ImageNet Classification with Deep Convolutional Neural Networks
A deep convolutional network demonstrated how data, GPU computation and learned representations could transform visual recognition.
THE RESEARCH ATLAS
Qwen3-ASR Technical Report
A speech-recognition family and a forced-alignment model address multilingual transcription and timestamp prediction.
Institution and location sources
Location refers to credited institutions or the listed organization, not author citizenship or an exhaustive collaboration list.
Why read this
Useful for understanding audio interfaces and the accuracy–latency trade-off.
Citation
@misc{shi2026qwen3,
title = {Qwen3-ASR Technical Report},
author = {Xian Shi and Xiong Wang and Zhifang Guo and Yongqi Wang and Pei Zhang and Xinyu Zhang and Zishan Guo and Hongkun Hao and Yu Xi and Baosong Yang and Jin Xu and Jingren Zhou and Junyang Lin},
year = {2026},
eprint = {2601.21337},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2601.21337}
}Rights remain with the original authors. Refer to the original source for citations and licensing.
Related research
Denoising Diffusion Probabilistic Models
A generative model learns to reverse a gradual noise process, establishing an influential approach to image synthesis.
Learning Transferable Visual Models From Natural Language Supervision
Contrastive training aligns images with natural-language descriptions, enabling transfer to new visual categories.