transformer search results




transformer - 20 / 113
www.marktechpost.com | Today
Summary:
The Generative Pre trained Transformer GPT series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. From GPT 1 to GPT 4o and its subsequent iterations, each mode...


Keywords: excel, few-shot, summarization, classification, analysis

arxiv.org | Today
Summary:
The bridge arm of the hybrid modular multilevel converter (MMC) is composed of half-bridge and full-bridge sub-modules cascaded together. Compared with the half-bridge MMC, it can operate in the boost-AC mode, where the modulation index can be higher than 1, and the DC voltage and the AC voltage level are no longer mutually constrained; compared with the full-bridge MMC, it has lower switching device costs and losses. When the hybrid MMC boost-AC mode is used in the power electronic transformer,...


Keywords: design, transformer

arxiv.org | Yesterday
Summary:
The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application domain for transformers is cybersecurity, in particular the malware domain analysis. The reason is the flexibility of the transformer models in handling long sequential features and understanding contextual relationships. However, as the use of transformers for malware analysis is still in the infancy stage, it is cri...


Keywords: generative, malware, analysis, cybersecurity, transformer

analyticsindiamag.com | Yesterday
Summary:
With Mixture of experts MoE , India can blend existing multilingual experts into one multilingual LLM while keeping training and resource costs low.The post MoE will Power the Next Generation of Indic LLMs appeared first on AIM....


Keywords: analytic, transformer, gpt, generative, ai

arxiv.org | Yesterday
Summary:
Foundational models have significantly advanced in natural language processing (NLP) and computer vision (CV), with the Transformer architecture becoming a standard backbone. However, the Transformer's quadratic complexity poses challenges for handling longer sequences and higher resolution images. To address this challenge, State Space Models (SSMs) like Mamba have emerged as efficient alternatives, initially matching Transformer performance in NLP tasks and later surpassing Vision Transformers...


Keywords: transformer, natural language processing, nlp,

stackoverflow.com | Yesterday
Summary:
I m currently working on converting Word2Vec embedding to PubMedBERT embedding. am working in Google Colab, and I m running into the issue where ImportError cannot import name UMAP from umap unknown location keeps popping up even though I ve ...


Keywords: visual, transformer

thecleverprogrammer.com | Yesterday
Summary:
code generation model is type of artificial intelligence that can automatically generate source code based on given input, which can be natural language instructions, existing code snippets, or structured data. So, if you want to learn how to build c...


Keywords: openai, pre-trained, tpu, gpt, hugging

www.marktechpost.com | Today
Summary:
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Keywords: bert , gpt, ml

arxiv.org | Yesterday
Summary:
SGD performs worse than Adam by a significant margin on Transformers, but the reason remains unclear. In this work, we provide an explanation of SGD's bad performance on Transformers through the lens of Hessian: (i) Transformers are "heterogeneous": the Hessian spectrum across parameter blocks vary dramatically, a phenomenon we call "block heterogeneity"; (ii) Heterogeneity hampers SGD: SGD performs badly on problems with block heterogeneity. To validate that heterogeneity hampers SGD, we check ...


Keywords: transformer

python.plainenglish.io | Today
Summary:
From text messaging to virtual assistants, natural language processing NLP has transformed the way humans communicate with machines. But the latest advancement, called Llama3, promises to take things even further. With vastly larger training datase...


Keywords: excel, python, analysis, rust, nlp

arxiv.org | Yesterday
Summary:
We study whether transformers can learn to implicitly reason over parametric knowledge, a skill that even the most capable language models struggle with. Focusing on two representative reasoning types, composition and comparison, we consistently find that transformers can learn implicit reasoning, but only through grokking, i.e., extended training far beyond overfitting. The levels of generalization also vary across reasoning types: when faced with out-of-distribution examples, transformers fail...


Keywords: transformer, metric

arxiv.org | Yesterday
Summary:
Large transformer models pretrained on offline reinforcement learning datasets have demonstrated remarkable in-context reinforcement learning (ICRL) capabilities, where they can make good decisions when prompted with interaction trajectories from unseen environments. However, when and how transformers can be trained to perform ICRL have not been theoretically well-understood. In particular, it is unclear which reinforcement-learning algorithms transformers can perform in context, and how distrib...


Keywords: transformer, algorithms, reinforcement learning, pretrained

pyimagesearch.com | Yesterday
Summary:
Table of Contents Understanding Tasks in Diffusers Part Introduction Why Not Image to Image ControlNet Models Configuring Your Development Environment Setup and Imports Installation Imports Utility Functions Canny ControlNet Setting Up Loading the ...


Keywords: pre-trained, cpu, design, hugging face

arxiv.org | Yesterday
Summary:
Point cloud analysis has seen substantial advancements due to deep learning, although previous Transformer-based methods excel at modeling long-range dependencies on this task, their computational demands are substantial. Conversely, the Mamba offers greater efficiency but shows limited potential compared with Transformer-based methods. In this study, we introduce PoinTramba, a pioneering hybrid framework that synergies the analytical power of Transformer with the remarkable computational effici...


Keywords: excel, framework, deep learning, analysis,

arxiv.org | Yesterday
Summary:
Autoregressively trained transformers have brought a profound revolution to the world, especially with their in-context learning (ICL) ability to address downstream tasks. Recently, several studies suggest that transformers learn a mesa-optimizer during autoregressive (AR) pretraining to implement ICL. Namely, the forward pass of the trained transformer is equivalent to optimizing an inner objective function in-context. However, whether the practical non-convex training dynamics will converge to...


Keywords: transformer

arxiv.org | Yesterday
Summary:
Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically shifted the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift focus from the overall architecture of the Transformer to the effectiveness of s...


Keywords: transformer, ios

arxiv.org | Today
Summary:
Sequential decision-making algorithms such as reinforcement learning (RL) in real-world scenarios inevitably face environments with partial observability. This paper scrutinizes the effectiveness of a popular architecture, namely Transformers, in Partially Observable Markov Decision Processes (POMDPs) and reveals its theoretical limitations. We establish that regular languages, which Transformers struggle to model, are reducible to POMDPs. This poses a significant challenge for Transformers in l...


Keywords: reinforcement learning, rl , ios,

arxiv.org | Today
Summary:
Recently, recurrent models based on linear state space models (SSMs) have shown promising performance in language modeling (LM), competititve with transformers. However, there is little understanding of the in-principle abilities of such models, which could provide useful guidance to the search for better LM architectures. We present a comprehensive theoretical study of the capacity of such SSMs as it compares to that of transformers and traditional RNNs. We find that SSMs and transformers have ...


Keywords: transformer

paperswithcode.com | Today
Summary:
The poor performance of transformers on arithmetic tasks seems to stem in large part from their inability to keep track of the exact position of each digit inside of large span of digits. Code...


Keywords: transformer

arxiv.org | Yesterday
Summary:
One of the inherent challenges in deploying transformers on time series is that \emph{reality only happens once}; namely, one typically only has access to a single trajectory of the data-generating process comprised of non-i.i.d. observations. We derive non-asymptotic statistical guarantees in this setting through bounds on the \textit{generalization} of a transformer network at a future-time $t$, given that it has been trained using $N\le t$ observations from a single perturbed trajectory of a ...


Keywords: transformer, time series, network, statistic


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