​Learning meaningful representations of complex objects that can be seen through multiple (k≥3kgeq 3k≥3) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to kkk views, either by instantiating 12k(k−1)tfrac12k(k-1)21​k(k−1) loss-pairs, or… Read More »Contrasting Multiple Representations with the Multi-Marginal Matching Gap Apple Machine Learning Research

​In this paper, we address the problem of estimating the rotational extrinsics, as well as the scale factors of two gyroscopes rigidly mounted on the same device. In particular, we formulate the problem as a least-squares minimization and introduce a direct algorithm that computes the… Read More »A Direct Algorithm for Multi-Gyroscope Infield Calibration Apple Machine Learning Research

​In this work, we study how well the learned weights of a neural network utilize the space available to them. This notion is related to capacity, but additionally incorporates the interaction of the network architecture with the dataset. Most learned weights appear to be full… Read More »Revealing the Utilized Rank of Subspaces of Learning in Neural Networks Apple Machine Learning Research

​We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the ‘generalization on the unseen (GOTU)’ setting, where training data is fully seen in some part of the domain but testing is made on another part, and for… Read More »On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions Apple Machine Learning Research

​Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which… Read More »CodeAct: Your LLM Agent Acts Better when Generating Code Apple Machine Learning Research

​[[{“value”:” Large Language Models (LLMs) have demonstrated remarkable proficiency in language generation tasks. However, their training process, which involves unsupervised learning from extensive datasets followed by supervised fine-tuning, presents significant challenges. The primary concern stems from the nature of pre-training datasets, such as Common Crawl,… Read More »Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Language model adaptation is a crucial area in artificial intelligence, focusing on enhancing large pre-trained language models to work effectively across various languages. This research is vital for enabling these models to understand and generate text in multiple languages, which is essential for global… Read More »Branch-and-Merge Method: Enhancing Language Adaptation in AI Models by Mitigating Catastrophic Forgetting and Ensuring Retention of Base Language Capabilities while Learning New Languages Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have shown exceptional capabilities in understanding and generating human language, making substantial contributions to applications such as conversational AI. Chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services. The effectiveness of these chatbots relies… Read More »Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT) and Time Between Tokens (TBT) do not capture the complete user experience during real-time interactions. This gap is critical in… Read More »Metron: A Holistic AI Framework for Evaluating User-Facing Performance in LLM Inference Systems Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI)… Read More »Optimizing Large Language Models (LLMs) on CPUs: Techniques for Enhanced Inference and Efficiency Tanya Malhotra Artificial Intelligence Category – MarkTechPost