Zobrazeno 1 - 10
of 60 242
pro vyhledávání: '"A, Kirsch"'
Autor:
Shimron, Efrat, Shan, Shanshan, Grover, James, Koonjoo, Neha, Shen, Sheng, Boele, Thomas, Sorby-Adams, Annabel J., Kirsch, John E., Rosen, Matthew S., Waddington, David E. J.
Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, critical barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan
Externí odkaz:
http://arxiv.org/abs/2411.06704
Transformers have demonstrated remarkable in-context learning capabilities across various domains, including statistical learning tasks. While previous work has shown that transformers can implement common learning algorithms, the adversarial robustn
Externí odkaz:
http://arxiv.org/abs/2411.05189
Autor:
Liang, Haotong, Wang, Chuangye, Yu, Heshan, Kirsch, Dylan, Pant, Rohit, McDannald, Austin, Kusne, A. Gilad, Zhao, Ji-Cheng, Takeuchi, Ichiro
Iterative cycles of theoretical prediction and experimental validation are the cornerstone of the modern scientific method. However, the proverbial "closing of the loop" in experiment-theory cycles in practice are usually ad hoc, often inherently dif
Externí odkaz:
http://arxiv.org/abs/2410.17430
Autor:
Okanovic, Patrik, Kirsch, Andreas, Kasper, Jannes, Hoefler, Torsten, Krause, Andreas, Gürel, Nezihe Merve
We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small subset of highly informative examples for labeling, in order to efficiently identi
Externí odkaz:
http://arxiv.org/abs/2410.13609
A De Bruijn cycle is a cyclic sequence in which every word of length $n$ over an alphabet $\mathcal{A}$ appears exactly once. De Bruijn tori are a two-dimensional analogue. Motivated by recent progress on universal partial cycles and words, which sho
Externí odkaz:
http://arxiv.org/abs/2409.12417
Autor:
Kirsch, Andreas
Epistemic uncertainty is crucial for safety-critical applications and out-of-distribution detection tasks. Yet, we uncover a paradoxical phenomenon in deep learning models: an epistemic uncertainty collapse as model complexity increases, challenging
Externí odkaz:
http://arxiv.org/abs/2409.02628
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. However, popular sampling methods like top-p (nucleus sampling) often struggle to balance quality and div
Externí odkaz:
http://arxiv.org/abs/2407.01082
Autor:
Park, Jihun, Horn, Jarryd A., Kirsch, Dylan J., Pant, Rohit K., Yoon, Hyeok, Baek, Sungha, Sarker, Suchismita, Mehta, Apurva, Zhang, Xiaohang, Lee, Seunghun, Greene, Richard, Paglione, Johnpierre, Takeuchi, Ichiro
The Bi${-}$Ni binary system has been of interest due to possible unconventional superconductivity aroused therein, such as time-reversal symmetry breaking in Bi/Ni bilayers or the coexistence of superconductivity and ferromagnetism in Bi$_3$Ni crysta
Externí odkaz:
http://arxiv.org/abs/2406.18704
Recently, transductive learning methods, which leverage holdout sets during training, have gained popularity for their potential to improve speed, accuracy, and fairness in machine learning models. Despite this, the composition of the holdout set its
Externí odkaz:
http://arxiv.org/abs/2406.12011
Autor:
Brandfonbrener, David, Zhang, Hanlin, Kirsch, Andreas, Schwarz, Jonathan Richard, Kakade, Sham
Selecting high-quality data for pre-training is crucial in shaping the downstream task performance of language models. A major challenge lies in identifying this optimal subset, a problem generally considered intractable, thus necessitating scalable
Externí odkaz:
http://arxiv.org/abs/2406.10670