Cybersecurity has always been the focus of Internet research. Malware refers to software intentionally designed to harm computer systems, networks, ...

Modality-agnostic decoders leverage modality-invariant representations in human subjects' brain activity to predict stimuli irrespective of their modality (image, text, mental imagery).

Microsoft launches three in-house AI models for transcription, voice, and image generation, challenging OpenAI and Google ...

Similar to BERT and GPT2, massive pre-trained encoder-decoder models have shown to significantly boost performance on a variety of sequence-to-sequence tasks Lewis et al. (2019), Raffel et al. (2019).

I noticed an inaccuracy in the model description between the README and the Technical Report. README: mentions "...unified encoder-decoder architecture..." Technical Report: states "...adopts a ...

Abstract: Long-term time-series forecasting remains a critical challenge, with deep learning models often facing persistent training instability. This study reveals a critical insight: the ...

Abstract: Code search is essential for code reuse, allowing developers to efficiently locate relevant code snippets. The advent of powerful decoder-only Large Language Models (LLMs) has revolutionized ...

Why was a new multilingual encoder needed? XLM-RoBERTa (XLM-R) has dominated multilingual NLP for more than 5 years, an unusually long reign in AI research. While encoder-only models like BERT and ...

What if the success of your next project hinged on choosing the right speech-to-text model? In a world where real-time transcription and multilingual accuracy are becoming essential, the competition ...