On the other hand, based on the extensibility of DeepSolo, we launch DeepSolo for multilingual text spotting, making further step to let Transformer decoder with explicit points solo for multilingual text detection, recognition, and script identifi...

Pygmalion AI Expectations Ability to ShapeRealityPygmalion and Galatea, Designed and sold by matintheworldIn the vast tapestry of our modern world, remarkable technological marvel known as Pygmalion AI has woven its way into our lives, captivating h...

have an eventbridge pipe that is pulling messages from self managed kafka topic which contains avro encoded messages. want to use an enrichment aws lambda function to decode those payloads before sending them to an SQS queue. Whenever use the input t...

Vision transformers ViTs are powerful artificial intelligence AI technologies that can identify or categorize objects in imageshowever, there are significant challenges related to both computing power requirements and decision making transparency...

Chklovskii and Halperin theoretically predicted that a QPC between filling fractions $u=1$ and $1/3$ could act as a DC step-up transformer with an amplification factor of 3/2 which was observed recently in experiments. We revisit this problem in the context of AC transport in a bilayer quantum Hall (QH) setting. We show that the AC amplification is bounded by the DC limit of 3/2 in the presence of intra-layer electron-electron interactions alone, however, the possibility of having interlayer i...

The geometric deep learning method PeSTo used to predict protein binding interfaces. The amino acids involved in the protein binding interface are highlighted in red. Credit Lucien Krapp EPFL . By Nik Papageorgiou Proteins are essential to the bi...

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Human Pose Estimation2023guideWhat is Human Pose Estimation Human pose estimation is popular computer vision task with more than 20 years of history. This domain focuses on localizing human body joints e.g., knees and wrists , also known as key poin...

We present in this paper a new architecture, the Pattern Attention Transformer (PAT), that is composed of the new doughnut kernel. Compared with tokens in the NLP field, Transformer in computer vision has the problem of handling the high resolution of pixels in images. In ViT, an image is cut into square-shaped patches. As the follow-up of ViT, Swin Transformer proposes an additional step of shifting to decrease the existence of fixed boundaries, which also incurs 'two connected Swin Transformer...

Motivated by the striking ability of transformers for in-context learning, several works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of transformers are expressive enough to simulate gradient descent iterations. Going beyond the question of expressivity, we ask: Can transformers learn to implement such algorithms by training over random problem instances? To our knowledge, we make the fi...

Automatic Derivation Machine This paper presents an artificial intelligence algorithm that can be used to derive formulas from various scientific 8230 Continue reading 8594...

Posted by Ziniu Hu, Student Researcher, and Alireza Fathi, Research Scientist, Google Research, Perception TeamLarge scale models, such as T5, GPT 3, PaLM, Flamingo and PaLI, have demonstrated the ability to store substantial amounts of knowledge whe...

Vision Transformers (ViTs) have been shown to be effective in various vision tasks. However, resizing them to a mobile-friendly size leads to significant performance degradation. Therefore, developing lightweight vision transformers has become a crucial area of research. This paper introduces CloFormer, a lightweight vision transformer that leverages context-aware local enhancement. CloFormer explores the relationship between globally shared weights often used in vanilla convolutional operators ...

Large transformer models powered by diverse data and model scale have dominated natural language modeling and computer vision and pushed the frontier of multiple AI areas. In reinforcement learning (RL), despite many efforts into transformer-based policies, a key limitation, however, is that current transformer-based policies cannot learn by directly combining information from multiple sub-optimal trials. In this work, we address this issue using recently proposed chain of hindsight to relabel e...

This paper explores the novel deep learning Transformers architectures for high-frequency Bitcoin-USDT log-return forecasting and compares them to the traditional Long Short-Term Memory models. A hybrid Transformer model, called \textbf{HFformer}, is then introduced for time series forecasting which incorporates a Transformer encoder, linear decoder, spiking activations, and quantile loss function, and does not use position encoding. Furthermore, possible high-frequency trading strategies for us...

Transformer networks have emerged as a groundbreaking technology in the field of artificial intelligence, specifically in natural language processing (NLP). Developed by Vaswani et al. in 2017, transformer networks have revolutionized various applications, including machine translation, chatbots, sentiment analysis, and more. This article explores the fundamentals of transformer networks, their architecture, and their transformative impact on the field of AI. Understanding : Traditional NLP models struggled to capture long-range dependencies and contextual relationships in language due to their sequential nature....

Transformers have emerged as the cornerstone of state-of-the-art natural language processing models, showcasing exceptional performance across a wide range of AI applications. However, the memory demands posed by the self-attention mechanism and the large feedforward network in Transformers limit their ability to handle long sequences, thereby creating challenges for tasks involving multiple long sequences or long-term dependencies. We present a distinct approach, Blockwise Parallel Transformer ...

Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and to adapt quickly to new information provided in their context or prompt. We study how transformers balance these two types of knowledge by considering a synthetic setup where toke...

Semantic textual similarity is the task of estimating the similarity between the meaning of two texts. In this paper, we fine-tune transformer architectures for semantic textual similarity on the Semantic Textual Similarity Benchmark by tuning the model partially and then end-to-end. We experiment with BERT, RoBERTa, and DeBERTaV3 cross-encoders by approaching the problem as a binary classification task or a regression task. We combine the outputs of the transformer models and use handmade featu...

This paper proposes Fully Adaptive Self Attention FASA mechanism for vision transformer to model the local and global information as well as the bidirectional interaction between them in context aware ways. Code...