Publications (9)
Integrated Photonic Sensing
Nicholas Thomas-Peter, Nathan K Langford, Animesh Datta +8
Loss is a critical roadblock to achieving photonic quantum-enhanced technologies. We explore a modular platform for implementing integrated photonics experiments and consider the e…
BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop
Lucas Charpentier, Leshem Choshen, Ryan Cotterell +11
BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 3rd BabyLM competition. As…
Kirillov's conjecture on Hecke-Grothendieck polynomials
Ben Brubaker, A. Suki Dasher, Michael Hu +6
We use algebraic methods in statistical mechanics to represent a multi-parameter class of polynomials in several variables as partition functions of a new family of solvable lattic…
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…
Imaging gate-induced molecular melting on a graphene field-effect transistor
Franklin Liou, Hsin-Zon Tsai, Zachary A. H. Goodwin +9
Solid-liquid phase transitions are fundamental physical processes, but atomically-resolved microscopy has yet to capture both the solid and liquid dynamics for such a transition. W…
End-to-End Multimodal Representation Learning for Video Dialog
Huda Alamri, Anthony Bilic, Michael Hu +2
Video-based dialog task is a challenging multimodal learning task that has received increasing attention over the past few years with state-of-the-art obtaining new performance rec…
Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Hu, Yinlam Chow +2
While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in ma…
Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
Inko Bovenzi, Adi Carmel, Michael Hu +5
In aims to uncover insights into medical decision-making embedded within observational data from clinical settings, we present a novel application of Inverse Reinforcement Learning…
Self-Supervised Representation Learning for CAD
Benjamin T. Jones, Michael Hu, Vladimir G. Kim +1
The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in…