Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings. In IEEE 54th Annual Sympo- sium on Foundations of Computer Science (FOCS), pages 117-126. IEEE, 2013.John Nelson and Huy L Nguy
Pratap, R., Sen, S. (2018). Faster Coreset Construction for Projective ClusteringviaLow-Rank Approximation. In: Iliopoulos, C., Leong, H., Sung, WK. (eds) Combinatorial Algorithms. IWOCA 2018. Lecture Notes in Computer Science(), vol 10979. Springer, Cham. https://doi.org/10.1007/978...
. 2.1 first-order algorithms to optimize the problem in eq. ( 1 ), the first-order riemannian optimization algorithm rsgd updates the solution at each k -th iteration by using an \(f_i\) instance, as $$\begin{aligned} {\textbf{x}}_{k+1}=r_{{\textbf{x}}_k}\left( -\beta _...
Detection algorithms return the coordinates of one or more targets, along with the probability distribution associated with each target. In the context of deep-learning-based genomic scans, classification refers to determining whether a (narrow) subgenomic region belongs to a certain class, e.g., ...
Machine Learning for Quantum Materials and Algorithms 51分钟 1501播放 Spin Transport, Reading and Writing in Antiferromagnets 46分钟 535播放 Non-Hermitian skin effect and non-Bloch band theory 46分钟 2489播放 Quantum excitations of "hidden orders" and Thermal transport of "hidden particles" 1时0分...
In this paper, we exploit the virtue of both the quicksort and quickhull algorithms for the construction of the convex hull of a finite set of disks in the plane, thus named the QuickhullDisk algorithm. QuickhullDisk takes an O(nlog n) time on average and O(mn) time in the worst ca...
To improve the stability of matrix factorization, algorithms minimize the accumulation and amplification of noise in the computation to provide reliable results17–22. With the development of neural dynamics in various fields23–30, researchers turn their attention to matrix factorization. Most of the ...
For this algorithm we combine two new algorithms: One that is fast when max(S) is small, and one that is fast when min(S) is large. In particular, when max(S) is small, we employ tools from number theory [21] to handle most instances, while for the remaining ones we apply the ...
and get faster every year—"faster every second" because the runtime can (in theory) retune the JIT compiled code as your program runs; and "faster ever year" because with each new release of the runtime, better, smarter, faster algorithms can take a fresh stab at optimizing your code....
and get faster every year—"faster every second" because the runtime can (in theory) retune the JIT compiled code as your program runs; and "faster ever year" because with each new release of the runtime, better, smarter, faster algorithms can take a fresh stab at optimizing your code....