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Transformer-based neural networks have shown great ability to handle multiple tasks like text generation, editing, and question-answering. In many cases, models that use more parameters show better performance measured by perplexity and high accuracies of end tasks. This is the main reason for the development of larger models in industries. However, larger models sometimes result…
Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for training predictive models without compromising sensitive information. As machine learning models increasingly rely on large datasets, ensuring the privacy of individuals whose data contributes to these models becomes crucial. Differentially private…
Autonomous robotics has seen significant advancements over the years, driven by the need for robots to perform complex tasks in dynamic environments. At the heart of these advancements lies the development of robust planning architectures that enable robots to plan, perceive, and execute tasks autonomously. Let’s delve into the various planning architectures for autonomous robotics,…
Incorporating demonstrating examples, known as in-context learning (ICL), significantly enhances large language models (LLMs) and large multimodal models (LMMs) without requiring parameter updates. Recent studies confirm the efficacy of few-shot multimodal ICL, particularly in improving LMM performance on out-of-domain tasks. With longer context windows in advanced models like GPT-4o and Gemini 1.5 Pro, researchers can…
Machine learning models, which can contain billions of parameters, require sophisticated methods to fine-tune their performance efficiently. Researchers aim to enhance the accuracy of these models while minimizing the computational resources needed. This improvement is crucial for practical applications in various domains, such as natural language processing & artificial intelligence, where efficient resource utilization can…
Accurate propagation modeling is paramount for effective radio deployments, coverage analysis, and interference mitigation in wireless communications. Path loss modeling, a widely adopted approach, enables generic predictions of signal power attenuation along wireless links, equipping network planners with essential insights into physical layer attributes. However, in non-line-of-sight (NLOS) scenarios, traditional models like Longley-Rice and free…
State-space models (SSMs) are crucial in deep learning for sequence modeling. They represent systems where the output depends on both current and past inputs. SSMs are widely applied in signal processing, control systems, and natural language processing. The main challenge is the inefficiency of existing SSMs, particularly regarding memory and computational costs. Traditional SSMs need…
Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph structure, facilitating node classification and link prediction tasks. Effective graph pooling is essential for downsizing and learning representations, categorized into global and hierarchical pooling. Hierarchical methods, such as TopK-based and…
The recent Yi-1.5-34B model introduced by 01.AI has brought about yet another advancement in the field of Artificial Intelligence. Positioned as a major improvement over its predecessors, this unique model bridges the gap between Llama 3 8B and 70B. It promises better performance in a number of areas, such as multimodal capability, code production, and…
The artificial intelligence and machine learning world has witnessed rapid advancements, with OpenAI at the forefront of these innovations. One of the latest developments is the release of GPT-4o, an optimized version of the highly acclaimed GPT-4. Let’s delve into the key updates in GPT-4o, compare it with its predecessor, and present a comparative table…