activity
20162021
most citedCan I Trust the Explainer? Verifying Post-hoc Explanatory Methods

36 citations · 72 across the 4 of their papers we have counts for

collaborators

20 papers

cs.LG2021

Don't Sweep your Learning Rate under the Rug: A Closer Look at Cross-modal Transfer of Pretrained Transformers

Danielle Rothermel, Margaret Li, Tim Rocktäschel +1

Self-supervised pre-training of large-scale transformer models on text corpora followed by finetuning has achieved state-of-the-art on a number of natural language processing tasks…

cs.MA2020

Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Kalesha Bullard, Franziska Meier, Douwe Kiela +2

Effective communication is an important skill for enabling information exchange and cooperation in multi-agent settings. Indeed, emergent communication is now a vibrant field of re…

cs.LG2020

Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial Optimization

Dmitrii Beloborodov, A. E. Ulanov, Jakob N. Foerster +2

Quantum hardware and quantum-inspired algorithms are becoming increasingly popular for combinatorial optimization. However, these algorithms may require careful hyperparameter tuni…

cs.AI2019

Improving Policies via Search in Cooperative Partially Observable Games

Adam Lerer, Hengyuan Hu, Jakob Foerster +1

Recent superhuman results in games have largely been achieved in a variety of zero-sum settings, such as Go and Poker, in which agents need to compete against others. However, just…

cs.CL2019

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

Cinjon Resnick, Abhinav Gupta, Jakob Foerster +2

Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compos…

cs.CL201936 cited

Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods

Oana-Maria Camburu, Eleonora Giunchiglia, Jakob Foerster +2

For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we ide…