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Found 14 papers, 2 papers with code
Modeling Randomly Walking Volatility with Chained Gamma Distributions
2) In the case of only using VI, this model can obtain accuracy that are slightly worse than MC, but still acceptable in practice; 3) Only using VI, the running time of Gam-Chain, under the most conservative settings, can be reduced to below 20% of that based on the lognormal chain via MC.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
Juvenile state hypothesis: What we can learn from lottery ticket hypothesis researches?
The original lottery ticket hypothesis performs pruning and weight resetting after training convergence, exposing it to the problem of forgotten learning knowledge and potential high cost of training.
M6-T: Exploring Sparse Expert Models and Beyond
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.
Graph Intention Network for Click-through Rate Prediction in Sponsored Search
Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namely weak generalization problem.
SCMA Codebook Design Based on Uniquely Decomposable Constellation Groups
Thus, BER performance of the proposed codebook design approach outperforms that of the existing codebook design schemes in both uncoded and coded SCMA systems, especially for large-size codebooks.
Tunable ferroelectricity in hBN intercalated twisted double-layer graphene
Van der Waals (vdW) assembly of two-dimensional materials has been long recognized as a powerful tool to create unique systems with properties that cannot be found in natural compounds.
Mesoscale and Nanoscale Physics Materials Science
Singlino-dominated dark matter in $Z_3$-NMSSM
Approximate analytical formulas describing the dark matter abundance and cross section in the scattering with nucleons are used to illustrate a dependence on theoretical parameters in neutralino and Higgs sectors.
High Energy Physics - Phenomenology
Radiative Decays of Charged Leptons in the Seesaw Effective Field Theory with One-loop Matching
For the first time, the Wilson coefficients of all the relevant six-dimensional operators are computed by carrying out the one-loop matching between the effective theory and full seesaw model, and applied to calculate the total rates of radiative decays of charged leptons.
High Energy Physics - Phenomenology High Energy Physics - Experiment
RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning
Today high-performance computing (HPC) platforms are still dominated by batch jobs.
A bi-diffusion based layer-wise sampling method for deep learning in large graphs
The Graph Convolutional Network (GCN) and its variants are powerful models for graph representation learning and have recently achieved great success on many graph-based applications.
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections
However, the recent development of randomized principal component analysis (RPCA) has opened up the possibility of obtaining approximate principal components on very large datasets.
Gear Training: A new way to implement high-performance model-parallel training
The training of Deep Neural Networks usually needs tremendous computing resources.
