3 citations · 6 across the 8 of their papers we have counts for
7 papers
AdaFlood: Adaptive Flood Regularization
Wonho Bae, Yi Ren, Mohamad Osama Ahmed +3
Although neural networks are conventionally optimized towards zero training loss, it has been recently learned that targeting a non-zero training loss threshold, referred to as a f…
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Yi Ren, Samuel Lavoie, Mikhail Galkin +2
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of resear…
MUG: A General Meeting Understanding and Generation Benchmark
Qinglin Zhang, Chong Deng, Jiaqing Liu +7
Listening to long video/audio recordings from video conferencing and online courses for acquiring information is extremely inefficient. Even after ASR systems transcribe recordings…
Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)
Qinglin Zhang, Chong Deng, Jiaqing Liu +7
ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SL…
Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up
Shenzheng Zhang, Qi Tan, Xinzhi Zheng +2
The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained…
How to prepare your task head for finetuning
Yi Ren, Shangmin Guo, Wonho Bae +1
In deep learning, transferring information from a pretrained network to a downstream task by finetuning has many benefits. The choice of task head plays an important role in fine-t…