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- Imperial College LondonGB386 papers
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32 papers · 1 filter
RiemannONets: Interpretable Neural Operators for Riemann Problems
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap +1
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numeri…
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Lakshmi Narasimhan Govindarajan, Rex G Liu, Drew Linsley +4
Humans learn by interacting with their environments and perceiving the outcomes of their actions. A landmark in artificial intelligence has been the development of deep reinforceme…
NeuroSurgeon: A Toolkit for Subnetwork Analysis
Michael A. Lepori, Ellie Pavlick, Thomas Serre
Despite recent advances in the field of explainability, much remains unknown about the algorithms that neural networks learn to represent. Recent work has attempted to understand t…
Learning Functional Transduction
Mathieu Chalvidal, Thomas Serre, Rufin VanRullen
Research in machine learning has polarized into two general approaches for regression tasks: Transductive methods construct estimates directly from available data but are usually p…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist tod…