3 citations · 10 across the 7 of their papers we have counts for
7 papers
Agentic Uncertainty Reveals Agentic Overconfidence
Jean Kaddour, Srijan Patel, Gbètondji Dovonon +3
Can AI agents predict whether they will succeed at a task? We study agentic uncertainty by eliciting success probability estimates before, during, and after task execution. All res…
Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
Georgy Tyukin, Gbetondji J-S Dovonon, Jean Kaddour +1
The inference demand for LLMs has skyrocketed in recent months, and serving models with low latencies remains challenging due to the quadratic input length complexity of the attent…
The State of Computer Vision Research in Africa
Abdul-Hakeem Omotayo, Ashery Mbilinyi, Lukman Ismaila +16
Despite significant efforts to democratize artificial intelligence (AI), computer vision which is a sub-field of AI, still lags in Africa. A significant factor to this, is the limi…
Setting the Record Straight on Transformer Oversmoothing
Gbètondji J-S Dovonon, Michael M. Bronstein, Matt J. Kusner
Transformer-based models have recently become wildly successful across a diverse set of domains. At the same time, recent work has shown empirically and theoretically that Transfor…
Long-run Behaviour of Multi-fidelity Bayesian Optimisation
Gbetondji J-S Dovonon, Jakob Zeitler
Multi-fidelity Bayesian Optimisation (MFBO) has been shown to generally converge faster than single-fidelity Bayesian Optimisation (SFBO) (Poloczek et al. (2017)). Inspired by rece…
Towards a Better Understanding of the Computer Vision Research Community in Africa
Abdul-Hakeem Omotayo, Mai Gamal, Eman Ehab +11
Computer vision is a broad field of study that encompasses different tasks (e.g., object detection). Although computer vision is relevant to the African communities in various appl…
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases
Aengus Lynch, Gbètondji J-S Dovonon, Jean Kaddour +1
The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data. For example…