A Yann LeCun team proposes dictionary learning to provide detailed visualizations of transformer representations and insights into semantic structures such as word-level disambiguation, sentence-level pattern formation, and long-range dependency captured by transformers.
A research team from Facebook AI proposes a Contrastive Semi-supervised Learning (CSL) approach that synthesizes pseudo-labelling and contrastive losses to improve the stability of learned speech representations for automatic speech recognition (ASR).
UC Berkeley, Facebook AI Research and New York University researchers’ Multiple Sequence Alignments (MSA) Transformer surpasses current state-of-the-art unsupervised structure learning methods by a wide margin.
Recent AI research on speech separation has explored ways to associate lip motions in videos with audio, but this approach suffers when speakers’ lips are occluded, which they often are in busy multi-speaker environments.
Facebook this week released Detection Transformers (DETR), a new approach for object detection and panoptic segmentation tasks that uses a completely different architecture than previous object detection systems.
As global AI development and deployment continues, the demand for AI talents is growing faster than ever. A number of industry leaders and reputable institutions offer AI residency programs designed to help nurture promising AI talents.
Recently, Facebook AI Research (FAIR) researchers introduced a structured memory layer which can be easily integrated into a neural network to greatly expand network capacity and the number of parameters without significantly changing calculation cost.
Researchers from New York University and Facebook AI Research recently added 50,000 test samples to the dataset. Facebook Chief AI Scientist Yann LeCun, who co-developed the MNIST, tweeted his approval: “MNIST reborn, restored and expanded.”
ImageNet Pre-training is common in a variety of CV (Computer Vision) tasks, as something of a consensus has emerged that pre-training can help a model learn transferrable information that can be useful for target tasks.