Abstract: In recent years, advancements in neural network designs and the availability of large-scale labeled datasets have led to significant improvements in the accuracy of piano transcription ...

Stop letting AI pick your passwords. They follow predictable patterns instead of being truly random, making them easy for ...

Cursor has introduced a new inference technique, “warp decode,” that restructures how Mixture-of-Experts (MoE) models execute ...

Climate change presents an escalating global challenge, demanding concerted efforts to mitigate its widespread effects. For Africa, a continent striving for economic advancement, understanding the ...

For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We ...

Abstract: Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural ...

This paper presents SimpleAR, a vanilla autoregressive visual generation model that achieves state-of-the-art text-to-image generation performance. First the first time, we demonstrate that: 🏆 with ...

Prevalence of obesity/overweight and its relationship with incidence of pancreatic cancer in the US states using BRFSS and CDC WONDER database of 2021.

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...

The advent of GPT models, along with other autoregressive or AR large language models har unfurled a new epoch in the field of machine learning, and artificial intelligence. GPT and autoregressive ...