Transformer-based neural networks have demonstrated proficiency in a variety of tasks, such as text generation, editing, and question-answering. Perplexity and end task accuracy measurements consistently show models with more parameters perform better, leading industries to develop larger models. However, in some cases, larger models do not guarantee superior performance. The 2 billion parameter model, MiniCPM,…
Large Language Models (LLMs) like ChatGPT are becoming increasingly significant due to their capability to execute a broad spectrum of tasks including language processing, knowledge extraction, reasoning, planning, coding, and tool use. This has catalyzed research into more refined AI models, hinting at the potential for Artificial General Intelligence (AGI).
LLMs are built on Transformer…
Large Language Models (LLMs) like ChatGPT have received significant interest due to their ability to perform varied AI tasks from language processing to tool use. These capabilities have pushed research toward creating more sophisticated AI models, opening possibilities for Artificial General Intelligence (AGI).
LLMs are built on the Transformer neural network architecture, using autoregressive learning to…
Researchers from several universities in China and UK have jointly developed a new method for Graph Neural Networks (GNNs), known as Edge-Node Attention-based Differentiable Pooling (ENADPool). This method uses hard clustering and incorporates attention mechanisms to compress node features and edge strengths in GNNs. They also introduced the Multi-distance GNN (MD-GNN) model to mitigate over-smoothing…
State-space models (SSMs) are an essential part of deep learning, used for sequence modeling. They observe a system where the output depends both on current and earlier inputs. This mechanism is utilized extensively in signal processing, control systems, and natural language processing. There lays a challenge with SSMs, it lies in their execution inefficiency, especially…
The paper discussed in this largely explored the effectiveness of machine-learning-based models in wireless link path loss predictions, in lieu of traditional models like Longley-Rice and free space path loss (FSPL). Traditional models suffer in accuracy in non-line-of-sight scenarios due to their inability to account for signal attenuation, or interference caused by electromagnetic interplay with…
Researchers from Columbia University and Databricks Mosaic AI have conducted a comparative study of full finetuning and Low-Rank Adaptation (LoRA), a parameter-efficient finetuning method, in large language models (LLMs). The efficient finetuning of LLMs, which can contain billions of parameters, is an ongoing challenge due to the substantial GPU memory required. This makes the process…
Recent research suggests that incorporating demonstrating examples, or in-context learning (ICL), significantly enhances large language models' (LLM's) and large multimodal models' (LMM's) performance. Studies have shown improvements in LLM performance with increased in-context examples, particularly in out-of-domain tasks. These findings are driven by newer models such as GPT-4o and Gemini 1.5 Pro, which include longer…
Data mapping, which involves linking fields from one database to another, is a crucial part of data management, particularly in transforming and integrating data from varying sources into a cohesive format. An innovative perspective on this process frames it as a search problem. The efficacy of viewing data mapping as a search problem provides useful…
Machine learning (ML) has become a fundamental part of several industries worldwide due to its wide range of applications. However, understanding and interpreting complex ML models continues to be a challenge. These models, often comprising multiple layers and intricate connections, require precise graph visualization tools to understand how data travels across the model and how…
Machine Learning (ML) models are increasingly becoming an integral part of various sectors globally, with their extensive applications and growing reliance on their capabilities. As these models grow in complexity, understanding and interpreting them becomes more challenging. Visualizing how data flows through the model and how the different parts interact is crucial to debug and…
The world of artificial intelligence (AI) and machine learning continues to evolve at a rapid pace, with OpenAI leading the charge. Their latest development is the introduction of GPT-4o, an optimized version of the widely used GPT-4, part of the Generative Pre-trained Transformer model series renowned for its natural language processing capabilities.
GPT-4 boasts enhanced contextual…