6 citations · 11 across the 9 of their papers we have counts for
9 papers
Understanding Task Vectors in In-Context Learning: Emergence, Functionality, and Limitations
Yuxin Dong, Jiachen Jiang, Zhihui Zhu +1
Task vectors offer a compelling mechanism for accelerating inference in in-context learning (ICL) by distilling task-specific information into a single, reusable representation. De…
Optimal Allocation of Pauli Measurements for Low-rank Quantum State Tomography
Zhen Qin, Casey Jameson, Zhexuan Gong +2
The process of reconstructing quantum states from experimental measurements, accomplished through quantum state tomography (QST), plays a crucial role in verifying and benchmarking…
Optimal quantum state tomography with local informationally complete measurements
Casey Jameson, Zhen Qin, Alireza Goldar +3
Quantum state tomography (QST) remains the gold standard for benchmarking and verification of near-term quantum devices. While QST for a generic quantum many-body state requires an…
AdaContour: Adaptive Contour Descriptor with Hierarchical Representation
Tianyu Ding, Jinxin Zhou, Tianyi Chen +3
Existing angle-based contour descriptors suffer from lossy representation for non-starconvex shapes. By and large, this is the result of the shape being registered with a single gl…
Generalized Neural Collapse for a Large Number of Classes
Jiachen Jiang, Jinxin Zhou, Peng Wang +4
Neural collapse provides an elegant mathematical characterization of learned last layer representations (a.k.a. features) and classifier weights in deep classification models. Such…
The Law of Parsimony in Gradient Descent for Learning Deep Linear Networks
Can Yaras, Peng Wang, Wei Hu +3
Over the past few years, an extensively studied phenomenon in training deep networks is the implicit bias of gradient descent towards parsimonious solutions. In this work, we inves…