The pre-training of language models (LMs) plays a crucial role in enabling their ability to understand and generate text. However, a significant challenge lies in effectively leveraging the diversity of training corpora, which often include data from varied sources such as Wikipedia, blogs, and social media. Models typically treat all input data equivalently, disregarding contextual […]

The post Researchers from Princeton University Introduce Metadata Conditioning then Cooldown (MeCo) to Simplify and Optimize Language Model Pre-training appeared first on MarkTechPost.

Complex domains like social media, molecular biology, and recommendation systems have graph-structured data that consists of nodes, edges, and their respective features. These nodes and edges do not have a structured relationship, so addressing them using graph neural networks (GNNs) is essential. However, GNNs rely on labeled data, which is difficult and expensive to obtain. […]

The post PyG-SSL: An Open-Source Library for Graph Self-Supervised Learning and Compatible with Various Deep Learning and Scientific Computing Backends appeared first on MarkTechPost.

Large language models (LLMs) have revolutionized natural language processing, enabling applications that range from automated writing to complex decision-making aids. However, ensuring these models produce factually accurate responses remains a significant challenge. At times, LLMs generate outputs that appear credible but are factually incorrect, a phenomenon often referred to as “hallucination.” This issue becomes particularly […]

The post DeepMind Research Introduces The FACTS Grounding Leaderboard: Benchmarking LLMs’ Ability to Ground Responses to Long-Form Input appeared first on MarkTechPost.

Advancements in neural networks have brought significant changes across domains like natural language processing, computer vision, and scientific computing. Despite these successes, the computational cost of training such models remains a key challenge. Neural networks often employ higher-order tensor weights to capture complex relationships, but this introduces memory inefficiencies during training. Particularly in scientific computing, […]

The post Researchers from Caltech, Meta FAIR, and NVIDIA AI Introduce Tensor-GaLore: A Novel Method for Efficient Training of Neural Networks with Higher-Order Tensor Weights appeared first on MarkTechPost.

Video-Language Representation Learning is a crucial subfield of multi-modal representation learning that focuses on the relationship between videos and their associated textual descriptions. Its applications are explored in numerous areas, from question answering and text retrieval to summarization. In this regard ,contrastive learning has emerged as a powerful technique that elevates video-language learning by enabling […]

The post HBI V2: A Flexible AI Framework that Elevates Video-Language Learning with a Multivariate Co-Operative Game appeared first on MarkTechPost.

Multimodal foundation models are becoming increasingly relevant in artificial intelligence, enabling systems to process and integrate multiple forms of data—such as images, text, and audio—to address diverse tasks. However, these systems face significant challenges. Existing models often struggle to generalize across a wide variety of modalities and tasks due to their reliance on limited datasets […]

The post EPFL Researchers Releases 4M: An Open-Source Training Framework to Advance Multimodal AI appeared first on MarkTechPost.

Ovarian lesions are frequently detected, often by chance, and managing them is crucial to avoid delayed diagnoses or unnecessary interventions. While transvaginal ultrasound is the primary diagnostic tool for distinguishing benign from malignant lesions, its accuracy heavily relies on the examiner’s expertise. A shortage of skilled ultrasound professionals exacerbates diagnostic delays, particularly as biopsies are […]

The post Transformer-Based AI Models for Ovarian Lesion Diagnosis: Enhancing Accuracy and Reducing Expert Referral Dependence Across International Centers appeared first on MarkTechPost.

The development of Physical AI—AI systems designed to simulate, predict, and optimize real-world physics—has long been constrained by significant challenges. Building accurate models often demands extensive computational resources and time, with simulations sometimes requiring days or weeks to produce actionable results. Additionally, the complexity of scaling these systems for practical use across industries such as […]

The post NVIDIA AI Introduces Cosmos World Foundation Model (WFM) Platform to Advance Physical AI Development appeared first on MarkTechPost.

Dense embedding-based text retrieval has become the cornerstone for ranking text passages in response to queries. The systems use deep learning models for embedding text into vector spaces that enable semantic similarity measurements. This method has been adopted widely in applications such as search engines and retrieval-augmented generation (RAG), where retrieving accurate and contextually relevant […]

The post This AI Paper from Tel Aviv University Introduces GASLITE: A Gradient-Based Method to Expose Vulnerabilities in Dense Embedding-Based Text Retrieval Systems appeared first on MarkTechPost.

In a time when global health faces persistent threats from emerging pandemics, the need for advanced biosurveillance and pathogen detection systems is increasingly evident. Traditional genomic analysis methods, while effective in isolated cases, often struggle to address the complexities of large-scale health monitoring. A significant challenge is identifying and understanding the genomic diversity in environments […]

The post Researchers from USC and Prime Intellect Released METAGENE-1: A 7B Parameter Autoregressive Transformer Model Trained on Over 1.5T DNA and RNA Base Pairs appeared first on MarkTechPost.

Predicting transcriptomes directly from genome sequences is a significant challenge in microbial genomics, particularly for the numerous sequenced microbes that remain unculturable or require complex experimental protocols like RNA-seq. The gap between genomic information and functional understanding leaves us without knowledge of the microbial adaptive processes, survival mechanisms, and gene regulation functions. This must be […]

The post This AI paper from the Beijing Institute of Technology and Harvard Unveils TXpredict for Predicting Microbial Transcriptomes appeared first on MarkTechPost.

When it comes to AI tools, chatbots are often the first thing that comes to mind —conversation-based interfaces for users to write queries and receive responses. These dialogue interfaces are certainly useful, but they aren’t always the best fit for handling our everyday work. Often tacked on to the side of our workflows, chatbots supplement […]

The post Meet Height: An Autonomous Project Management Platform Leading the Next Wave of AI Tools appeared first on MarkTechPost.