1 citations · 2 across the 4 of their papers we have counts for
4 papers
Simulating Human Memory with Language Models
Qihan Wang, Nicholas Tomlin, Michael Hu +2
Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap, we run a series of classic m…
LOFT: Finding Lottery Tickets through Filter-wise Training
Qihan Wang, Chen Dun, Fangshuo Liao +2
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exist ``\textit{winning tickets}'' in large neural networks. These tickets represent ``sparse'' versions of the…
How much pre-training is enough to discover a good subnetwork?
Cameron R. Wolfe, Fangshuo Liao, Qihan Wang +2
Neural network pruning is useful for discovering efficient, high-performing subnetworks within pre-trained, dense network architectures. More often than not, it involves a three-st…
Mitigating deep double descent by concatenating inputs
John Chen, Qihan Wang, Anastasios Kyrillidis
The double descent curve is one of the most intriguing properties of deep neural networks. It contrasts the classical bias-variance curve with the behavior of modern neural network…