The foundational importance of arrays in computer science cannot be overstated. Arrays and lists are the bedrock of data structures, often the first concepts introduced to budding programmers. Since their inception back to Fortran in 1957 and continuing to hold prominence in contemporary languages like Python, arrays maintain a consistent and universal presence across the…
The increasing reliance on machine learning models in critical applications raises concerns about their susceptibility to manipulation and exploitation. Once trained on a dataset, these models often retain information indefinitely, making them vulnerable to privacy breaches, adversarial attacks, or unintended biases. Therefore, techniques are urgently needed to allow models to unlearn specific data subsets, reducing…
PyTorch introduced TK-GEMM, an optimized Triton FP8 GEMM kernel, to address the challenge of accelerating FP8 inference for large language models (LLMs) like Llama3 using Triton Kernels. Standard PyTorch execution often struggles with the overhead of launching multiple kernels on the GPU for each operation in LLMs, leading to inefficient inference. The researchers aim to…
Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, human instructions are sometimes too brief for current methods to capture and follow. Multimodal large language models (MLLMs) show promising capabilities in cross-modal understanding and visual-aware response generation via LMs. We investigate how MLLMs…
Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals. Here, we circumvent the challenges by converting regression…
Rendering scenes observed in a monocular video from novel viewpoints is a chal- lenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized tech- niques, which only run a deep net forward pass on a test scene. In contrast, for dy- namic scenes, scene-specific…
Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices. Despite recent trends favoring alternative activation functions such as GELU or SiLU, known for increased computation, this study strongly advocates for reinstating ReLU activation in LLMs. We…
There has been rapid growth in the open-source landscape for Large Language Models (LLMs) after the release of the Llama3 model and its successor, Llama 2, by Meta in 2023. This release has led to the development of multiple innovative LLMs. These models have played an important role in this dynamic field by influencing natural…
Language models are incredibly powerful tools that can understand and generate human-like text by learning patterns from massive datasets. However, the traditional method of training these models, called “next-token prediction,” has its limitations. It essentially teaches the model to predict the next word in a sequence, but this approach can lead to suboptimal performance, especially…
Natural Language Processing (NLP) is integral to artificial intelligence, enabling seamless communication between humans and computers. This interdisciplinary field incorporates linguistics, computer science, and mathematics, facilitating automatic translation, text categorization, and sentiment analysis. Traditional NLP methods like CNN, RNN, and LSTM have evolved with transformer architecture and large language models (LLMs) like GPT and BERT…