PPT-Training DNNs with up to 5x less memory via optimal tensor

rematerialization Paras Jain Joint work with Ajay Jain Ani Nrusimha Amir Gholami Pieter Abbeel Kurt Keutzer Ion Stoica Joseph Gonzalez To appear at MLSys 2020 Deep learning continues to adapt to increasing complex applications. Clean regularly the chain with an appropriate chaincleaner Never use alkali based or acid based solvents such as rust cleaners If those solvent be used chain might break and cause serious injury In order to obtain good gear shifting performance the 27-750. Texture, Microstructure & Anisotropy. A.D. Rollett. Last revised:. 7. th. Feb. . ‘. 14. 2. Bibliography. R.E. Newnham,. Properties of Materials: Anisotropy, Symmetry, Structure. , Oxford University Press, 2004, 620.112 N55P.. Dieter Jaksch. Outline. Lecture 1: Introduction. What defines a quantum simulator? Quantum simulator criteria. Strongly correlated quantum systems.. Lecture 2: Optical lattices. Bose-Einstein condensation, adiabatic loading of an optical lattice. Hamiltonian . Prof Geraint F. Lewis. Sydney Institute for Astronomy. The University of Sydney. Tensors. The Goals:. Understand what a Tensor is. Understand what a Tensor does. Tensor Algebra. Tensor Contractions. The Metric Tensor. (& Gravitons ?). -. Vishal. . Kasliwal. Classical Electromagnetism. Vacuum. Maxwell Field Equations. Light!!. Electromagnetic waves. Quantum Electromagnetism. Hamiltonian of Quantized. Field. where. Tensor Network States: Algorithms and Applications, Beijing, December 2014. Tensor Network Renormalization. Guifre. Vidal. (. Evenbly. , Vidal, arXiv:1412.0732). Tensor Renormalization Methods. What is the usefulness of renormalization (or coarse-graining) in many-body physics??? . geometry. From superconducting. qubits. to . spin chains. Michael . Kolodrubetz. , Physics . Department, Boston University. Theory collaborators: . Anatoli. . Polkovnikov. (BU), Vladimir . Gritsev. with the UNC-Utah NAMIC Tools. Martin Styner UNC. Thanks to Guido . Gerig. , . UUtah. NAMIC: National Alliance for Medical Image Computing. And many, many folks. Overview of the UNC – Utah NAMIC pipeline. Tensor Decomposition and Clustering. Moses . Charikar. Stanford University. 1. Rich theory of analysis of algorithms and complexity founded on worst case analysis. Too pessimistic. Gap between theory and practice. Shenghan Jiang. Boston College. Benasque. February. , 09, 2017. Symmetric tensor-networks and topological phases. Collaborators:. Ying Ran (Boston College) . Panjin. Kim, . Hyungyong. Lee, Jung . Hoon. He Zhang. 1. , He Huang. 2. , . Rui. Li. 1. , . Jie. Chen. 1. , Li-Shi Luo. 2. Jefferson Lab. Old Dominion University. FEIS-2, 05/15/2015. Outline. He Zhang. ---. 3. ---. Introduction of FMM. He Zhang. Trevor Linton, University of Utah. Acknowledgements. Thomas Henderson. Ross Whitaker. Tolga Tasdizen. The support of IAVO Research, Inc. through contract FA9550-08-C-005.. Field of Study. Geographical Information Systems. near the ground states of nuclei. = Study by . 16. O(p,pd). 14. N reaction =. Isao Tanihata, H. J. Ong, s. Terashima and E443 collaboration at RCNP. IRCNPC and SPNEE, Beihang University, Beijing, China. Juan Andrés . Bazerque. , Gonzalo . Mateos. , and . Georgios. B. . Giannakis. . August. 8, 2012. . Spincom. group, University of Minnesota. . Acknowledgment: . AFOSR MURI grant no. FA 9550-10-1-0567.