52 citations · 83 across the 12 of their papers we have counts for
12 papers
Approximate Contraction of Arbitrary Tensor Networks with a Flexible and Efficient Density Matrix Algorithm
Linjian Ma, Matthew Fishman, Miles Stoudenmire +1
Tensor network contractions are widely used in statistical physics, quantum computing, and computer science. We introduce a method to efficiently approximate tensor network contrac…
Tensor Rank and Other Multipartite Entanglement Measures of Graph States
Louis Schatzki, Linjian Ma, Edgar Solomonik +1
Graph states play an important role in quantum information theory through their connection to measurement-based computing and error correction. Prior work has revealed elegant conn…
Cost-efficient Gaussian Tensor Network Embeddings for Tensor-structured Inputs
Linjian Ma, Edgar Solomonik
This work discusses tensor network embeddings, which are random matrices () with tensor network structure. These embeddings have been used to perform dimensionality reduction of…
LEAP: Learnable Pruning for Transformer-based Models
Zhewei Yao, Xiaoxia Wu, Linjian Ma +4
Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current pruning algorithms e…
Low Rank Approximation in Simulations of Quantum Algorithms
Linjian Ma, Chao Yang
Simulating quantum algorithms on classical computers is challenging when the system size, i.e., the number of qubits used in the quantum algorithm, is moderately large. However, so…
Fast and Accurate Randomized Algorithms for Low-rank Tensor Decompositions
Linjian Ma, Edgar Solomonik
Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The widely used alternating least squares (ALS) method, which solves a sequence of over-determine…
Efficient parallel CP decomposition with pairwise perturbation and multi-sweep dimension tree
Linjian Ma, Edgar Solomonik
CP tensor decomposition with alternating least squares (ALS) is dominated in cost by the matricized-tensor times Khatri-Rao product (MTTKRP) kernel that is necessary to set up the…
AutoHOOT: Automatic High-Order Optimization for Tensors
Linjian Ma, Jiayu Ye, Edgar Solomonik
High-order optimization methods, including Newton's method and its variants as well as alternating minimization methods, dominate the optimization algorithms for tensor decompositi…
Comparison of Accuracy and Scalability of Gauss-Newton and Alternating Least Squares for CP Decomposition
Navjot Singh, Linjian Ma, Hongru Yang +1
Alternating least squares is the most widely used algorithm for CP tensor decomposition. However, alternating least squares may exhibit slow or no convergence, especially when high…
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Sheng Shen, Zhen Dong, Jiayu Ye +5
Transformer based architectures have become de-facto models used for a range of Natural Language Processing tasks. In particular, the BERT based models achieved significant accurac…
Inefficiency of K-FAC for Large Batch Size Training
Linjian Ma, Gabe Montague, Jiayu Ye +4
In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch. However, for bot…
Accelerating Alternating Least Squares for Tensor Decomposition by Pairwise Perturbation
Linjian Ma, Edgar Solomonik
The alternating least squares algorithm for CP and Tucker decomposition is dominated in cost by the tensor contractions necessary to set up the quadratic optimization subproblems.…