1.7k citations · 2.2k across the 12 of their papers we have counts for
13 papers
On the "Induction Bias" in Sequence Models
M. Reza Ebrahimi, Michaël Defferrard, Sunny Panchal +1
Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform state tracking. In particular, a growi…
NeuroSteiner: A Graph Transformer for Wirelength Estimation
Sahil Manchanda, Dana Kianfar, Markus Peschl +2
A core objective of physical design is to minimize wirelength (WL) when placing chip components on a canvas. Computing the minimal WL of a placement requires finding rectilinear St…
DE-HNN: An effective neural model for Circuit Netlist representation
Zhishang Luo, Truong Son Hy, Puoya Tabaghi +7
The run-time for optimization tools used in chip design has grown with the complexity of designs to the point where it can take several days to go through one design cycle which ha…
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay
Natasha Butt, Blazej Manczak, Auke Wiggers +4
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchma…
Learning to recover orientations from projections in single-particle cryo-EM
Jelena Banjac, Laurène Donati, Michaël Defferrard
A major challenge in single-particle cryo-electron microscopy (cryo-EM) is that the orientations adopted by the 3D particles prior to imaging are unknown; yet, this knowledge is es…
DeepSphere: a graph-based spherical CNN
Michaël Defferrard, Martino Milani, Frédérick Gusset +1
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance. DeepSphere, a method based on a graph representati…