – MarkTechPost

NVIDIA AI Introduces Omni-RGPT: A Unified Multimodal Large Language Model for Seamless Region-level Understanding in Images and Videos Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Multimodal large language models (MLLMs) bridge vision and language, enabling effective interpretation of visual content. However, achieving precise and scalable region-level comprehension for static images and dynamic videos remains challenging. Temporal inconsistencies, scaling inefficiencies, and limited video comprehension hinder progress, particularly in maintaining consistent… Read More »NVIDIA AI Introduces Omni-RGPT: A Unified Multimodal Large Language Model for Seamless Region-level Understanding in Images and Videos Asif Razzaq Artificial Intelligence Category – MarkTechPost

This AI Paper from Alibaba Unveils WebWalker: A Multi-Agent Framework for Benchmarking Multistep Reasoning in Web Traversal Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Enabling artificial intelligence to navigate and retrieve contextually rich, multi-faceted information from the internet is important in enhancing AI functionalities. Traditional search engines are limited to superficial results, failing to capture the nuances required to investigate profoundly integrated content across a network of related… Read More »This AI Paper from Alibaba Unveils WebWalker: A Multi-Agent Framework for Benchmarking Multistep Reasoning in Web Traversal Aswin Ak Artificial Intelligence Category – MarkTechPost

CMU Researchers Propose QueRE: An AI Approach to Extract Useful Features from a LLM Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) have become integral to various artificial intelligence applications, demonstrating capabilities in natural language processing, decision-making, and creative tasks. However, critical challenges remain in understanding and predicting their behaviors. Treating LLMs as black boxes complicates efforts to assess their reliability, particularly… Read More »CMU Researchers Propose QueRE: An AI Approach to Extract Useful Features from a LLM Sajjad Ansari Artificial Intelligence Category – MarkTechPost

Meet Tensor Product Attention (TPA): Revolutionizing Memory Efficiency in Language Models Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have become central to natural language processing (NLP), excelling in tasks such as text generation, comprehension, and reasoning. However, their ability to handle longer input sequences is limited by significant computational challenges, particularly memory overhead during inference caused by key-value… Read More »Meet Tensor Product Attention (TPA): Revolutionizing Memory Efficiency in Language Models Aswin Ak Artificial Intelligence Category – MarkTechPost